# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("decontam")
# install.packages("compositions")
# install.packages("ggupset")
# install.packages("gt")
# install.packages("ggrepel")
# install.packages("ploty")
# install.packages("viridis")
# install.packages("vegan")
# install.packages("anomalize")
# install.packages("cluster")
# install.packages("factoextra")
# install.packages("gt")
# if(!requireNamespace("BiocManager")){
# install.packages("BiocManager")
# }
# BiocManager::install("phyloseq")
# install.packages("indicspecies")2023 Gulf data analysis
1 Context & introduction
Core scientific questions:
How does the single-celled eukaryotic (microeukaryote or protistan) community composition and diversity change from the Mississippi River plume to offshore?
What environmental factors play a role in shaping the microeukaryotic community dynamics at shallow versus deep depths in the Gulf of Mexico?
How do putative functional roles among microeukaryotes vary across critical environmental gradients?
Are there specific taxonomic groups, classes, or species that may be considered indicative of the Northern Gulf ecosystem? Especially along specific temperature, salinity, oxygen, and nutrient trends?
2 Set up R environment
Set up R & import starting data. Installation prompts.
library(tidyverse);library(phyloseq); library(decontam)
library(compositions); library(patchwork);
library(ggupset); library(gt); library(ggrepel)
library(viridis); library(vegan);
library(treemapify);library(gt)
#
library(anomalize)
library(cluster)
library(factoextra)
library(phyloseq)
library(indicspecies)2.1 Import required data
Below is direct from GoM-2023-amplicons.qmd
load("input-data/gulf-2023-sequence-analysis_02062026.RData", verbose = TRUE)Loading objects:
asv_long_avg_wtax_clean
asv_long_wtax_clean
tax_key
metadata_allsamples
metadata_seqsamples
metadata_stn_info2.2 Set ordering & factoring
offshore_on_shore_order <- c("S1", "S2", "S3", "S4", "S5",
"S9", "S8", "S7",
"S11", "S12", "S14", "S15")
stn_order_shore_offshore <- c(1, 2, 3, 4, 5,
9, 8, 7, 6,
10, 11, 12, 13, 14, 15, 16, 17)
transect_labels <- c("transect1", "transect1", "transect1", "transect1", "transect1", "transect2", "transect2", "transect2", "transect3", "transect3", "transect3", "transect3")
depth_order <- as.character(sort((unique(metadata_seqsamples$Depth))))
depth_order_round <- as.character(sort(round(unique(metadata_seqsamples$Depth), digits = 0)))
depth_bin <- c("< 5m","5-25m","25-60m",
"60-270m","270-400m","400-800m",
"800-1200m","1200-2000m","2000-3000m")
blues <- c("#dde4f0","#b7cae8","#96b3e3",
"#6b94d6","#3473d9", "#0b4fbd",
"#0b429c", "#07214a", "#081730")
names(blues) <- depth_bin
watercol_features <- c("other", "DCM", "Salinity maximum", "Oxygen minimum", "Salinity maximum, DCM")
watercol_features_label <- c("Depth with no feature", "DCM", "Salinity maximum", "Oxygen minimum", "Salinity maximum, DCM")
watercol_shape <- c(21, 24, 25, 22, 23)
names(watercol_shape) <- watercol_features_label
# watercol_shape
water_mass_order <- c("AAIW", "CSWr", "GCW", "NADW", "NASUW", "TACW", "TACWn", "Uncharacterized Upper", "Uncharacterized Deep")
water_mass_color <- c("darkblue", "orange", "forestgreen", "lightgreen", "lightblue", "darkred", "pink", "grey80", "grey20")
names(water_mass_color) <- water_mass_order2.2.1 Study stats
(unique(asv_long_wtax_clean$Station)) # 12 stations [1] 15 1 11 12 14 2 3 4 5 7 8 9length(unique(asv_long_wtax_clean$Station)) # 12 stations[1] 12length(unique(asv_long_wtax_clean$STN_NISKIN)) # 90 unique samples[1] 903 K-means clustering by sequence data
Normalize raw sequence data around samples. The goal is to compare this to water masses and group sample types.
# install.packages("anomalize")
# install.packages("cluster")
# install.packages("factoextra")
# library(anomalize)
# library(cluster)
# library(factoextra)
set.seed(3500)Normalize across variables. Will not include categorical values. We are grouping STN_NISKIN.
head(asv_long_avg_wtax_clean)# A tibble: 6 × 15
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
2 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
3 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
4 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
5 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
6 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
# ℹ 9 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl># unique(asv_long_avg_wtax_clean$Supergroup_simplified)seq_only_mat <- asv_long_avg_wtax_clean %>%
select(STN_NISKIN, FeatureID, MEAN_REPS_seq) %>% distinct() %>%
pivot_wider(names_from = FeatureID, values_from = MEAN_REPS_seq, values_fill = 0) %>% distinct() %>%
drop_na() %>%
column_to_rownames(var = "STN_NISKIN") %>%
as.matrixNormalize matrix of numeric metadata.
# Using clr approach, margin is set to 2 to perform this across each ASV ID, so the relationships between samples can be determined.
standarize_seq <- decostand(seq_only_mat, MARGIN = 2, method = "rclr")
# standarize_seq <- decostand(seq_only_mat, MARGIN = 2, method = "range")
# ?decostand
# Output from here should be stn_niskin
rownames(standarize_seq) <- rownames(seq_only_mat)
colnames(standarize_seq) <- colnames(seq_only_mat)First group to see how many clusters we need.
kclusts <-
tibble(k = 2:16) %>%
mutate(
kclust = map(k, ~ kmeans(standarize_seq, .x)),
glanced = map(kclust, broom::glance))
# Unnest
output_kclusts <- kclusts %>%
unnest(cols = c(glanced))
# ?kmeans
# plot
output_kclusts %>%
ggplot(aes(x = k, y = tot.withinss)) +
geom_line(alpha = 0.5, linewidth = 1.2, color = "darkgreen") +
geom_point(size = 2, color = "darkgreen") +
theme_linedraw()
Above plot shows hypothetical numbers of k clusters (x-axis) with the variance within the cluster (y-axis). We want to isolate the number of clusters where we cover as much variance as possible - BUT if we add more clusters, not much will be helpful. This means “the bend” here. In this case, We are going to go with 9 clusters.
Use chosen number of clusters (n=9) to compute kmeans.
kmeans_seq_5 <- kmeans(standarize_seq, centers = 9)
summary(kmeans_seq_5) Length Class Mode
cluster 90 -none- numeric
centers 95067 -none- numeric
totss 1 -none- numeric
withinss 9 -none- numeric
tot.withinss 1 -none- numeric
betweenss 1 -none- numeric
size 9 -none- numeric
iter 1 -none- numeric
ifault 1 -none- numeric# Report some findings using "broom"
broom::tidy(kmeans_seq_5)# A tibble: 9 × 10,566
0009645516609bda2246e1955ff9ec…¹ 00170876c27b5c06fb83…² 0030ad8ce44f257c42da…³
<dbl> <dbl> <dbl>
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6 0.00124 0.0737 0.00402
7 0.0507 -0.0778 -0.0665
8 -0.102 -0.0106 0.0790
9 0.133 -0.0124 -0.118
# ℹ abbreviated names: ¹`0009645516609bda2246e1955ff9ec1d`,
# ²`00170876c27b5c06fb835ad64c2125e1`, ³`0030ad8ce44f257c42daf3673bf92197`
# ℹ 10,563 more variables: `0031878bb9396df83dc75d335c478287` <dbl>,
# `0031bd7a8b2eceaf8e92456e9cae584d` <dbl>,
# `00372866120200b41a8ee26a79bf474d` <dbl>,
# `003ff3e98dff52952a7036585a32c2f2` <dbl>,
# `0045eeb379be57a2b672e0925b8014e1` <dbl>, …broom::glance(kmeans_seq_5)# A tibble: 1 × 4
totss tot.withinss betweenss iter
<dbl> <dbl> <dbl> <int>
1 58372. 8664. 49708. 3Combine kmeans clustering with original data.
# Combine with data
output_kmeans_seq5 <- broom::augment(kmeans_seq_5, standarize_seq)
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ff0b55135858c15f69a1bcebd6134f74 ff184f1db25826ac8dd45a45e7ee1c4b
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ff3893e435701ed72eb856f72ca3c1bd ff3b29b7cc7ae4d0374a91a9c9d59640
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ff492b7892fba861b97a072e14aa98b1 ff53dc3338bac648f0009bd00910627f
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ff5e2165a87b99c7c031c35057897abc ff642c6f8d18e2c37ad5c00d047c3eb9
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5 -0.04477361 -0.04549207
6 0.11912458 0.10304926
ff67adc28e62ff47e0639dcebf40f683 ff6e71faf5001d87b566c15430239c37
1 0.01295818 0.08358212
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5 0.03268295 -0.04072835
6 -0.01079536 0.10822871
ff71293d08e0ab7bccb757548f798fcc ff72a51a160102f5ca6cdc4c42ac7b68
1 0.08900522 0.004181146
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ff739662b76575f0d1d02ad0d130ebbc ff798a700b8e53818f6071478c14c0cf
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ff81d3aa9f5bda55fdefbd1eec255ed1 ff83fbd2f0a7f3d89a1116c870765a60
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4 -0.065101399 -0.096435111
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6 -0.073936584 -0.063834890
ff879334bf9f69e15c23d2414408fd6d ff9457b65d4c1d234639ee3b7580405f
1 0.009874108 0.12213954
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3 0.056608274 0.15770741
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5 -0.023911630 -0.07589939
6 0.063468309 0.17827543
ff9b2c0d405bbf080cc2628ccc1d945d ff9c65921696b2ccfb10b32a83b8a052
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5 -0.04477361 0.01589690
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ffaf64c301fc79b3073c01699a902858 ffb23cb0656ec0833247d03d5755d33a
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6 -0.5836815 -0.6879055
ffc0afaeebcb25c0b776b11661a7b8b6 ffc45b8bab44284e140214d6695747be
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3 0.11062277 0.13125511
4 0.12987803 0.15146701
5 -0.04477361 -0.05535667
6 0.11912458 0.14308976
ffca685a118446e131f61f64885bb871 ffd1535877018af9acee20d745b20737
1 0.09158332 0.46435571
2 -0.16604769 -0.40397419
3 0.11062277 0.54472564
4 0.12987803 0.66341871
5 -0.04477361 -0.04252184
6 0.11912458 0.46755798
ffd96ef15fd96d9d5b60c8fed2047a64 ffdd69fbe513404cfc559aba720ada33
1 0.15766398 -0.002433837
2 -0.20269383 0.032400854
3 0.01544412 -0.016545383
4 0.08912086 -0.013039799
5 0.03961330 0.018952001
6 -0.02109915 -0.026530289
ffdde364b4a214b443c2c5f05244abd8 ffe41c05a314b1a75fcbbc456a26495f
1 -0.26932177 0.10924787
2 0.67623478 -0.21985485
3 -0.04216987 0.13506698
4 -0.15138699 0.15649929
5 0.07306456 -0.06367894
6 -0.07096962 0.15156988
ffe6aff859ffbb79d9cbbf74ffc597f6 ffec89ba79c1319c553445880138beda
1 0.1271396 0.01020165
2 -0.2940941 -0.05526524
3 0.1524285 0.02889630
4 0.1768429 0.02603979
5 -0.0872841 -0.02770453
6 0.1811174 0.04244967
fff85d1b7c71811d19e0ffef5e5cf722 fffe762ccf23af75f41699f19f56dea2
1 0.04595008 -0.25334370
2 -0.07888616 0.64808593
3 0.05090680 0.07107685
4 0.06172233 -0.05470822
5 -0.01849484 0.02096381
6 0.05322651 0.05902577
ffff2dba0dcf2d28c9d7728c7913c40e .cluster
1 -0.11038329 2
2 0.21175759 9
3 -0.19282466 2
4 -0.20292444 2
5 0.08691461 5
6 -0.21600003 2kmeans_membership_5 <- data.frame(output_kmeans_seq5) %>%
select(STN_NISKIN = `.rownames`, CLUSTER = `.cluster`) %>% distinct()
# Samples in each cluster
summary(kmeans_membership_5$CLUSTER) 1 2 3 4 5 6 7 8 9
9 15 22 3 18 4 7 6 6 # save(kmeans_membership_5, file = "input-data/kmeans-membership-seqs-9.RData")3.0.0.1 Table with features
load("input-data/kmeans-membership-seqs-9.RData", verbose = TRUE)Loading objects:
kmeans_membership_5# Import table from Portela et al.
water_mass <- read.csv("input-data/water-mass-data.csv")head(metadata_seqsamples)# A tibble: 6 × 23
Station Niskin Depth Latitude Longitude Pressure Date Time_UTC ENV_VARIABLE
<int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
1 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 DIC
2 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 NH4
3 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 NO2
4 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 NO3
5 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 Oxygen_CTD
6 1 5 18.6 28.8 89.5 18.7 7/30/2… 18:56:28 PO4
# ℹ 14 more variables: value <dbl>, DATE_GMT <date>, TIME_GMT <chr>,
# datetime_cst <dttm>, SUNRISE <dttm>, SUNSET <dttm>, DAY_NIGHT <chr>,
# HR_OF_DAY <int>, stn <chr>, STN_ORDER <fct>, TRANSECT <fct>,
# DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>, STN_NISKIN <chr>ts_only <- metadata_seqsamples %>%
select(STN_NISKIN, ENV_VARIABLE, value) %>%
filter(ENV_VARIABLE == "Temperature" | ENV_VARIABLE == "Salinity") %>%
distinct() %>%
pivot_wider(names_from = ENV_VARIABLE, values_from = value)
# head(ts_only)
head(water_mass) Watermass TEMP_MIN TEMP_MAX SAL_MIN SAL_MAX
1 CSWr 22.0 28.0 0.00 36.70
2 NASUW 20.0 25.0 36.80 40.00
3 GCW 20.0 22.5 0.00 36.80
4 TACW 8.0 20.0 35.10 36.60
5 TACWn 9.0 13.0 35.25 35.90
6 AAIW 5.5 6.5 35.05 35.11# add id for tracking
# p2 <- penguins |> mutate(id = row_number(), .before = 1)
tmp <- ts_only %>%
mutate(ID = row_number(), .before = 1) %>%
left_join(water_mass,
join_by(Temperature >= TEMP_MIN,
Temperature < TEMP_MAX,
Salinity >= SAL_MIN,
Salinity < SAL_MAX)) %>%
reframe(Watermass = paste(Watermass, collapse = ", "), .by = ID)
ts_mass <- ts_only %>%
mutate(ID = row_number(), .before = 1) %>%
left_join(tmp)Joining with `by = join_by(ID)`# unique(ts_mass$Watermass)table_wfeature <- metadata_seqsamples %>%
left_join(metadata_stn_info %>%
select(Station, OilRig_Count, MLD, SAL_MAX, O2_MIN, DCM, STN_CATEGORY_3, STN_CATEGORY_4)) %>%
left_join(kmeans_membership_5, by = join_by(STN_NISKIN)) %>%
# Water mass
left_join((ts_mass %>% select(STN_NISKIN, Watermass)), by = join_by(STN_NISKIN)) %>%
# Refine water mass
mutate(WATER_MASS = case_when(
(Watermass == "TACW, TACWn") ~ "TACWn",
# (Watermass == "NA") ~ "Uncharacterized",
(Watermass == "NA" & Depth > 150) ~ "Uncharacterized Deep",
(Watermass == "NA" & Depth <= 150) ~ "Uncharacterized Upper",
TRUE ~ Watermass)) %>%
mutate(SAL_THRESHOLD = case_when(
Depth > SAL_MAX ~ "Below salinity maximum", #At and below the salinity maximum
Depth <= SAL_MAX ~ "Above salinity maximum",
)) %>%
mutate(MLD_THRESHOLD = case_when(
Depth > MLD ~ "Below MLD", #At and below the salinity maximum
Depth <= MLD ~ "Above MLD",
)) %>%
mutate(DCM_THRESHOLD = case_when(
Depth > DCM ~ "Below DCM",
Depth <= DCM ~ "Above DCM", #At and above the DCM
)) %>%
mutate(O2_THRESHOLD = case_when(
Depth > O2_MIN ~ "Below oxygen minimum",
Depth <= O2_MIN ~ "Above oxygen minimum", #At and above the O2 minimum
))Joining with `by = join_by(Station)`table_wstn_wdepth <- table_wfeature %>%
select(-ENV_VARIABLE, -value, -Watermass) %>% distinct()
table(table_wstn_wdepth$WATER_MASS)
CSWr GCW TACW
8 7 9
TACWn Uncharacterized Deep Uncharacterized Upper
3 44 19 head(table_wstn_wdepth)# A tibble: 6 × 34
Station Niskin Depth Latitude Longitude Pressure Date Time_UTC DATE_GMT
<int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <date>
1 1 5 18.6 28.8 89.5 18.7 7/30/20… 18:56:28 2023-07-30
2 1 10 2.48 28.8 89.5 2.50 7/30/20… 18:56:28 2023-07-30
3 2 1 353. 28.6 89.1 356. 7/30/20… 21:36:00 2023-07-30
4 2 3 269. 28.6 89.1 271. 7/30/20… 21:36:00 2023-07-30
5 2 6 150. 28.6 89.1 151. 7/30/20… 21:36:00 2023-07-30
6 2 8 24.2 28.6 89.1 24.3 7/30/20… 21:36:00 2023-07-30
# ℹ 25 more variables: TIME_GMT <chr>, datetime_cst <dttm>, SUNRISE <dttm>,
# SUNSET <dttm>, DAY_NIGHT <chr>, HR_OF_DAY <int>, stn <chr>,
# STN_ORDER <fct>, TRANSECT <fct>, DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>,
# STN_NISKIN <chr>, OilRig_Count <int>, MLD <dbl>, SAL_MAX <dbl>,
# O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>, STN_CATEGORY_4 <chr>,
# CLUSTER <fct>, WATER_MASS <chr>, SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>,
# DCM_THRESHOLD <chr>, O2_THRESHOLD <chr>3.0.1 Create Table S1
# Import sequence and ASV stats for a supplementary table
tmp_seq_stats <- read.csv(file = "output-tables/seq-asv-stats.csv") %>%
unite(STN_NISKIN, STN, NISKIN, sep = "_") %>%
select(STN_NISKIN, Sequence_files, Total_ASVs, Total_sequences)
# head(tmp_seq_stats)table_supp <- table_wfeature %>%
select(-Pressure, -datetime_cst, -starts_with("SUN"), -HR_OF_DAY, -stn) %>%
pivot_wider(names_from = ENV_VARIABLE, values_from = value) %>%
left_join(tmp_seq_stats)Joining with `by = join_by(STN_NISKIN)`glimpse(table_supp)Rows: 90
Columns: 44
$ Station <int> 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, …
$ Niskin <int> 5, 10, 1, 3, 6, 8, 11, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, …
$ Depth <dbl> 18.603, 2.482, 353.454, 269.068, 149.888, 24.170, 2.90…
$ Latitude <dbl> 28.849, 28.849, 28.648, 28.648, 28.648, 28.648, 28.648…
$ Longitude <dbl> 89.460, 89.460, 89.136, 89.136, 89.136, 89.136, 89.136…
$ Date <chr> "7/30/2023", "7/30/2023", "7/30/2023", "7/30/2023", "7…
$ Time_UTC <chr> "18:56:28", "18:56:28", "21:36:00", "21:36:00", "21:36…
$ DATE_GMT <date> 2023-07-30, 2023-07-30, 2023-07-30, 2023-07-30, 2023-…
$ TIME_GMT <chr> "18:56:28", "18:56:28", "21:36:00", "21:36:00", "21:36…
$ DAY_NIGHT <chr> "Day", "Day", "Day", "Day", "Day", "Day", "Day", "Day"…
$ STN_ORDER <fct> S1, S1, S2, S2, S2, S2, S2, S3, S3, S3, S3, S3, S3, S3…
$ TRANSECT <fct> transect1, transect1, transect1, transect1, transect1,…
$ DIST_OUTFLOW <dbl> 13151.06, 13151.06, 41762.97, 41762.97, 41762.97, 4176…
$ DEPTH_BIN <chr> "5-25m", "< 5m", "270-400m", "60-270m", "60-270m", "5-…
$ STN_NISKIN <chr> "S1_N5", "S1_N10", "S2_N1", "S2_N3", "S2_N6", "S2_N8",…
$ OilRig_Count <int> 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, …
$ MLD <dbl> 11.5, 11.5, 8.5, 8.5, 8.5, 8.5, 8.5, 10.5, 10.5, 10.5,…
$ SAL_MAX <dbl> 18.603, 18.603, 32.750, 32.750, 32.750, 32.750, 32.750…
$ O2_MIN <dbl> 46.453, 46.453, 353.408, 353.408, 353.408, 353.408, 35…
$ DCM <dbl> 2.482, 2.482, 25.800, 25.800, 25.800, 25.800, 25.800, …
$ STN_CATEGORY_3 <chr> "Coastal", "Coastal", "Coastal", "Coastal", "Coastal",…
$ STN_CATEGORY_4 <chr> "Coastal", "Coastal", "Coastal", "Coastal", "Coastal",…
$ CLUSTER <fct> 7, 2, 6, 2, 2, 7, 5, 6, 6, 2, 9, 6, 6, 9, 1, 4, 8, 9, …
$ Watermass <chr> "CSWr", "NA", "TACW, TACWn", "TACW", "TACW", "CSWr", "…
$ WATER_MASS <chr> "CSWr", "Uncharacterized Upper", "TACWn", "TACW", "TAC…
$ SAL_THRESHOLD <chr> "Above salinity maximum", "Above salinity maximum", "B…
$ MLD_THRESHOLD <chr> "Below MLD", "Above MLD", "Below MLD", "Below MLD", "B…
$ DCM_THRESHOLD <chr> "Below DCM", "Above DCM", "Below DCM", "Below DCM", "B…
$ O2_THRESHOLD <chr> "Above oxygen minimum", "Above oxygen minimum", "Below…
$ DIC <dbl> 2098.112, 2033.992, 2212.046, 2204.230, 2198.769, 2158…
$ NH4 <dbl> 0.640, 0.750, 1.230, 1.250, 0.960, 0.610, 0.800, 0.480…
$ NO2 <dbl> 0.07, 0.38, 0.03, 0.02, 0.02, 0.12, 0.00, 0.02, 0.02, …
$ NO3 <dbl> 0.060, 0.230, 25.910, 16.100, 16.790, 3.730, 0.050, 23…
$ Oxygen_CTD <dbl> 2.341, 3.207, 2.704, 2.880, 2.925, 4.396, 3.563, 4.743…
$ PO4 <dbl> 0.080, 0.180, 1.650, 1.100, 0.940, 0.380, 0.160, 1.620…
$ SIL <dbl> 2.70, 10.00, 14.00, 7.30, 6.80, 9.60, 4.10, 26.40, 26.…
$ Salinity <dbl> 36.253, 27.381, 35.278, 35.693, 36.002, 36.103, 30.101…
$ TA <dbl> 0.002417433, 0.002420547, 0.002364161, 0.002376253, 0.…
$ Temperature <dbl> 24.227, 30.918, 10.561, 13.332, 15.248, 25.404, 31.751…
$ pH <dbl> 8.0718, 8.2451, 7.7250, 7.7869, 7.8140, 7.9767, 7.5511…
$ Prok_count <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Sequence_files <chr> "GOM23_S1_N5_18S_REP1,GOM23_S1_N5_18S_REP1_orig", "GOM…
$ Total_ASVs <int> 3380, 1155, 2239, 3320, 6415, 4013, 3013, 3466, 2352, …
$ Total_sequences <int> 452806, 439363, 513337, 545869, 944938, 466075, 802360…# save(table_supp, file = "output-tables/table_supp.RData")Repeat and format for supplementary table
table_supp1 <- table_wfeature %>%
select(-Pressure, -datetime_cst, -starts_with("SUN"), -HR_OF_DAY, -stn) %>%
pivot_wider(names_from = ENV_VARIABLE, values_from = value) %>%
left_join(tmp_seq_stats) %>%
select(Station, Niskin, STN_NISKIN, Depth, DEPTH_BIN, Latitude, Longitude,
Date, Time_UTC, DAY_NIGHT, TRANSECT, DIST_OUTFLOW, STN_CATEGORY_4, CLUSTER,
OilRig_Count, MLD, SAL_MAX, O2_MIN, DCM, Temperature, Salinity,
DIC, NH4, NO2, NO3, PO4, SIL, Oxygen_CTD, TA, pH, WATER_MASS, Prok_count, Sequence_files, Total_ASVs, Total_sequences)Joining with `by = join_by(STN_NISKIN)`# write.csv(table_supp1, file = "output-tables/supp-table-allsampleinfo.csv")head(table_supp1)# A tibble: 6 × 35
Station Niskin STN_NISKIN Depth DEPTH_BIN Latitude Longitude Date Time_UTC
<int> <int> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr>
1 1 5 S1_N5 18.6 5-25m 28.8 89.5 7/30/2… 18:56:28
2 1 10 S1_N10 2.48 < 5m 28.8 89.5 7/30/2… 18:56:28
3 2 1 S2_N1 353. 270-400m 28.6 89.1 7/30/2… 21:36:00
4 2 3 S2_N3 269. 60-270m 28.6 89.1 7/30/2… 21:36:00
5 2 6 S2_N6 150. 60-270m 28.6 89.1 7/30/2… 21:36:00
6 2 8 S2_N8 24.2 5-25m 28.6 89.1 7/30/2… 21:36:00
# ℹ 26 more variables: DAY_NIGHT <chr>, TRANSECT <fct>, DIST_OUTFLOW <dbl>,
# STN_CATEGORY_4 <chr>, CLUSTER <fct>, OilRig_Count <int>, MLD <dbl>,
# SAL_MAX <dbl>, O2_MIN <dbl>, DCM <dbl>, Temperature <dbl>, Salinity <dbl>,
# DIC <dbl>, NH4 <dbl>, NO2 <dbl>, NO3 <dbl>, PO4 <dbl>, SIL <dbl>,
# Oxygen_CTD <dbl>, TA <dbl>, pH <dbl>, WATER_MASS <chr>, Prok_count <dbl>,
# Sequence_files <chr>, Total_ASVs <int>, Total_sequences <int>unique(table_supp1$Sequence_files) [1] "GOM23_S1_N5_18S_REP1,GOM23_S1_N5_18S_REP1_orig"
[2] "GOM23_S1_N10_18S_REP2,GOM23_S1_N10_18S_REP2_orig"
[3] "GOM23_S2_N1_18S_REP2,GOM23_S2_N1_18S_REP2_orig"
[4] "GOM23_S2_N3_18S_REP1,GOM23_S2_N3_18S_REP1_orig"
[5] "GOM23_S2_N6_18S_REP1,GOM23_S2_N6_18S_REP1_orig,GOM23_S2_N6_18S_REP2,GOM23_S2_N6_18S_REP2_orig"
[6] "GOM23_S2_N8_18S_REP2,GOM23_S2_N8_18S_REP2_orig"
[7] "GOM23_S2_N11_18S_REP1,GOM23_S2_N11_18S_REP1_orig,GOM23_S2_N11_18S_REP2,GOM23_S2_N11_18S_REP2_orig"
[8] "GOM23_S3_N1_18S_S166,GOM23_S3_N1_18S_orig"
[9] "GOM23_S3_N2_18S_S174,GOM23_S3_N2_18S_orig"
[10] "GOM23_S3_N3_18S_S182,GOM23_S3_N3_18S_orig"
[11] "GOM23_S3_N4_18S_S97,GOM23_S3_N4_18S_orig"
[12] "GOM23_S3_N5_18S_S105,GOM23_S3_N5_18S_orig"
[13] "GOM23_S3_N6_18S_S113,GOM23_S3_N6_18S_orig"
[14] "GOM23_S3_N7_18S_S121,GOM23_S3_N7_18S_orig"
[15] "GOM23_S3_N8_18S_S129,GOM23_S3_N8_18S_orig"
[16] "GOM23_S3_N9_18S_S137,GOM23_S3_N9_18S_orig"
[17] "GOM23_S3_N12_18S_S144,GOM23_S3_N12_18S_orig"
[18] "GOM23_S4_N1_18S_S152,GOM23_S4_N1_18S_orig"
[19] "GOM23_S4_N2_18S_S160,GOM23_S4_N2_18S_orig"
[20] "GOM23_S4_N3_18S_S167,GOM23_S4_N3_18S_orig"
[21] "GOM23_S4_N4_18S_S175,GOM23_S4_N4_18S_orig"
[22] "GOM23_S4_N5_18S_S183,GOM23_S4_N5_18S_orig"
[23] "GOM23_S4_N6_18S_S98,GOM23_S4_N6_18S_orig"
[24] "GOM23_S4_N9_18S_S106,GOM23_S4_N9_18S_orig"
[25] "GOM23_S4_N10_18S_S114,GOM23_S4_N10_18S_orig"
[26] "GOM23_S4_N11_18S_S122,GOM23_S4_N11_18S_orig"
[27] "GOM23_S5_N1_18S_S130,GOM23_S5_N1_18S_orig"
[28] "GOM23_S5_N2_18S_orig"
[29] "GOM23_S5_N3_18S_S145,GOM23_S5_N3_18S_orig"
[30] "GOM23_S5_N4_18S_S153,GOM23_S5_N4_18S_orig"
[31] "GOM23_S5_N5_18S_S161,GOM23_S5_N5_18S_orig"
[32] "GOM23_S5_N6_18S_S168,GOM23_S5_N6_18S_orig"
[33] "GOM23_S5_N10_18S_S176,GOM23_S5_N10_18S_orig"
[34] "GOM23_S5_N11_18S_S184,GOM23_S5_N11_18S_orig"
[35] "GOM23_S5_N12_18S_S99,GOM23_S5_N12_18S_orig"
[36] "GOM23_S7_N2_18S_S107,GOM23_S7_N2_18S_orig"
[37] "GOM23_S7_N3_18S_S115,GOM23_S7_N3_18S_orig"
[38] "GOM23_S7_N4_18S_S123,GOM23_S7_N4_18S_orig"
[39] "GOM23_S7_N5_18S_S131,GOM23_S7_N5_18S_orig"
[40] "GOM23_S7_N6_18S_S138,GOM23_S7_N6_18S_orig"
[41] "GOM23_S7_N7_18S_S146,GOM23_S7_N7_18S_orig"
[42] "GOM23_S7_N9_18S_S154,GOM23_S7_N9_18S_orig"
[43] "GOM23_S7_N10_18S_S162,GOM23_S7_N10_18S_orig"
[44] "GOM23_S7_N11_18S_S169,GOM23_S7_N11_18S_orig"
[45] "GOM23_S7_N12_18S_S177,GOM23_S7_N12_18S_orig"
[46] "GOM23_S8_N1_18S_S185,GOM23_S8_N1_18S_orig"
[47] "GOM23_S8_N3_18S_S100"
[48] "GOM23_S8_N4_18S_S108,GOM23_S8_N4_18S_orig"
[49] "GOM23_S8_N5_18S_S116,GOM23_S8_N5_18S_orig"
[50] "GOM23_S8_N7_18S_S124,GOM23_S8_N7_18S_orig"
[51] "GOM23_S8_N8_18S_S132,GOM23_S8_N8_18S_orig"
[52] "GOM23_S8_N9_18S_orig"
[53] "GOM23_S8_N10_18S_S147,GOM23_S8_N10_18S_orig"
[54] "GOM23_S8_N11_18S_S155,GOM23_S8_N11_18S_orig"
[55] "GOM23_S9_N5_18S_S170,GOM23_S9_N5_18S_orig"
[56] "GOM23_S9_N9_18S_orig"
[57] "GOM23_S11_N1_18S_S186,GOM23_S11_N1_18S_orig"
[58] "GOM23_S11_N4_18S_S101,GOM23_S11_N4_18S_orig"
[59] "GOM23_S11_N5_18S_S109,GOM23_S11_N5_18S_orig"
[60] "GOM23_S11_N6_18S_S117,GOM23_S11_N6_18S_orig"
[61] "GOM23_S11_N7_18S_S125,GOM23_S11_N7_18S_orig"
[62] "GOM23_S11_N10_18S_S133,GOM23_S11_N10_18S_orig"
[63] "GOM23_S11_N11_18S_S140,GOM23_S11_N11_18S_orig"
[64] "GOM23_S11_N12_18S_S148,GOM23_S11_N12_18S_orig"
[65] "GOM23_S12_N1_18S_S156,GOM23_S12_N1_18S_orig"
[66] "GOM23_S12_N2_18S_S163,GOM23_S12_N2_18S_orig"
[67] "GOM23_S12_N3_18S_S171,GOM23_S12_N3_18S_orig"
[68] "GOM23_S12_N4_18S_S179,GOM23_S12_N4_18S_orig"
[69] "GOM23_S12_N5_18S_S187,GOM23_S12_N5_18S_orig"
[70] "GOM23_S12_N6_18S_S102,GOM23_S12_N6_18S_orig"
[71] "GOM23_S12_N11_18S_S118,GOM23_S12_N11_18S_orig"
[72] "GOM23_S12_N12_18S_S126,GOM23_S12_N12_18S_orig"
[73] "GOM23_S14_N1_18S_S134,GOM23_S14_N1_18S_orig"
[74] "GOM23_S14_N2_18S_S141,GOM23_S14_N2_18S_orig"
[75] "GOM23_S14_N3_18S_S149,GOM23_S14_N3_18S_orig"
[76] "GOM23_S14_N4_18S_S157,GOM23_S14_N4_18S_orig"
[77] "GOM23_S14_N5_18S_S164,GOM23_S14_N5_18S_orig"
[78] "GOM23_S14_N6_18S_S172,GOM23_S14_N6_18S_orig"
[79] "GOM23_S14_N7_18S_S180,GOM23_S14_N7_18S_orig"
[80] "GOM23_S14_N8_18S_S188,GOM23_S14_N8_18S_orig"
[81] "GOM23_S14_N9_18S_S103,GOM23_S14_N9_18S_orig"
[82] "GOM23_S14_N10_18S_S111,GOM23_S14_N10_18S_orig"
[83] "GOM23_S14_N11_18S_S119,GOM23_S14_N11_18S_orig"
[84] "GOM23_S14_N12_18S_S127,GOM23_S14_N12_18S_orig"
[85] "GOM_S15_N6_18S_orig"
[86] "GOM_S15_N7_18S_S158,GOM_S15_N7_18S_orig"
[87] "GOM_S15_N8_18S_S165,GOM_S15_N8_18S_orig"
[88] "GOM_S15_N9_18S_S173,GOM_S15_N9_18S_orig"
[89] "GOM_S15_N10_18S_S181,GOM_S15_N10_18S_orig"
[90] "GOM_S15_N12_18S_S189,GOM_S15_N12_18S_orig" 4 Sample comparison & taxonomic classification
4.1 PCA analysis
Create wide format matrix with sequence counts averaged across replicates (by ASV) Make wide format and as a matrix.
asv_wide_avg_matrix <- asv_long_avg_wtax_clean %>%
filter(STN_NISKIN != "S0_N0") %>%
select(FeatureID, STN_NISKIN, MEAN_REPS_seq) %>%
pivot_wider(names_from = STN_NISKIN, values_from = MEAN_REPS_seq, values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "FeatureID") %>%
as.matrix()
# dim(asv_wide_avg_matrix)
# Working with 10563 ASVs
# dim(asv_long_avg_wtax_clean)pca_clr <- prcomp(data.frame(compositions::clr(t(asv_wide_avg_matrix))))variance_clr <- (pca_clr$sdev^2)/sum(pca_clr$sdev^2)
barplot(variance_clr, main = "Log-Ratio PCA Screeplot", xlab = "PC Axis", ylab = "% Variance",
cex.names = 1.5, cex.axis = 1.5, cex.lab = 1.5, cex.main = 1.5)
4.1.0.1 Plot PCA
PCA with cluster
# CLUSTER_COLOR <- c("#c85e82", "#efc85a", "#df9147","#6e9b4a", "#606aa9")
# CLUSTER_NUM <- c(1, 2, 3, 4, 5)
CLUSTER_COLOR <- c("#bd2a2a","#c85e82", "#efc85a", "#df9147","#47c475","#8b94b5","#6e9b4a", "#606aa9", "#524740")
CLUSTER_NUM <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
names(CLUSTER_NUM) <- CLUSTER_COLOR
PCA_PLOT_kmeans <- data.frame(pca_clr$x, STN_NISKIN = rownames(pca_clr$x)) %>%
select(PC1, PC2, PC3, STN_NISKIN) %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = depth_bin)) %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
## Generate PCA plot
ggplot(aes(x = PC1, y = PC2,
shape = SURFACE,
fill = cluster_order)) + # Try this for a supplementary figure.
# fill = depth_order)) +
geom_hline(yintercept = 0) + geom_vline(xintercept = 0, color = "#525252") +
geom_jitter(stroke = 0.6, size = 2.5, width = 0.8) +
# geom_jitter(stroke = 0.6, size = 2.5, width = 0.8, shape = 21) +
ylab(paste0('PC2 ',round(variance_clr[2]*100,2),'%')) +
xlab(paste0('PC1 ',round(variance_clr[1]*100,2),'%')) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = CLUSTER_COLOR) +
theme_bw() +
theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold")) +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`PCA_PLOT_kmeans
PCA with depth
blues <- c("#dde4f0","#b7cae8","#96b3e3",
"#6b94d6","#3473d9", "#0b4fbd",
"#0b429c", "#07214a", "#081730")
names(blues) <- depth_bin
PCA_PLOT_depth <- data.frame(pca_clr$x, STN_NISKIN = rownames(pca_clr$x)) %>%
select(PC1, PC2, PC3, STN_NISKIN) %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = depth_bin)) %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
## Generate PCA plot
ggplot(aes(x = PC1, y = PC2,
shape = SURFACE,
fill = depth_order)) +
geom_hline(yintercept = 0) + geom_vline(xintercept = 0, color = "#525252") +
geom_jitter(stroke = 0.6, size = 2.5, width = 0.8) +
# geom_jitter(stroke = 0.6, size = 2.5, width = 0.8, shape = 21) +
ylab(paste0('PC2 ',round(variance_clr[2]*100,2),'%')) +
xlab(paste0('PC1 ',round(variance_clr[1]*100,2),'%')) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = blues) +
coord_fixed() +
theme_bw() +
theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm")) +
plot.margin = margin(b = 0, unit = "pt")) +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`PCA_PLOT_depth
4.1.1 PCA above DCM
# head(table1_wfeature)
above_DCM_df <- table_wstn_wdepth %>%
filter(DCM_THRESHOLD == "Above DCM")
above_dcm_list <- as.character(unique(above_DCM_df$STN_NISKIN))Make wide format and as a matrix.
aboveDMC_wide_matrix <- asv_long_avg_wtax_clean %>%
filter(STN_NISKIN %in% above_dcm_list) %>%
select(FeatureID, STN_NISKIN, MEAN_REPS_seq) %>%
pivot_wider(names_from = STN_NISKIN, values_from = MEAN_REPS_seq, values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "FeatureID") %>%
as.matrix()pca_clr_abovedcm <- prcomp(data.frame(compositions::clr(t(aboveDMC_wide_matrix))))variance_clr_abovedcm <- (pca_clr_abovedcm$sdev^2)/sum(pca_clr_abovedcm$sdev^2)
barplot(variance_clr_abovedcm, main = "Log-Ratio PCA Screeplot", xlab = "PC Axis", ylab = "% Variance",
cex.names = 1.5, cex.axis = 1.5, cex.lab = 1.5, cex.main = 1.5)
blues <- c("#dde4f0","#b7cae8","#96b3e3",
"#6b94d6","#3473d9", "#0b4fbd",
"#0b429c", "#07214a", "#081730")
names(blues) <- depth_bin
pca_abovedcm <- data.frame(pca_clr_abovedcm$x, STN_NISKIN = rownames(pca_clr_abovedcm$x)) %>%
select(PC1, PC2, PC3, STN_NISKIN) %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = depth_bin)) %>%
# mutate(FEATURE = str_replace_na(Features_list, replacement = "other")) %>%
# mutate(FEATURE_ORDER = factor(FEATURE, levels = watercol_features, labels = watercol_features_label)) %>%
## Generate PCA plot
ggplot(aes(x = PC1, y = PC2,
shape = ,
fill = depth_order)) +
geom_hline(yintercept = 0) + geom_vline(xintercept = 0, color = "#525252") +
geom_jitter(stroke = 0.6, size = 2.5, width = 0.8, shape = 21, color = "black") +
ylab(paste0('PC2 ',round(variance_clr_abovedcm[2]*100,2),'%')) +
xlab(paste0('PC1 ',round(variance_clr_abovedcm[1]*100,2),'%')) +
# scale_shape_manual(values = watercol_shape) +
scale_fill_manual(values = blues) +
theme_bw() +
theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
plot.margin = margin(2, 1, 2, 1, "cm")) +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`pca_abovedcm
# ggsave("figures/pca-plot.svg", width = 6, height = 5, device = "svg", limitsize = FALSE)5 Diversity indices
Use wide dataframe from above.
shannon_all <- vegan::diversity(asv_wide_avg_matrix, index = "shannon", MARGIN = 2)# ?diversity
invsimp_all <- vegan::diversity(asv_wide_avg_matrix, index = "invsimpson", MARGIN = 2)
# head(data.frame(invsimp_all))5.1 Plot diversity
5.1.1 Shannon
By depth
depth_bin <- c("< 5m","5-25m","25-60m",
"60-270m","270-400m","400-800m",
"800-1200m","1200-2000m","2000-3000m")
SHANNON <- data.frame(shannon_all) %>%
rownames_to_column(var = "STN_NISKIN") %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = depth_order,
x = shannon_all,
fill = depth_order,
shape = MLD_THRESHOLD)) +
geom_jitter(stroke = 0.6, size = 2.5, width = 0.8) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = blues) +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
axis.title = element_text(color = "black", size = 10),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 1, size = 10),
panel.grid.major = element_line(color = "grey80", linetype = "dashed")) +
labs(x = "Shannon Diversity", y = "") +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`SHANNON_cluster <- data.frame(shannon_all) %>%
rownames_to_column(var = "STN_NISKIN") %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = (depth_bin))) %>%
mutate(STN_ORDER = factor(as.character(Station), levels = stn_order_shore_offshore)) %>%
mutate(transect_order = factor(TRANSECT, levels = c("transect1","transect2","transect3"), labels = c("Transect 1", "Transect 2", "Transect 3"))) %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
#
ggplot(aes(x = STN_ORDER, y = shannon_all,
shape = SURFACE,
fill = cluster_order)) +
facet_grid(cols = vars(transect_order), scale = "free_x") +
geom_jitter(stroke = 0.6, width = 0.2, size = 3) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = CLUSTER_COLOR) +
theme_bw() + theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm"),
plot.margin = margin(b = 0, unit = "pt"),
strip.text = element_text(color="black", size=12),
strip.background = element_blank()) +
labs(y = "Shannon Diversity", x = "Station") +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5, reverse = FALSE)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`SHANNON_cluster
SHANNON_depth <- data.frame(shannon_all) %>%
rownames_to_column(var = "STN_NISKIN") %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = (depth_bin))) %>%
mutate(STN_ORDER = factor(as.character(Station), levels = stn_order_shore_offshore)) %>%
mutate(transect_order = factor(TRANSECT, levels = c("transect1","transect2","transect3"), labels = c("Transect 1", "Transect 2", "Transect 3"))) %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
#
ggplot(aes(x = STN_ORDER, y = shannon_all,
shape = SURFACE,
fill = depth_order)) +
facet_grid(cols = vars(transect_order), scale = "free_x") +
geom_jitter(stroke = 0.6, width = 0.2, size = 3) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = blues) +
theme_bw() + theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm"),
plot.margin = margin(b = 0, unit = "pt"),
strip.text = element_text(color="black", size=12),
strip.background = element_blank()) +
labs(y = "Shannon Diversity", x = "Station") +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5, reverse = FALSE)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`SHANNON_depth
SHANNON_depth_profile <- data.frame(shannon_all) %>%
rownames_to_column(var = "STN_NISKIN") %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = (depth_bin))) %>%
mutate(STN_ORDER = factor(as.character(Station), levels = stn_order_shore_offshore)) %>%
mutate(transect_order = factor(TRANSECT, levels = c("transect1","transect2","transect3"), labels = c("Transect 1", "Transect 2", "Transect 3"))) %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
#
ggplot(aes(y = Depth, x = shannon_all,
shape = SURFACE,
fill = depth_order)) +
facet_grid(cols = vars(transect_order), scale = "free_y") +
geom_hline(yintercept = table_wstn_wdepth$DCM, colour = "red", alpha = 0.2) +
geom_point(stroke = 0.6, size = 3) +
scale_y_reverse() +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = blues) +
theme_bw() + theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm"),
plot.margin = margin(b = 0, unit = "pt"),
strip.text = element_text(color="black", size=12),
strip.background = element_blank()) +
labs(x = "Shannon Diversity", y = "Depth (m)") +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5, reverse = FALSE)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`5.1.2 Inverse Simpson
SIMPSON <- data.frame(invsimp_all) %>%
rownames_to_column(var = "STN_NISKIN") %>%
left_join(table_wstn_wdepth) %>%
# Factoring
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = depth_order, x = invsimp_all,
fill = depth_order, shape = MLD_THRESHOLD)) +
geom_jitter(stroke = 0.6, size = 2.5, width = 0.8) +
scale_shape_manual(values = c(21, 23)) +
scale_fill_manual(values = blues) +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
axis.title = element_text(color = "black", size = 10),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 1, size = 10),
panel.grid.major = element_line(color = "grey80", linetype = "dashed")) +
labs(x = "Inverse Simpsons", y = "") +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 22,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5)))Joining with `by = join_by(STN_NISKIN)`SIMPSON
5.2 T-S plot
# Import table from Portela et al.
water_mass <- read.csv("input-data/water-mass-data.csv")water_mass %>%
ggplot() +
geom_rect(data = water_mass, aes(xmin = SAL_MIN, xmax = SAL_MAX, ymin = TEMP_MIN, ymax = TEMP_MAX,
linetype = Watermass, color = Watermass),
alpha = 0.1) +
theme_minimal() +
scale_color_manual(values = c("#7116c7", "black", "black", "#f571be", "#db3e25", "#339ef5", "#339ef5")) +
scale_linetype_manual(values = c("solid", "longdash", "dotted", "solid", "dotted", "longdash", "dotted"))
Portela, E., M. Tenreiro, E. Pallàs-Sanz, T. Meunier, A. Ruiz-Angulo, R. Sosa-Gutiérrez, and S. Cusí. 2018. Hydrography of the central and western gulf of Mexico. Journal of Geophysical Research. Oceans, 123(8):5134–5149, https://doi.org/10.1029/2018jc013813.
AAIW - Antarctic Intermediate Water CSWr - Caribbean Surface Water Remnant GCW - Gulf Common Water NADW - North Atlantic Deep Water NASUW - North Atlantic Subtropical Underwater TACW - Tropical Atlantic Central Water TACWn - Tropical Atlantic Central Water (nucleus)
5.3 Plot K-means T-S plotß
# head(table1_wfeature)
# CLUSTER_COLOR <- c("#c85e82", "#efc85a", "#df9147","#6e9b4a", "#606aa9")
# CLUSTER_NUM <- c(1, 2, 3, 4, 5)
CLUSTER_COLOR <- c("#bd2a2a","#c85e82", "#efc85a", "#df9147","#47c475","#8b94b5","#6e9b4a", "#606aa9", "#524740")
CLUSTER_NUM <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
names(CLUSTER_NUM) <- CLUSTER_COLOR
ts_cluster_df <- table_supp %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(SURFACE = case_when(
DEPTH_BIN == "< 5m" ~ "Surface",
TRUE ~ "sub-surface"
)) %>%
mutate(depth_order = factor(DEPTH_BIN, levels = depth_bin))
water_mass_mod <- water_mass %>%
mutate(SAL_MIN_2 = case_when(
SAL_MIN < 25 ~ 25,
TRUE ~ SAL_MIN
))
ts_cluster <- ggplot() +
# water masses
geom_rect(data = water_mass_mod, aes(xmin = SAL_MIN_2, xmax = SAL_MAX, ymin = TEMP_MIN, ymax = TEMP_MAX, color = Watermass, linetype = Watermass),
fill = "grey80", linewidth = 0.5, alpha = 0.2) +
scale_color_manual(values = c("#7116c7", "black", "black", "#f571be", "#db3e25", "#339ef5", "#339ef5")) +
scale_linetype_manual(values = c("solid", "longdash", "dotted", "solid", "dotted", "longdash", "dotted")) +
geom_text_repel(data = water_mass_mod, size = 3, fontface = "bold", color = "black",
aes(label = Watermass, x = SAL_MIN_2, y = TEMP_MIN),
box.padding = 1.5, nudge_x = .25, nudge_y = .5,
segment.linetype = 3, segment.alpha = 0.8,
segment.curvature = -1e-20, force = 1,
point.padding = 0.5) +
# samples
geom_jitter(data = ts_cluster_df, aes(x = Salinity, y = Temperature, fill = cluster_order, shape = SURFACE), stroke = 0.6, size = 2.5, width = 0.2, stat = "identity", color = "black") +
scale_fill_manual(values = CLUSTER_COLOR) +
scale_shape_manual(values = c(21, 23)) +
scale_x_continuous(limits = c(25, 41)) +
# scale_y_log10() +
theme_bw() +
theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm")) +
plot.margin = margin(b = 0, unit = "pt")) +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 21,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5))) +
labs(x = "Salinity (ppt)", y = "Temperature (°C)")
ts_cluster
ts_depth <- ggplot() +
# water masses
geom_rect(data = water_mass_mod, aes(xmin = SAL_MIN_2, xmax = SAL_MAX, ymin = TEMP_MIN, ymax = TEMP_MAX, color = Watermass, linetype = Watermass),
fill = "grey80", linewidth = 0.5, alpha = 0.2) +
scale_color_manual(values = c("#7116c7", "black", "black", "#f571be", "#db3e25", "#339ef5", "#339ef5")) +
scale_linetype_manual(values = c("solid", "longdash", "dotted", "solid", "dotted", "longdash", "dotted")) +
geom_text_repel(data = water_mass_mod, size = 3, fontface = "bold", color = "black",
aes(label = Watermass, x = SAL_MIN_2, y = TEMP_MIN),
box.padding = 1.5, nudge_x = .25, nudge_y = .5,
segment.linetype = 3, segment.alpha = 0.8,
segment.curvature = -1e-20, force = 1,
point.padding = 0.5) +
# samples
geom_jitter(data = ts_cluster_df, aes(x = Salinity, y = Temperature, fill = depth_order, shape = SURFACE), stroke = 0.6, size = 2.5, width = 0.2, stat = "identity", color = "black") +
scale_fill_manual(values = blues) +
scale_shape_manual(values = c(21, 23)) +
scale_x_continuous(limits = c(25, 41)) +
# scale_y_log10() +
theme_bw() +
theme(axis.text = element_text(color="black", size=12),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
# plot.margin = margin(2, 1, 2, 1, "cm")) +
plot.margin = margin(b = 0, unit = "pt")) +
guides(fill = guide_legend(override.aes = list(
color = "black",
shape = 21,
size = 5)),
shape = guide_legend(override.aes = list(
color = "black",
size = 5))) +
labs(x = "Salinity (ppt)", y = "Temperature (°C)")
ts_depth
5.3.1 Combine pca and diversity
5.3.1.1 By depth
(SHANNON_depth_profile + theme(legend.position = "none")) / (PCA_PLOT_depth) +
plot_layout(nrow = 2, widths = c(1, 0.5)) +
plot_annotation(tag_levels = c("A"))
# ggsave("figures/diversity-pca-by-DEPTH.svg", width = 8, height = 7, device = "svg", limitsize = FALSE)(SHANNON_depth) / ((PCA_PLOT_depth) + theme(legend.position = "none") + (ts_depth + theme(legend.position = "right"))) + plot_layout(nrow = 2, heights = c(0.8, 1, 1)) + plot_annotation(tag_levels = c("A"))
# ggsave("figures/diversity-pca-ts-plot-DEPTH.svg", width = 8, height = 7, device = "svg", limitsize = FALSE)5.3.1.2 By cluster
(SHANNON_cluster) / ((PCA_PLOT_kmeans) + theme(legend.position = "none") + (ts_cluster + theme(legend.position = "right"))) + plot_layout(nrow = 2, heights = c(0.8, 1, 1)) + plot_annotation(tag_levels = c("A"))
# ggsave("figures/diversity-pca-ts-plot-CLUSTER.svg", width = 8, height = 7, device = "svg", limitsize = FALSE)6 Dendrogram by k-mean cluster
Import kmeans clustering. Order all samples by k-mean cluster.
Get order of kmeans clusters 1 to 5, with station niskin order.
table_kmeans_order <- table_supp %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
select(cluster_order, CLUSTER, STN_NISKIN, Depth) %>% distinct() %>%
arrange(cluster_order, Depth)
stn_bykmeans <- rev(as.character(table_kmeans_order$STN_NISKIN))
stn_bykmeans [1] "S14_N1" "S5_N2" "S14_N3" "S5_N3" "S4_N1" "S5_N4" "S11_N1"
[8] "S7_N3" "S4_N4" "S3_N4" "S7_N7" "S3_N7" "S8_N7" "S7_N9"
[15] "S12_N11" "S8_N10" "S7_N11" "S14_N12" "S3_N12" "S12_N12" "S15_N10"
[22] "S11_N10" "S4_N10" "S2_N8" "S1_N5" "S14_N2" "S12_N2" "S12_N3"
[29] "S7_N2" "S4_N3" "S12_N5" "S3_N1" "S7_N4" "S3_N2" "S11_N4"
[36] "S5_N6" "S15_N6" "S7_N5" "S8_N3" "S4_N6" "S7_N6" "S8_N4"
[43] "S11_N6" "S3_N5" "S15_N7" "S3_N6" "S14_N8" "S2_N1" "S14_N9"
[50] "S14_N6" "S14_N7" "S2_N11" "S15_N12" "S8_N11" "S5_N12" "S4_N11"
[57] "S11_N12" "S15_N9" "S8_N8" "S8_N9" "S11_N11" "S3_N9" "S7_N12"
[64] "S14_N5" "S5_N5" "S12_N6" "S11_N5" "S15_N8" "S14_N10" "S5_N1"
[71] "S12_N1" "S4_N2" "S14_N4" "S12_N4" "S4_N5" "S8_N1" "S3_N3"
[78] "S8_N5" "S11_N7" "S2_N3" "S2_N6" "S5_N11" "S1_N10" "S9_N9"
[85] "S4_N9" "S14_N11" "S9_N5" "S3_N8" "S5_N10" "S7_N10" Get color assignments
# CLUSTER_COLOR <- c("#c85e82", "#efc85a", "#df9147","#6e9b4a", "#606aa9")
# CLUSTER_NUM <- c(1, 2, 3, 4, 5)
CLUSTER_COLOR <- c("#bd2a2a","#c85e82", "#efc85a", "#df9147","#47c475","#8b94b5","#6e9b4a", "#606aa9", "#524740")
CLUSTER_NUM <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
names(CLUSTER_NUM) <- CLUSTER_COLOR
blues <- c("#dde4f0","#b7cae8","#96b3e3",
"#6b94d6","#3473d9", "#0b4fbd",
"#0b429c", "#07214a", "#081730")
names(blues) <- depth_bin
stn_order_seqs <- c(1, 2, 3, 4, 5,
9, 8, 7,
11, 12, 14, 15)
stn_order_color <- c("#e8dddc", "#e09f99", "#db756b","#d94638", "#db1e0d",
"#a89492", "#ad6f6a", "#a6453c",
"#4f4948", "#523735", "#4a221e", "#450d08")
names(stn_order_color) <- stn_order_seqsDendrogram assign lists
table_supp_dendro <- table_supp %>%
mutate(cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
mutate(stn_order = factor(STN_NISKIN, levels = (stn_bykmeans))) %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
mutate(water_mass_ORDER = factor(WATER_MASS, levels = water_mass_order)) %>%
mutate(dist_outflow = DIST_OUTFLOW/1000) %>%
mutate(STN = factor(Station, levels = stn_order_seqs)) %>%
mutate(OIL_COUNT = as.character(OilRig_Count)) %>%
mutate(MLD_THRES = case_when(
grepl("Above", MLD_THRESHOLD) ~ "Above",
TRUE ~ "Below"),
SAL_THRES = case_when(
grepl("Above", SAL_THRESHOLD) ~ "Above",
TRUE ~ "Below"),
DCM_THRES = case_when(
grepl("Above", DCM_THRESHOLD) ~ "Above",
TRUE ~ "Below"),
O2_THRES = case_when(
grepl("Above", O2_THRESHOLD) ~ "Above",
TRUE ~ "Below"))Set variables.
variable_fill <- list(
"K-means" = "cluster_order",
"Depth" = "depth_order",
"Water mass" = "water_mass_ORDER",
"Station" = "STN",
"MLD" = "MLD_THRES",
"DCM" = "DCM_THRES",
"O2 min" = "O2_THRES",
"Sal max" = "SAL_THRES",
"Distance" = "dist_outflow",
"Transect" = "TRANSECT",
"Oil Rigs" = "OIL_COUNT",
"Day Night" = "DAY_NIGHT",
"Cells/ml" = "Prok_count",
# Nutrients
"Oxygen" = "Oxygen_CTD",
"NO2" = "NO2",
"NO3" = "NO3",
"NH4" = "NH4",
"PO4" = "PO4",
"SIL" = "SIL",
"DIC" = "DIC",
"TA" = "TA"
)Set scales.
scale_fill <- list(
scale_fill_manual(values = CLUSTER_COLOR, guide = guide_legend(order = 1)), #kmeans
scale_fill_manual(values = blues, guide = guide_legend(reverse = TRUE, order = 2)), # depth
scale_fill_manual(values = water_mass_color, guide = guide_legend(reverse = TRUE, order = 3)), #water mass
scale_fill_manual(values = stn_order_color, guide = guide_legend(reverse = FALSE, order = 4)), # station
scale_fill_manual(values = c("#d8d8f0", "#181752"), guide = guide_legend(order = 5)), #MLD
scale_fill_manual(values = c("#d8d8f0", "#181752")), #DCM
scale_fill_manual(values = c("#d8d8f0", "#181752")), #O2
scale_fill_manual(values = c("#d8d8f0", "#181752")), #Sal
scale_fill_viridis_c(option = "magma", direction = -1, guide = guide_legend(reverse = FALSE, order = 6)), #dist
scale_fill_manual(values = c("#efaaf2", "#f003fc", "#470d4a"), guide = guide_legend(reverse = FALSE, order = 7)), #transect
scale_fill_manual(values = c("grey90","#e38aa6","#ba4e70","#a13356","#8a1a3d","#54051e"), guide = guide_legend(reverse = FALSE, order = 8)), #Oil rig count
scale_fill_manual(values = c("#96bbe0","#0a3157"), guide = guide_legend(reverse = TRUE, order = 9)), #DAy night
scale_fill_fermenter(na.value = "grey50", palette = "Reds", direction = 1, guide = guide_legend(order = 10)), # proks
## Nutrients
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 11)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 12)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 13)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 14)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 15)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 16)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 17)),
scale_fill_viridis(option = "plasma", guide = guide_legend(order = 18))
)Align variables and scales
names(scale_fill) <- names(variable_fill)
var_levels <- names(variable_fill)
var_levels [1] "K-means" "Depth" "Water mass" "Station" "MLD"
[6] "DCM" "O2 min" "Sal max" "Distance" "Transect"
[11] "Oil Rigs" "Day Night" "Cells/ml" "Oxygen" "NO2"
[16] "NO3" "NH4" "PO4" "SIL" "DIC"
[21] "TA" Set plot object.
heatmap_combined_vars <- ggplot(table_supp_dendro, aes(y = stn_order)) +
purrr::imap(
variable_fill, \(x, y) {
list(
geom_tile(
aes(
x = y,
fill = .data[[x]]
),
color = "black",
width = .95
),
scale_fill[[y]],
ggnewscale::new_scale_fill()
)
}
) +
scale_x_discrete(
limits = var_levels
) +
# coord_fixed(ratio = 1) +
facet_grid(rows = vars(cluster_order), scales = "free", space = "free") +
theme_void() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(
angle = 90,
hjust = 1
),
axis.title = element_blank(),
strip.text = element_blank(),
legend.position = "left",
panel.border = element_rect(fill = NA, linewidth = 0.8),
panel.background = element_rect(colour = NA, fill = "transparent"),
plot.background = element_rect(colour = NA, fill = "transparent"),
plot.margin = margin(0, 0, 0, 0, "cm"),
panel.spacing = unit(0, "cm"),
legend.title = element_blank()
)heatmap_combined_vars
6.0.1 k-mean tax
tax_color <- c("#607080","#2f97f7","#2c5c1d","#6da65b", "grey20",
"#de6600", #Gyrista
"#ee429b", #dinos
"#1abc9c", # ciliates
"#23b521","#f5bfb0","#fea02f", "#f7d472",
"#d7b6d7","#460e57",
"#572f0e","#916643","#c95e06", "#dba272", "#e6d2b8", "#b09b10",
"#ad3d46", #Haptista
"#5e0810", "grey30", "#db9399","#93c47d","#ca7ade")
tax_simplified_list <- as.character(rev(sort(unique(asv_long_avg_wtax_clean $Supergroup_simplified))))
tax_simplified_list [1] "TSAR-Telonemia_X" "TSAR-Stramenopiles_X"
[3] "TSAR-Radiolaria" "TSAR-Perkinsea"
[5] "TSAR-NA" "TSAR-Gyrista"
[7] "TSAR-Dinoflagellata" "TSAR-Ciliophora"
[9] "TSAR-Chrompodellids" "TSAR-Cercozoa"
[11] "TSAR-Bigyra" "TSAR-Apicomplexa"
[13] "TSAR-Alveolata_X" "Provora"
[15] "Obazoa-Opisthokonta_X" "Obazoa-Metazoa"
[17] "Obazoa-Ichthyosporea" "Obazoa-Fungi"
[19] "Obazoa-Choanoflagellata" "Obazoa-Apusomonada_X"
[21] "Haptista" "Excavata"
[23] "Eukaryota_X" "Cryptista"
[25] "Archaeplastida" "Amoebozoa" names(tax_color) <- tax_simplified_list
names(tax_simplified_list) <- tax_color# Sum by taxonomic groups
bykmeans_tax <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
group_by(STN_NISKIN, Station, Niskin,
Depth, DEPTH_BIN, Supergroup_simplified, CLUSTER) |>
summarise(SUM_SEQ = sum(MEAN_REPS_seq),
ASV_COUNT = n()) %>%
# Factoring and ordering
mutate(stn_order = factor(STN_NISKIN, levels = (stn_bykmeans)),
tax_order = factor(Supergroup_simplified, levels = (tax_simplified_list)),
cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
ggplot(aes(y = (stn_order), x = SUM_SEQ, fill = tax_order)) +
geom_bar(stat = "identity", position = "fill",
color = "grey20", width = 0.7, linewidth = 0.3) +
scale_fill_manual(values = tax_color) +
scale_y_discrete(position = "left") +
facet_grid(rows = vars(cluster_order), scales = "free", space = "free") +
scale_x_continuous(expand = c(0,0)) +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
strip.background = element_blank(),
strip.text = element_blank(),
axis.title = element_text(color = "black", size = 8),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 0, size = 5),
panel.grid.major = element_line(color = "grey70", linetype = "dashed")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Relative Abundance", y = "")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.bykmeans_tax
6.0.2 k-mean asv total
Total ASVs
# Sum by taxonomic groups
ASV_TOTAL <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
group_by(STN_NISKIN, Station, Niskin, CLUSTER) |>
summarise(ASV_COUNT = n()) %>%
# Factoring and ordering
mutate(stn_order = factor(STN_NISKIN, levels = (stn_bykmeans)),
cluster_order = factor(CLUSTER, levels = (CLUSTER_NUM))) %>%
ggplot(aes(y = stn_order, x = ASV_COUNT)) +
geom_bar(stat = "identity", position = "stack",
color = "black", fill = "grey40", width = 0.7, linewidth = 0.3) +
scale_y_discrete(position = "left") +
scale_x_continuous(expand = c(0,0)) +
facet_grid(rows = vars(cluster_order), scales = "free", space = "free") +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
strip.background = element_blank(),
strip.text = element_blank(),
axis.title = element_text(color = "black", size = 8),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 0, size = 5),
panel.grid.major = element_line(color = "grey70", linetype = "dashed")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Total ASVs", y = "")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.ASV_TOTAL
6.1 Plot Dendrogram compilation
heatmap_combined_vars +
(bykmeans_tax + theme(axis.text.y = element_text(color = "black", size = 8, hjust = 1, vjust = 0.5))) +
(ASV_TOTAL+ theme(axis.text.y = element_blank())) +
plot_layout(nrow = 1, widths = c(1, 0.3, 0.3), guides = "collect")
# ggsave("figures/heatmap-kmeans-all.svg", width = 9, height = 18, device = "svg", limitsize = FALSE)
# ggsave("figures/heatmap-kmeans-legend.svg", width = 9, height = 30, device = "svg", limitsize = FALSE)7 Taxonomy
Richness by depth, station, and feature.
head(table_wstn_wdepth)# A tibble: 6 × 34
Station Niskin Depth Latitude Longitude Pressure Date Time_UTC DATE_GMT
<int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <date>
1 1 5 18.6 28.8 89.5 18.7 7/30/20… 18:56:28 2023-07-30
2 1 10 2.48 28.8 89.5 2.50 7/30/20… 18:56:28 2023-07-30
3 2 1 353. 28.6 89.1 356. 7/30/20… 21:36:00 2023-07-30
4 2 3 269. 28.6 89.1 271. 7/30/20… 21:36:00 2023-07-30
5 2 6 150. 28.6 89.1 151. 7/30/20… 21:36:00 2023-07-30
6 2 8 24.2 28.6 89.1 24.3 7/30/20… 21:36:00 2023-07-30
# ℹ 25 more variables: TIME_GMT <chr>, datetime_cst <dttm>, SUNRISE <dttm>,
# SUNSET <dttm>, DAY_NIGHT <chr>, HR_OF_DAY <int>, stn <chr>,
# STN_ORDER <fct>, TRANSECT <fct>, DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>,
# STN_NISKIN <chr>, OilRig_Count <int>, MLD <dbl>, SAL_MAX <dbl>,
# O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>, STN_CATEGORY_4 <chr>,
# CLUSTER <fct>, WATER_MASS <chr>, SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>,
# DCM_THRESHOLD <chr>, O2_THRESHOLD <chr>tax_color <- c("#607080","#2f97f7","#2c5c1d","#6da65b", "grey20",
"#de6600", #Gyrista
"#ee429b", #dinos
"#1abc9c", # ciliates
"#23b521","#f5bfb0","#fea02f", "#f7d472",
"#d7b6d7","#460e57",
"#572f0e","#916643","#c95e06", "#dba272", "#e6d2b8", "#b09b10",
"#ad3d46", #Haptista
"#5e0810", "grey30", "#db9399","#93c47d","#ca7ade")
tax_simplified_list <- as.character(rev(sort(unique(asv_long_avg_wtax_clean $Supergroup_simplified))))
tax_simplified_list [1] "TSAR-Telonemia_X" "TSAR-Stramenopiles_X"
[3] "TSAR-Radiolaria" "TSAR-Perkinsea"
[5] "TSAR-NA" "TSAR-Gyrista"
[7] "TSAR-Dinoflagellata" "TSAR-Ciliophora"
[9] "TSAR-Chrompodellids" "TSAR-Cercozoa"
[11] "TSAR-Bigyra" "TSAR-Apicomplexa"
[13] "TSAR-Alveolata_X" "Provora"
[15] "Obazoa-Opisthokonta_X" "Obazoa-Metazoa"
[17] "Obazoa-Ichthyosporea" "Obazoa-Fungi"
[19] "Obazoa-Choanoflagellata" "Obazoa-Apusomonada_X"
[21] "Haptista" "Excavata"
[23] "Eukaryota_X" "Cryptista"
[25] "Archaeplastida" "Amoebozoa" names(tax_color) <- tax_simplified_list
names(tax_simplified_list) <- tax_color
# view(tax_simplified_list)Low taxonomic resolution should be at DIVISION level. Then to SUPERGROUP_CLASS level.
# Sum by taxonomic groups
four <- c("TSAR-Dinoflagellata", "TSAR-Radiolaria", "TSAR-Ciliophora", "TSAR-Gyrista")7.0.1 Plot sequence relative abundance
asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
group_by(Station, DEPTH_BIN, Supergroup, Supergroup_simplified) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq),
ASV_COUNT = n()) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
mutate(depth_bin_order = factor(DEPTH_BIN, levels = rev(depth_bin)),
tax_order = factor(Supergroup_simplified, levels = rev(tax_simplified_list))) |>
ggplot(aes(y = depth_bin_order, x = SUM_SEQ, fill = tax_order)) +
geom_bar(stat = "identity", position = "fill", color = "black", width = 0.9) +
scale_fill_manual(values = tax_color) +
scale_x_continuous(expand = c(0,0)) +
facet_grid(rows = vars(Station), scales = "free_y", space = "free", axis.labels = "all_x") +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black", face = "bold"),
strip.background = element_blank(),
strip.text.y = element_text(angle = 0, color = "black", face = "bold"),
axis.title = element_text(color = "black", face = "bold"),
axis.text.y = element_text(color = "black", face = "bold"),
axis.text.x = element_text(color = "black", face = "bold")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Sequence Relative Abundace", y = "Depth (m)")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.
7.0.2 Plot sequence relative abundance with depths
asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
group_by(Station, Depth, Supergroup_simplified) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq),
ASV_COUNT = n()) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
mutate(tax_order = factor(Supergroup_simplified, levels = rev(tax_simplified_list))) |>
ggplot(aes(y = as.factor(Depth), x = SUM_SEQ, fill = tax_order)) +
geom_bar(stat = "identity", position = "fill", color = "white", width = 0.9) +
scale_fill_manual(values = tax_color) +
# scale_y_reverse() +
scale_x_continuous(expand = c(0,0)) +
facet_grid(rows = vars(Station), scales = "free_y", space = "free", axis.labels = "all_x") +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black", face = "bold"),
strip.background = element_blank(),
strip.text.y = element_text(angle = 0, color = "black", face = "bold"),
axis.title = element_text(color = "black", face = "bold"),
axis.text.y = element_text(color = "black", face = "bold"),
axis.text.x = element_text(color = "black", face = "bold")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Sequence Relative Abundace", y = "Depth (m)")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.
Plot ASV richness by taxonomic groups
# tax_color
asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
group_by(DEPTH_BIN, Supergroup_simplified, Class) %>%
summarise(ASV_COUNT = n()) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "Eukaryota_X") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
mutate(depth_bin_order = factor(DEPTH_BIN, levels = rev(depth_bin)),
tax_order = factor(Supergroup_simplified, levels = rev(tax_simplified_list))) |>
ggplot(aes(y = depth_bin_order, x = Class, fill = tax_order, size = ASV_COUNT)) +
geom_point(shape = 21, color = "grey10", alpha = 0.7, stroke = 0.5) +
scale_fill_manual(values = tax_color) +
scale_size_continuous(range = c(1,10), breaks = c(1, 10, 50, 100, 500, 1000, 2000)) +
facet_grid(cols = vars(tax_order), scales = "free_x", space = "free_x", axis.labels = "all_x") +
theme_classic() + theme(
legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(color = "black", face = "bold"),
strip.background = element_blank(),
panel.grid.major = element_line(color = "grey90", linetype = "dashed"),
strip.text.y = element_text(angle = 90, hjust = 0, vjust = 0.5, size = 8, color = "black", face = "bold"),
strip.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5, size = 8, color = "black", face = "bold"),
axis.title = element_text(color = "black", face = "bold"),
axis.text.y = element_text(color = "black", face = "bold"),
axis.text.x = element_text(color = "black", face = "bold", angle = 90,
hjust = 1, vjust = 0, size = 8)) +
guides(fill = "none",
size = guide_legend(nrow = 1, shape = 21, color = "black", fill = "black")) +
labs(x = "", y = "Depth (m)")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.Warning in guide_legend(nrow = 1, shape = 21, color = "black", fill = "black"): Arguments in `...` must be used.
✖ Problematic arguments:
• shape = 21
• color = "black"
• fill = "black"
ℹ Did you misspell an argument name?
# ggsave("figures/tax-dotplot.svg", width = 22, height = 5, device = "svg", limitsize = FALSE)8 K-means plot with taxonomy
# head(table_supp1)
kmeans_depth <- table_supp1 %>% ungroup() %>%
select(CLUSTER, STN_NISKIN, STN_CATEGORY_4, ends_with("_THRESHOLD"), DAY_NIGHT, DEPTH_BIN, WATER_MASS) %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
distinct() %>%
ggplot(aes(y = (as.factor((CLUSTER))))) +
geom_bar(stat = "count", aes(fill = depth_order), color = "white", width = 0.6) +
scale_fill_manual(values = blues, guide = guide_legend(reverse = TRUE, order = 2)) +
scale_x_continuous(expand = c(0,0)) +
theme_classic() +
theme(axis.text = element_text(color="black", size=12),
axis.text.y = element_blank(),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
plot.margin = margin(b = 0, unit = "pt")) +
labs(x = "Number of samples", y = "")kmeans_category <- table_supp1 %>% ungroup() %>%
select(CLUSTER, STN_NISKIN, STN_CATEGORY_4, ends_with("_THRESHOLD"), DAY_NIGHT, DEPTH_BIN, WATER_MASS) %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
distinct() %>%
ggplot(aes(y = (as.factor((CLUSTER))))) +
geom_bar(stat = "count", aes(fill = STN_CATEGORY_4), color = "white", width = 0.6) +
scale_fill_manual(values = c("grey5", "grey20", "grey50", "grey80")) +
scale_x_continuous(expand = c(0,0)) +
theme_classic() +
theme(axis.text = element_text(color="black", size=12),
# axis.text.y = element_blank(),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
plot.margin = margin(b = 0, unit = "pt")) +
labs(x = "Number of samples", y = "k-means cluster")kmeans_water <- table_supp1 %>% ungroup() %>%
select(CLUSTER, STN_NISKIN, STN_CATEGORY_4, ends_with("_THRESHOLD"), DAY_NIGHT, DEPTH_BIN, WATER_MASS) %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
mutate(WATER_MASS_ORDER = factor(WATER_MASS, levels = water_mass_order)) %>%
distinct() %>%
ggplot(aes(y = (as.factor((CLUSTER))))) +
geom_bar(stat = "count", aes(fill = WATER_MASS_ORDER), color = "white", width = 0.6) +
scale_fill_manual(values = water_mass_color) +
scale_x_continuous(expand = c(0,0)) +
theme_classic() +
theme(axis.text.x = element_text(color="black", size=12),
axis.text.y = element_blank(),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
plot.margin = margin(b = 0, unit = "pt")) +
labs(x = "Number of samples", y = "")kmeans_tax <- asv_long_avg_wtax_clean %>%
left_join(table_supp1 %>%
select(STN_NISKIN, CLUSTER) %>%
distinct()) %>%
group_by(CLUSTER, Supergroup_simplified) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq)) %>%
filter(Supergroup_simplified != "Eukaryota") %>%
filter(Supergroup_simplified != "TSAR-NA") %>%
filter(Supergroup_simplified != "Obazoa-Metazoa") %>%
mutate(tax_order = factor(Supergroup_simplified, levels = rev(tax_simplified_list))) |>
ggplot(aes(y = as.factor(CLUSTER), x = SUM_SEQ, fill = tax_order)) +
geom_bar(stat = "identity", position = "fill", color = "white", width = 0.6) +
scale_fill_manual(values = tax_color) +
# scale_y_reverse() +
scale_x_continuous(expand = c(0,0)) +
theme_classic() +
theme(axis.text.x = element_text(color="black", size=12),
axis.text.y = element_blank(),
legend.title = element_blank(),
panel.grid.major = element_line(color = "grey80", linetype = "dashed"),
axis.title = element_text(color="black", size= 12),
legend.text = element_text(color = "black", size = 8, face = "bold"),
plot.margin = margin(b = 0, unit = "pt")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Sequence Relative Abundace", y = "")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.kmeans_category + kmeans_depth + kmeans_water + kmeans_tax + plot_layout(nrow = 1, guides = "collect")
ggsave("figures/kmeans-barplot-composition.svg", width = 14, height = 4, device = "svg", limitsize = FALSE)9 Taxa by depth - curated
9.1 Curate specific taxa datasets
stn_long <- table_supp %>% select(DIST_OUTFLOW, Station) %>% distinct() %>%
mutate(Station = as.character(Station))9.1.1 Dinos
dinos_subset <- c("Dinoflagellata-Dinophyceae","Dinoflagellata-Syndiniales")
seq_total <- asv_long_avg_wtax_clean %>%
# filter(Supergroup_simplified == "TSAR-Dinoflagellata") %>%
filter(SUPERGROUP_CLASS %in% dinos_subset) %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
dinos_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
# filter(Supergroup_simplified == "TSAR-Dinoflagellata") %>%
filter(FeatureID %in% keep_asvs) %>%
# left_join(table_wstn_wdepth) %>%
unite(TAXA, Class, Order, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
grepl("Labyrinthulomycetes", TAXA) ~ "Labyrinthulomycetes",
grepl("Pseudodendromonadales", TAXA) ~ "Pseudodendromonadales",
grepl("Dinophyceae_X", TAXA) ~ "Dinophyceae",
grepl("Dinophyta_X", TAXA) ~ "Dinophyta",
TRUE ~ TAXA
)) %>%
filter(!is.na(Order)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, Depth, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`# head(dinos_curated)
#
tmp <- dinos_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 8, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
dinos_curated_5 <- dinos_curated %>%
filter(TAXA %in% top_5)mat_tax_df <- dinos_curated %>% ungroup() %>%
# Save taxa id curated and ASV ID
unite(sample, STN_NISKIN, DEPTH_BIN, sep = ";") %>%
group_by(TAXA, sample) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq)) %>%
pivot_wider(names_from = TAXA, values_from = SUM_SEQ, values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "sample") %>%
as.matrix `summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead. # colnames(mat_tmp)
clr_tmp <- vegan::decostand(mat_tax_df, MARGIN = 2, method = "rclr")
##
colnames(clr_tmp) <- colnames(mat_tax_df)
rownames(clr_tmp) <- rownames(mat_tax_df)
##
data.frame(clr_tmp) %>%
rownames_to_column(var = "sample") %>%
pivot_longer(cols = -c(sample), names_to = "TAXA", values_to = "CLR") %>%
separate(sample, into = c("STN_NISKIN", "DEPTH_BIN"), sep = ";") %>%
mutate(DEPTH_BIN_ORDER = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = DEPTH_BIN_ORDER, x = TAXA, fill = CLR)) +
geom_tile(color = "black") +
scale_fill_fermenter(type = "div",
palette = "RdBu",
limits = c(-4, 4)) +
coord_fixed(ratio = 1) +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 90, size = 8,
hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = "black", size = 8),
plot.title = element_text(color = "black", size = 8, face = "bold"),
legend.position = "right") +
labs(x = "", y = "", title = "TSAR-Dinoflagellata")
9.1.2 Rad
# tax_simplified_list
rad_subset <- c("Radiolaria-Polycystinea","Radiolaria-Acantharea","Radiolaria-RAD-A","Radiolaria-RAD-B","Radiolaria-RAD-C")seq_total <- asv_long_avg_wtax_clean %>%
# filter(Supergroup_simplified == "TSAR-Radiolaria") %>%
filter(SUPERGROUP_CLASS %in% rad_subset) %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
rad_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(FeatureID %in% keep_asvs) %>%
unite(TAXA, Class, Order, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
grepl("Acantharea", TAXA) ~ "Acantharea",
grepl("RAD_A", TAXA) ~ "RAD_A",
grepl("RAD_B", TAXA) ~ "RAD_B",
grepl("RAD_C", TAXA) ~ "RAD_C",
grepl("Radiolaria_XX", TAXA) ~ "Radiolaria",
TRUE ~ TAXA
)) %>%
filter(!is.na(Order)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, Depth, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`#
tmp <- rad_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 5, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
rad_curated_5 <- rad_curated %>%
filter(TAXA %in% top_5)mat_tax_df_surface <- rad_curated_5 %>% ungroup() %>%
filter(DEPTH_BIN == "< 5m") %>%
unite(sample, Station, TRANSECT, sep = ";") %>%
group_by(TAXA, sample) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq)) %>%
pivot_wider(names_from = TAXA, values_from = SUM_SEQ, values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "sample") %>%
as.matrix `summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead. # colnames(mat_tmp)
clr_tmp <- vegan::decostand(mat_tax_df_surface, MARGIN = 2, method = "rclr")
##
colnames(clr_tmp) <- colnames(mat_tax_df_surface)
rownames(clr_tmp) <- rownames(mat_tax_df_surface)
##
data.frame(clr_tmp) %>%
rownames_to_column(var = "sample") %>%
pivot_longer(cols = -c(sample), names_to = "TAXA", values_to = "CLR") %>%
separate(sample, into = c("Station", "Transect"), sep = ";") %>%
left_join(stn_long) %>%
mutate(Station_Order = factor(Station, levels = (stn_order_shore_offshore))) %>%
ggplot(aes(x = (DIST_OUTFLOW/1000), y = CLR, shape = Transect)) +
geom_hline(yintercept = 0) +
geom_line(aes(group = Transect)) +
geom_jitter(color = "black", fill = "#ee429b", size = 2) +
scale_shape_manual(values = c(21, 22, 23)) +
facet_grid(rows = vars(TAXA), scales = "free_x") +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 0, size = 8,
hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(color = "black", angle = 0, size = 8,
hjust = 1, vjust = 0.5),
plot.title = element_text(color = "black", size = 8, face = "bold"),
strip.text.y = element_text(color = "black", angle = 0, size = 8,
hjust = 0, vjust = 0.5),
strip.background = element_blank(),
panel.grid.major = element_line(color = "grey70", linetype = "dashed"),
legend.position = "left",
aspect.ratio = 1/2) +
labs(x = "km", y = "Center log-ratio", title = "")Joining with `by = join_by(Station)`
9.1.3 Ciliate
other_ciliates <- c("Oligohymenophorea;Oligohymenophorea_X", "Litostomatea;Haptoria_6", "Heterotrichea;Heterotrichea_X", "Spirotrichea;Hypotrichia", "Ciliophora_X;Ciliophora_XX")
ciliate_subset <- c("Ciliophora-Spirotrichea", "Ciliophora-Phyllopharyngea", "Ciliophora-Oligohymenophorea", "Ciliophora-CONThreeP")
# Curate asvs to keep
seq_total <- asv_long_avg_wtax_clean %>%
# filter(Supergroup_simplified == "TSAR-Ciliophora") %>%
filter(SUPERGROUP_CLASS %in% ciliate_subset) %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
ciliate_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
# filter(Supergroup_simplified == "TSAR-Ciliophora") %>%
filter(FeatureID %in% keep_asvs) %>%
# left_join(table_wstn_wdepth) %>%
unite(TAXA, Class, Order, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
(TAXA %in% other_ciliates) ~ "Ciliates unannotated",
grepl("CONTH_", TAXA) ~ "CONThreeP unannotated",
grepl("CONThreeP_X", TAXA) ~ "CONThreeP unannotated",
(Class == "Discotrichidae") ~ "CONThreeP;Discotrichidae",
grepl("Phyllopharyngea;Cyrtophoria", TAXA) ~ "Phyllopharyngea;Cyrtophoria",
grepl("Spirotrichea;Choreotrichida", TAXA) ~ "Spirotrichea;Choreotrichida",
grepl("Litostomatea;Litostomatea_X", TAXA) ~ "Litostomatea",
grepl("Prostomatea_1;Prostomatea_1_X", TAXA) ~ "Prostomatea",
grepl("Colpodea_X", TAXA) ~ "Colpodea",
TRUE ~ TAXA
)) %>%
filter(!is.na(Order)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, Depth, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`head(ciliate_curated)# A tibble: 6 × 8
FeatureID TAXA Station STN_NISKIN Depth DEPTH_BIN TRANSECT MEAN_REPS_seq
<chr> <chr> <int> <chr> <dbl> <chr> <fct> <dbl>
1 0129873aba97… Spir… 11 S11_N11 3.06e1 25-60m transec… 289
2 0129873aba97… Spir… 11 S11_N12 1.95e0 < 5m transec… 5
3 0129873aba97… Spir… 11 S11_N6 6.01e2 400-800m transec… 9
4 0129873aba97… Spir… 12 S12_N11 2.50e1 25-60m transec… 363
5 0129873aba97… Spir… 12 S12_N12 1.78e0 < 5m transec… 215
6 0129873aba97… Spir… 12 S12_N5 1.40e3 1200-200… transec… 5tmp <- ciliate_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 5, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
ciliate_curated_5 <- ciliate_curated %>%
filter(TAXA %in% top_5)9.1.4 Gyrista
gyrista_less <- c("Pelagophyceae unannotated", "Pelagophyceae;NA","Coscinodiscophyceae;Coscinodiscales", "Bacillariophyceae;Licmophorales", "Eustigmatophyceae;Eustigmatophyceae_X", "Pirsoniales;Pirsoniales_X","Peronosporomycetes;Peronosporomycetes_X", "Bacillariophyceae;Cymbellales")
# Curate asvs to keep
seq_total <- asv_long_avg_wtax_clean %>%
filter(Supergroup_simplified == "TSAR-Gyrista") %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
gyrista_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(Supergroup_simplified == "TSAR-Gyrista") %>%
filter(FeatureID %in% keep_asvs) %>%
# left_join(table_wstn_wdepth) %>%
unite(TAXA, Class, Order, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
is.na(Class) ~ "Gyrista;Unannotated",
(TAXA %in% gyrista_less) ~ "Gyrista;Unannotated",
grepl(";NA", TAXA) ~ paste(Class, "Unannotated", sep = ";"),
(TAXA == "Gyrista_X;Gyrista_XX") ~ "Gyrista;Unannotated",
grepl("Dictyochophyceae", TAXA) ~ "Dictyochophyceae",
grepl("Chrysophyceae_Clade-EC2", TAXA) ~ "Chrysophyceae;Clade-EC2",
grepl("Bacillariophyceae;Bacillariophyceae_X", TAXA) ~ "Bacillariophyceae;Unannotated",
grepl("Pelagophyceae", TAXA) ~ "Pelagophyceae;Pelagomonadales",
grepl("MOCH-", TAXA) ~ "Gyrista;MOCH clades 2-5",
(TAXA == "Mediophyceae;Mediophyceae_X" | TAXA == "Mediophyceae;NA") ~ "Mediophyceae;Unannotated",
TRUE ~ TAXA
)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, Depth, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`head(gyrista_curated)# A tibble: 6 × 8
FeatureID TAXA Station STN_NISKIN Depth DEPTH_BIN TRANSECT MEAN_REPS_seq
<chr> <chr> <int> <chr> <dbl> <chr> <fct> <dbl>
1 006f5664d2538… Chry… 11 S11_N11 30.6 25-60m transec… 174.
2 006f5664d2538… Chry… 12 S12_N11 25.0 25-60m transec… 468.
3 006f5664d2538… Chry… 12 S12_N12 1.78 < 5m transec… 64
4 006f5664d2538… Chry… 14 S14_N12 2.90 < 5m transec… 65
5 006f5664d2538… Chry… 15 S15_N12 2.82 < 5m transec… 26
6 006f5664d2538… Chry… 2 S2_N11 2.9 < 5m transec… 28.8tmp <- gyrista_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 5, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
gyrista_curated_5 <- gyrista_curated %>%
filter(TAXA %in% top_5)9.1.5 Bigyra
# Curate asvs to keep
seq_total <- asv_long_avg_wtax_clean %>%
filter(Supergroup_simplified == "TSAR-Bigyra") %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
bigyra_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(Supergroup_simplified == "TSAR-Bigyra") %>%
filter(FeatureID %in% keep_asvs) %>%
unite(TAXA, Order, Family, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
grepl("Labyrinthulomycetes", TAXA) ~ "Labyrinthulomycetes",
grepl("Pseudodendromonadales", TAXA) ~ "Pseudodendromonadales",
TRUE ~ TAXA
)) %>%
filter(!is.na(Order)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`tmp <- bigyra_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 5, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
bigyra_curated_5 <- bigyra_curated %>%
filter(TAXA %in% top_5)9.1.6 Haptista
# Curate asvs to keep
seq_total <- asv_long_avg_wtax_clean %>%
filter(Supergroup_simplified == "Haptista") %>%
group_by(FeatureID) %>%
summarise(SAMPLE_COUNT = n_distinct(STN_NISKIN),
SEQ_SUM = sum(MEAN_REPS_seq)) %>%
filter(SAMPLE_COUNT >= 9)
#
keep_asvs <- as.character(unique(seq_total$FeatureID))
#
haptista_curated <- asv_long_avg_wtax_clean %>%
left_join(table_wstn_wdepth) %>%
filter(Supergroup_simplified == "Haptista") %>%
filter(FeatureID %in% keep_asvs) %>%
unite(TAXA, Class, Order, sep = ";", remove = FALSE) %>%
mutate(TAXA = case_when(
grepl("Pterocystida", TAXA) ~ "Pterocystida",
grepl("Haptophyta_Clade_", TAXA) ~ "Haptophyta Clades-3/4",
TRUE ~ TAXA
)) %>%
filter(!is.na(Order)) %>%
select(FeatureID, TAXA, Station, STN_NISKIN, DEPTH_BIN, TRANSECT, MEAN_REPS_seq) %>% distinct()Joining with `by = join_by(STN_NISKIN)`tmp <- haptista_curated %>%
group_by(TAXA) %>%
summarise(SUM = sum(MEAN_REPS_seq)) %>%
slice_max(n = 5, order_by = SUM)
top_5 <- as.character(unique(tmp$TAXA))
haptista_curated_5 <- haptista_curated %>%
filter(TAXA %in% top_5)9.2 Taxa by cluster
Create tree plots for key taxa
dino_colors <- c("#db1f7d", "#d1a9bd", "#8a1751", "#540c30",
"#4d917b", "#89c977",
"#e8912e","#d9bfa3","#aba250", "#cc9e6a","#436e16", "#a34e27")
# names(dino_colors) <- tax_dino_order
tree_dino <- dinos_curated %>% ungroup() %>%
left_join((table_wstn_wdepth %>% select(STN_NISKIN, CLUSTER) %>% distinct())) %>%
# filter(Depth >= 200) %>%
group_by(TAXA, CLUSTER) %>%
summarise(SUM = sum(MEAN_REPS_seq),
COUNT = n()) %>%
separate(TAXA, into = c("GROUP", "SUBGROUP"), sep = ";", remove = FALSE) %>%
mutate(SUBGROUP = str_replace_na(SUBGROUP, "Other")) %>%
ggplot(aes(area = SUM, fill = SUBGROUP,
# label = SUBGROUP,
subgroup = GROUP,
subgroup2 = TAXA)) +
geom_treemap() +
scale_fill_manual(values = dino_colors) +
facet_grid(cols = vars(CLUSTER)) +
geom_treemap_subgroup_border(colour = "white", size = 3) +
geom_treemap_subgroup2_border(colour = "white", size = 1) +
# geom_treemap_subgroup2_text(colour = "white", alpha = 0.8, fontface = "bold") +
theme_minimal() +
theme(strip.text = element_text(color = "black", face = "bold",
hjust = 0, vjust = 0),
legend.title = element_blank())Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.Warning: Expected 2 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6,
7, 8, 9].rad_colors <- c("#805edb", "#afe3cd", "#164531", "#e3e340", "#c4c49b", "#5b6940", "#648bb3")
# names(rad_colors) <- tax_rad_order
tree_rad <- rad_curated %>% ungroup() %>%
left_join((table_wstn_wdepth %>% select(STN_NISKIN, CLUSTER) %>% distinct())) %>%
# filter(Depth >= 200) %>%
group_by(TAXA, CLUSTER) %>%
summarise(SUM = sum(MEAN_REPS_seq),
COUNT = n()) %>%
separate(TAXA, into = c("GROUP", "SUBGROUP"), sep = ";", remove = FALSE) %>%
mutate(SUBGROUP = str_replace_na(SUBGROUP, "Other")) %>%
ggplot(aes(area = SUM, fill = SUBGROUP,
# label = SUBGROUP,
subgroup = GROUP,
subgroup2 = TAXA)) +
geom_treemap() +
scale_fill_manual(values = rad_colors) +
facet_grid(cols = vars(CLUSTER)) +
geom_treemap_subgroup_border(colour = "white", size = 3) +
geom_treemap_subgroup2_border(colour = "white", size = 1) +
# geom_treemap_subgroup2_text(colour = "white", alpha = 0.8, fontface = "bold") +
theme_minimal() +
theme(strip.text = element_text(color = "black", face = "bold",
hjust = 0, vjust = 0),
legend.title = element_blank())Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.Warning: Expected 2 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6,
7, 8, 9].gyrista_colors <- c("#93c47d", "#de5472","#7db7c4", "#a27dc4",
"#d65c1a", "#c98d6d", "#662a0a", "#805944", "#bd5f17","#a3690b",
"#d93529", "#736b6b","#d4cf44",
"#225706","#8ce663", "#38a605",
"#0ba7d6","#1836cc", "#6abbd4", "#5669c4","#97b8c2","#0d3142",
"#5cd1b8",
"#d596e3", "#b82fd6", "#a10b83","#a85b99")
# names(gyrista_colors) <- tax_gyrista_order
tree_gyrista <- gyrista_curated %>% ungroup() %>%
left_join((table_wstn_wdepth %>% select(STN_NISKIN, CLUSTER) %>% distinct())) %>%
# filter(Depth >= 200) %>%
group_by(TAXA, CLUSTER) %>%
summarise(SUM = sum(MEAN_REPS_seq),
COUNT = n()) %>%
separate(TAXA, into = c("GROUP", "SUBGROUP"), sep = ";", remove = FALSE) %>%
mutate(SUBGROUP = str_replace_na(SUBGROUP, "Other")) %>%
ggplot(aes(area = SUM, fill = SUBGROUP,
# label = SUBGROUP,
subgroup = GROUP,
subgroup2 = TAXA)) +
geom_treemap() +
scale_fill_manual(values = gyrista_colors) +
facet_grid(cols = vars(CLUSTER)) +
geom_treemap_subgroup_border(colour = "white", size = 3) +
geom_treemap_subgroup2_border(colour = "white", size = 1) +
# geom_treemap_subgroup2_text(colour = "white", alpha = 0.8, fontface = "bold") +
theme_minimal() +
theme(strip.text = element_text(color = "black", face = "bold",
hjust = 0, vjust = 0),
legend.title = element_blank())Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.Warning: Expected 2 pieces. Missing pieces filled with `NA` in 8 rows [91, 92, 93, 94,
95, 96, 97, 98].ciliate_colors <- c("#f5bfb0", "#ed7e5f", "#d13206",
"#cf9229", "#69552a",
"#cfcf57", "#84c2a5","#604a70","#d1d106",
"#accf97", "#7ade3c", "#2edb8a", "#02703d",
"#3899a8", "#445cad", "#5402e0", "#916dcf", "#604a70", "#7b857e")
# names(ciliate_colors) <- tax_ciliate_order
tree_ciliate <- ciliate_curated %>% ungroup() %>%
left_join((table_wstn_wdepth %>% select(STN_NISKIN, CLUSTER) %>% distinct())) %>%
# filter(Depth >= 200) %>%
group_by(TAXA, CLUSTER) %>%
summarise(SUM = sum(MEAN_REPS_seq),
COUNT = n()) %>%
separate(TAXA, into = c("GROUP", "SUBGROUP"), sep = ";", remove = FALSE) %>%
mutate(SUBGROUP = str_replace_na(SUBGROUP, "Other")) %>%
ggplot(aes(area = SUM, fill = SUBGROUP,
# label = SUBGROUP,
subgroup = GROUP,
subgroup2 = TAXA)) +
geom_treemap() +
scale_fill_manual(values = ciliate_colors) +
facet_grid(cols = vars(CLUSTER)) +
geom_treemap_subgroup_border(colour = "white", size = 3) +
geom_treemap_subgroup2_border(colour = "white", size = 1) +
# geom_treemap_subgroup2_text(colour = "white", alpha = 0.8, fontface = "bold") +
theme_minimal() +
theme(strip.text = element_text(color = "black", face = "bold",
hjust = 0, vjust = 0),
legend.title = element_blank())Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.Warning: Expected 2 pieces. Missing pieces filled with `NA` in 16 rows [1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16].9.2.1 Compile tree plot
tree_dino + tree_rad + tree_gyrista + tree_ciliate + plot_layout(ncol = 1)
9.3 Function CLR relationship
# # requires:
# # depth_bin
# # stn_order_shore_offshore
# # stn_long
toptaxa_depth_clr <- function(DF, TITLE){
# Get matrix for depth profile
mat_tax_df <- DF %>% ungroup() %>%
unite(sample, STN_NISKIN, DEPTH_BIN, sep = ";") %>%
group_by(TAXA, sample) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq)) %>%
pivot_wider(names_from = TAXA, values_from = SUM_SEQ,
values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "sample") %>%
as.matrix
# colnames(mat_tmp)
clr_depth <- vegan::decostand(mat_tax_df, MARGIN = 1, method = "rclr")
##
colnames(clr_depth) <- colnames(mat_tax_df)
rownames(clr_depth) <- rownames(mat_tax_df)
#
(data.frame(clr_depth) %>%
rownames_to_column(var = "sample") %>%
pivot_longer(cols = -c(sample), names_to = "TAXA", values_to = "CLR") %>%
separate(sample, into = c("STN_NISKIN", "DEPTH_BIN"), sep = ";") %>%
mutate(DEPTH_BIN_ORDER = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = DEPTH_BIN_ORDER, x = TAXA, fill = CLR)) +
geom_tile(color = "black") +
scale_fill_steps2(limits = c(-2.5,2.5),
show.limits = TRUE,
low = "#273fa1",
mid = "#ffffff",
high = "#a12727") +
# scale_fill_gradientn(
# colors = c("blue", "dodgerblue", "white", "orange", "red"),
# rescaler = ~ scales::rescale_mid(.x, mid = 0)
# ) +
# scale_fill_gradient2(
# low = LO,
# low = "#3843e0",
# mid = MID,
# mid = "#ede4e4",
# high = HI,
# high = "#d12b1f",
# midpoint = 0,
# na.value = "grey50",
# aesthetics = "fill",
# limits = c(-4, 4)
# ) +
coord_fixed(ratio = 1) +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 90, size = 8,
hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = "black", size = 8),
plot.title = element_text(color = "black", size = 8, face = "bold"),
legend.position = "right") +
labs(x = "", y = "", title = TITLE))
}# requires:
# depth_bin
# stn_order_shore_offshore
# stn_long
toptaxa_surf_clr <- function(DF, TITLE){
mat_tax_df_surface <- DF %>% ungroup() %>%
filter(DEPTH_BIN == "< 5m") %>%
unite(sample, Station, TRANSECT, sep = ";") %>%
group_by(TAXA, sample) %>%
summarise(SUM_SEQ = sum(MEAN_REPS_seq)) %>%
pivot_wider(names_from = TAXA, values_from = SUM_SEQ,
values_fn = mean, values_fill = 0) %>%
column_to_rownames(var = "sample") %>%
as.matrix
# mat_tax_df_surface
clr_surf <- vegan::decostand(mat_tax_df_surface, MARGIN = 1, method = "rclr")
##
colnames(clr_surf) <- colnames(mat_tax_df_surface)
rownames(clr_surf) <- rownames(mat_tax_df_surface)
##
(data.frame(clr_surf) %>%
rownames_to_column(var = "sample") %>%
pivot_longer(cols = -c(sample), names_to = "TAXA", values_to = "CLR") %>%
separate(sample, into = c("Station", "Transect"), sep = ";") %>%
left_join(stn_long) %>%
mutate(Station_Order = factor(Station, levels = rev(stn_order_shore_offshore))) %>%
# ggplot(aes(y = Station_Order, x = TAXA, fill = CLR)) +
# geom_tile(color = "black") +
# scale_fill_steps2(limits = c(-3,3), show.limits = TRUE) +
ggplot(aes(x = (DIST_OUTFLOW/1000), y = CLR, fill = TAXA)) +
geom_hline(yintercept = 0) +
# geom_line(aes(group = Transect, linetype = Transect)) +
geom_jitter(color = "white", size = 2, shape = 21) +
# scale_shape_manual(values = c(21, 22, 23)) +
# facet_grid(rows = vars(TAXA), scales = "free_x") +
# coord_fixed(ratio = 1) +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 90, size = 8,
hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = "black", size = 8),
plot.title = element_text(color = "black", size = 8, face = "bold"),
legend.position = "right") +
labs(x = "", y = "", title = TITLE))
}9.3.1 Dinos
toptaxa_surf_clr(dinos_curated_5, "Dinoflagellate") +
toptaxa_depth_clr(dinos_curated_5, "Dinoflagellates") +
plot_layout(nrow = 1, heights = c(1, 0.9))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.3.2 Rad
toptaxa_surf_clr(rad_curated_5, "Rhizaria") +
toptaxa_depth_clr(rad_curated_5, "Rhizaria") +
plot_layout(nrow = 1, heights = c(1, 0.6))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.3.3 Ciliate
toptaxa_surf_clr(ciliate_curated_5, "Ciliates") +
toptaxa_depth_clr(ciliate_curated_5, "Ciliates") +
plot_layout(nrow = 1, heights = c(1, 0.6))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.3.4 Gyrista
toptaxa_surf_clr(gyrista_curated_5, "Gyrista") +
toptaxa_depth_clr(gyrista_curated_5, "Gyrista") +
plot_layout(nrow = 1, heights = c(1, 0.6))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.3.5 Bigyra
toptaxa_surf_clr(bigyra_curated_5, "Bigyra") +
toptaxa_depth_clr(bigyra_curated_5, "Bigyra") +
plot_layout(nrow = 1, heights = c(1, 0.6))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.3.6 Haptista
toptaxa_surf_clr(haptista_curated_5, "Haptista") +
toptaxa_depth_clr(haptista_curated_5, "Haptista") +
plot_layout(nrow = 1, heights = c(1, 0.6))`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
9.4 Compile main text - tax specific
toptaxa_surf_clr(dinos_curated_5, "Dinoflagellate") +
toptaxa_depth_clr(dinos_curated_5, "Dinoflagellates") +
toptaxa_surf_clr(ciliate_curated_5, "Ciliates") +
toptaxa_depth_clr(ciliate_curated_5, "Ciliates") +
toptaxa_surf_clr(rad_curated_5, "Rhizaria") +
toptaxa_depth_clr(rad_curated_5, "Rhizaria") +
toptaxa_surf_clr(gyrista_curated_5, "Gyrista") +
toptaxa_depth_clr(gyrista_curated_5, "Gyrista") +
plot_layout(nrow = 2, ncol = 4, guides = "collect")`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
Joining with `by = join_by(Station)`
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
toptaxa_depth_clr(dinos_curated_5, "Dinoflagellates") +
toptaxa_depth_clr(rad_curated_5, "Rhizaria") +
toptaxa_depth_clr(gyrista_curated_5, "Gyrista") +
toptaxa_depth_clr(ciliate_curated_5, "Ciliates") +
plot_layout(nrow = 1, ncol = 4, guides = "collect")`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by TAXA and sample.
ℹ Output is grouped by TAXA.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(TAXA, sample))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
# ggsave("figures/depth-clr-main-four.svg", width = 12, height = 8, device = "svg", limitsize = FALSE)10 RDA analysis
RDA reveals relationships among the sites and the environmental parameters. We can explore the relationship among all ASV signatures by site, and then model the effect that each environmental parameter has on it. Null H = variances across groups are equal p-value of < 0.05 means there is a significant difference in variance among groups.
# library(car)
# Levene's test with one independent variable
# leveneTest(weight ~ group, data = PlantGrowth)
# Levene's test with multiple independent variables
# leveneTest(len ~ supp*dose, data = ToothGrowth)set.seed(1234)
head(table_wstn_wdepth)# A tibble: 6 × 34
Station Niskin Depth Latitude Longitude Pressure Date Time_UTC DATE_GMT
<int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <date>
1 1 5 18.6 28.8 89.5 18.7 7/30/20… 18:56:28 2023-07-30
2 1 10 2.48 28.8 89.5 2.50 7/30/20… 18:56:28 2023-07-30
3 2 1 353. 28.6 89.1 356. 7/30/20… 21:36:00 2023-07-30
4 2 3 269. 28.6 89.1 271. 7/30/20… 21:36:00 2023-07-30
5 2 6 150. 28.6 89.1 151. 7/30/20… 21:36:00 2023-07-30
6 2 8 24.2 28.6 89.1 24.3 7/30/20… 21:36:00 2023-07-30
# ℹ 25 more variables: TIME_GMT <chr>, datetime_cst <dttm>, SUNRISE <dttm>,
# SUNSET <dttm>, DAY_NIGHT <chr>, HR_OF_DAY <int>, stn <chr>,
# STN_ORDER <fct>, TRANSECT <fct>, DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>,
# STN_NISKIN <chr>, OilRig_Count <int>, MLD <dbl>, SAL_MAX <dbl>,
# O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>, STN_CATEGORY_4 <chr>,
# CLUSTER <fct>, WATER_MASS <chr>, SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>,
# DCM_THRESHOLD <chr>, O2_THRESHOLD <chr># head(table_wfeature)
head(table_supp)# A tibble: 6 × 44
Station Niskin Depth Latitude Longitude Date Time_UTC DATE_GMT TIME_GMT
<int> <int> <dbl> <dbl> <dbl> <chr> <chr> <date> <chr>
1 1 5 18.6 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
2 1 10 2.48 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
3 2 1 353. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
4 2 3 269. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
5 2 6 150. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
6 2 8 24.2 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
# ℹ 35 more variables: DAY_NIGHT <chr>, STN_ORDER <fct>, TRANSECT <fct>,
# DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>, STN_NISKIN <chr>, OilRig_Count <int>,
# MLD <dbl>, SAL_MAX <dbl>, O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>,
# STN_CATEGORY_4 <chr>, CLUSTER <fct>, Watermass <chr>, WATER_MASS <chr>,
# SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>, DCM_THRESHOLD <chr>,
# O2_THRESHOLD <chr>, DIC <dbl>, NH4 <dbl>, NO2 <dbl>, NO3 <dbl>,
# Oxygen_CTD <dbl>, PO4 <dbl>, SIL <dbl>, Salinity <dbl>, TA <dbl>, …10.1 All
Isolate categorical vs. numeric metadata. For numeric data, create a normalized scale.
head(table_wstn_wdepth)# A tibble: 6 × 34
Station Niskin Depth Latitude Longitude Pressure Date Time_UTC DATE_GMT
<int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <date>
1 1 5 18.6 28.8 89.5 18.7 7/30/20… 18:56:28 2023-07-30
2 1 10 2.48 28.8 89.5 2.50 7/30/20… 18:56:28 2023-07-30
3 2 1 353. 28.6 89.1 356. 7/30/20… 21:36:00 2023-07-30
4 2 3 269. 28.6 89.1 271. 7/30/20… 21:36:00 2023-07-30
5 2 6 150. 28.6 89.1 151. 7/30/20… 21:36:00 2023-07-30
6 2 8 24.2 28.6 89.1 24.3 7/30/20… 21:36:00 2023-07-30
# ℹ 25 more variables: TIME_GMT <chr>, datetime_cst <dttm>, SUNRISE <dttm>,
# SUNSET <dttm>, DAY_NIGHT <chr>, HR_OF_DAY <int>, stn <chr>,
# STN_ORDER <fct>, TRANSECT <fct>, DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>,
# STN_NISKIN <chr>, OilRig_Count <int>, MLD <dbl>, SAL_MAX <dbl>,
# O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>, STN_CATEGORY_4 <chr>,
# CLUSTER <fct>, WATER_MASS <chr>, SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>,
# DCM_THRESHOLD <chr>, O2_THRESHOLD <chr>head(table_supp)# A tibble: 6 × 44
Station Niskin Depth Latitude Longitude Date Time_UTC DATE_GMT TIME_GMT
<int> <int> <dbl> <dbl> <dbl> <chr> <chr> <date> <chr>
1 1 5 18.6 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
2 1 10 2.48 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
3 2 1 353. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
4 2 3 269. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
5 2 6 150. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
6 2 8 24.2 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
# ℹ 35 more variables: DAY_NIGHT <chr>, STN_ORDER <fct>, TRANSECT <fct>,
# DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>, STN_NISKIN <chr>, OilRig_Count <int>,
# MLD <dbl>, SAL_MAX <dbl>, O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>,
# STN_CATEGORY_4 <chr>, CLUSTER <fct>, Watermass <chr>, WATER_MASS <chr>,
# SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>, DCM_THRESHOLD <chr>,
# O2_THRESHOLD <chr>, DIC <dbl>, NH4 <dbl>, NO2 <dbl>, NO3 <dbl>,
# Oxygen_CTD <dbl>, PO4 <dbl>, SIL <dbl>, Salinity <dbl>, TA <dbl>, …# Make categorical data table from metadata (Table 1)
categorical <- table_wstn_wdepth %>%
select(STN_NISKIN, CLUSTER, WATER_MASS, STN_ORDER, TRANSECT, OilRig_Count, DAY_NIGHT, HR_OF_DAY, ends_with("_THRESHOLD")) %>% distinct() %>%
arrange(STN_NISKIN)
# Generate a matrix of numerical data from metadata
metadata_for_rda_0 <- table_supp %>%
# Ratios for consideration
mutate(SIL_NO3 = (SIL/NO3)) %>%
mutate(T_S = (Temperature/Salinity)) %>%
select(STN_NISKIN, Depth, DIST_OUTFLOW, DIC, NH4, NO2, NO3, Oxygen_CTD, PO4, SIL, Salinity, TA, Temperature, pH, SIL_NO3, T_S) %>% distinct() %>%
mutate_if(is.numeric, list(~na_if(., Inf))) %>%
arrange(STN_NISKIN) %>%
column_to_rownames(var = "STN_NISKIN") %>%
as.matrixNormalize matrix of numeric metadata then re-combine with categorical metadata.
# Normalize
standarize_metadata <- decostand(metadata_for_rda_0, MARGIN = 2, method = "standardize")
# Combine normalized metadata with categorical data
metadata_normalized <- categorical %>%
right_join(as.data.frame(standarize_metadata) %>% rownames_to_column(var = "STN_NISKIN")) %>%
arrange(STN_NISKIN) %>%
column_to_rownames(var = "STN_NISKIN")Joining with `by = join_by(STN_NISKIN)`head(metadata_normalized) CLUSTER WATER_MASS STN_ORDER TRANSECT OilRig_Count
S11_N1 9 Uncharacterized Deep S11 transect3 4
S11_N10 7 GCW S11 transect3 4
S11_N11 4 Uncharacterized Upper S11 transect3 4
S11_N12 5 Uncharacterized Upper S11 transect3 4
S11_N4 6 Uncharacterized Deep S11 transect3 4
S11_N5 3 Uncharacterized Deep S11 transect3 4
DAY_NIGHT HR_OF_DAY SAL_THRESHOLD MLD_THRESHOLD DCM_THRESHOLD
S11_N1 Night 1 Below salinity maximum Below MLD Below DCM
S11_N10 Night 1 Below salinity maximum Below MLD Above DCM
S11_N11 Night 1 Above salinity maximum Below MLD Above DCM
S11_N12 Night 1 Above salinity maximum Above MLD Above DCM
S11_N4 Night 1 Below salinity maximum Below MLD Below DCM
S11_N5 Night 1 Below salinity maximum Below MLD Below DCM
O2_THRESHOLD Depth DIST_OUTFLOW DIC NH4
S11_N1 Below oxygen minimum 1.0656158 -0.3419148 0.6121780 -1.06454524
S11_N10 Above oxygen minimum -0.8498515 -0.3419148 -0.1416923 -0.40242311
S11_N11 Above oxygen minimum -0.9092012 -0.3419148 -1.5790889 -0.30310479
S11_N12 Above oxygen minimum -0.9483176 -0.3419148 -1.9992678 -0.83280249
S11_N4 Below oxygen minimum 0.4165933 -0.3419148 0.8090325 -0.03825594
S11_N5 Below oxygen minimum 0.1425326 -0.3419148 NA -0.83280249
NO2 NO3 Oxygen_CTD PO4 SIL Salinity
S11_N1 0.07507059 0.6630853 1.36650383 0.6131333 1.135296 -0.09578975
S11_N10 4.26400958 -1.1921350 0.09008943 -1.1882518 -1.188307 1.15859224
S11_N11 -0.46543766 -1.2909892 -0.96215622 -1.3308075 -1.226399 0.57653588
S11_N12 -0.46543766 -1.2909892 -0.25902583 -1.3696863 -1.178784 -1.27651201
S11_N4 -0.46543766 0.9937355 0.34978219 1.0148811 1.163865 -0.12106515
S11_N5 -0.46543766 1.1513908 -0.20757727 1.1055983 1.078158 -0.13550824
TA Temperature pH SIL_NO3 T_S
S11_N1 -1.01867175 -0.9599997 -0.4956247 -0.4005353 -0.9333484
S11_N10 0.59845104 0.7378415 0.6733987 -0.4005414 0.6144269
S11_N11 0.03073905 1.3989757 1.3606220 NA 1.2817223
S11_N12 -0.13764584 1.5769990 1.4945189 NA 1.6635245
S11_N4 -1.14888438 -0.8786557 -0.7451080 -0.4087931 -0.8540955
S11_N5 NA -0.8160761 NA -0.4139334 -0.7931227# Output from here should be the same rownames for ASV table below.
rownames(metadata_normalized) [1] "S11_N1" "S11_N10" "S11_N11" "S11_N12" "S11_N4" "S11_N5" "S11_N6"
[8] "S11_N7" "S12_N1" "S12_N11" "S12_N12" "S12_N2" "S12_N3" "S12_N4"
[15] "S12_N5" "S12_N6" "S14_N1" "S14_N10" "S14_N11" "S14_N12" "S14_N2"
[22] "S14_N3" "S14_N4" "S14_N5" "S14_N6" "S14_N7" "S14_N8" "S14_N9"
[29] "S15_N10" "S15_N12" "S15_N6" "S15_N7" "S15_N8" "S15_N9" "S1_N10"
[36] "S1_N5" "S2_N1" "S2_N11" "S2_N3" "S2_N6" "S2_N8" "S3_N1"
[43] "S3_N12" "S3_N2" "S3_N3" "S3_N4" "S3_N5" "S3_N6" "S3_N7"
[50] "S3_N8" "S3_N9" "S4_N1" "S4_N10" "S4_N11" "S4_N2" "S4_N3"
[57] "S4_N4" "S4_N5" "S4_N6" "S4_N9" "S5_N1" "S5_N10" "S5_N11"
[64] "S5_N12" "S5_N2" "S5_N3" "S5_N4" "S5_N5" "S5_N6" "S7_N10"
[71] "S7_N11" "S7_N12" "S7_N2" "S7_N3" "S7_N4" "S7_N5" "S7_N6"
[78] "S7_N7" "S7_N9" "S8_N1" "S8_N10" "S8_N11" "S8_N3" "S8_N4"
[85] "S8_N5" "S8_N7" "S8_N8" "S8_N9" "S9_N5" "S9_N9" Generate a matrix from ASV counts
# head(asv_long_avg_wtax_clean)
asv_wide_avg_reps_matrix <- asv_long_avg_wtax_clean %>%
ungroup() %>%
select(FeatureID, STN_NISKIN, MEAN_REPS_seq) %>%
pivot_wider(names_from = FeatureID, values_from = MEAN_REPS_seq, values_fn = mean, values_fill = 0) %>%
arrange(STN_NISKIN) %>%
column_to_rownames(var = "STN_NISKIN") %>%
as.matrix()
rownames(asv_wide_avg_reps_matrix) [1] "S11_N1" "S11_N10" "S11_N11" "S11_N12" "S11_N4" "S11_N5" "S11_N6"
[8] "S11_N7" "S12_N1" "S12_N11" "S12_N12" "S12_N2" "S12_N3" "S12_N4"
[15] "S12_N5" "S12_N6" "S14_N1" "S14_N10" "S14_N11" "S14_N12" "S14_N2"
[22] "S14_N3" "S14_N4" "S14_N5" "S14_N6" "S14_N7" "S14_N8" "S14_N9"
[29] "S15_N10" "S15_N12" "S15_N6" "S15_N7" "S15_N8" "S15_N9" "S1_N10"
[36] "S1_N5" "S2_N1" "S2_N11" "S2_N3" "S2_N6" "S2_N8" "S3_N1"
[43] "S3_N12" "S3_N2" "S3_N3" "S3_N4" "S3_N5" "S3_N6" "S3_N7"
[50] "S3_N8" "S3_N9" "S4_N1" "S4_N10" "S4_N11" "S4_N2" "S4_N3"
[57] "S4_N4" "S4_N5" "S4_N6" "S4_N9" "S5_N1" "S5_N10" "S5_N11"
[64] "S5_N12" "S5_N2" "S5_N3" "S5_N4" "S5_N5" "S5_N6" "S7_N10"
[71] "S7_N11" "S7_N12" "S7_N2" "S7_N3" "S7_N4" "S7_N5" "S7_N6"
[78] "S7_N7" "S7_N9" "S8_N1" "S8_N10" "S8_N11" "S8_N3" "S8_N4"
[85] "S8_N5" "S8_N7" "S8_N8" "S8_N9" "S9_N5" "S9_N9" clr_asv_matrix <- data.frame(compositions::clr(asv_wide_avg_reps_matrix))
# Confirm that all
asv_list <- colnames(asv_wide_avg_reps_matrix)
# asv_list
meta_list <- rownames(metadata_normalized)
clr_list <- rownames(clr_asv_matrix)
setdiff(meta_list, clr_list)character(0)Run RDA analysis
# surf_table <- table_supp %>%
# filter(grepl("Above",DCM_THRESHOLD)) %>%
# ungroup()
# surf_subset <- as.character(unique(surf_table$STN_NISKIN))
# surf_subset# | eval: false
# rda_all_gom <- vegan::rda(clr_asv_matrix ~ ., data = metadata_normalized, na.action = na.exclude)
# rda_all_gom
# ?rda# | eval: false
# adj_rda_out <- RsquareAdj(rda_all_gom)$adj.r.squared
# # RsquareAdj(rda_all_gom)
# adj_rda_out
# anova_out <- anova.cca(rda_all_gom, step = 100, by = "term")
# ?anova.cca()Large f-ratio means there is a lot of between group variance compared to within-group variance (independent variables have a greater influence on depdendent variables) Smaller f-ratios indicate the groups are similar to one another. So differences in groups cannot be differentiated from “random” Variance indicates the overall spread of data
10.2 RDA function
First create a function for running RDA analysis.
asv_list <- colnames(asv_wide_avg_reps_matrix)
# asv_list
meta_list <- rownames(metadata_normalized)
clr_list <- rownames(clr_asv_matrix)
save(table_supp, table_wstn_wdepth, metadata_normalized, asv_long_avg_wtax_clean, file = "RDA_Robjects_07012026.RData")head(metadata_normalized) CLUSTER WATER_MASS STN_ORDER TRANSECT OilRig_Count
S11_N1 9 Uncharacterized Deep S11 transect3 4
S11_N10 7 GCW S11 transect3 4
S11_N11 4 Uncharacterized Upper S11 transect3 4
S11_N12 5 Uncharacterized Upper S11 transect3 4
S11_N4 6 Uncharacterized Deep S11 transect3 4
S11_N5 3 Uncharacterized Deep S11 transect3 4
DAY_NIGHT HR_OF_DAY SAL_THRESHOLD MLD_THRESHOLD DCM_THRESHOLD
S11_N1 Night 1 Below salinity maximum Below MLD Below DCM
S11_N10 Night 1 Below salinity maximum Below MLD Above DCM
S11_N11 Night 1 Above salinity maximum Below MLD Above DCM
S11_N12 Night 1 Above salinity maximum Above MLD Above DCM
S11_N4 Night 1 Below salinity maximum Below MLD Below DCM
S11_N5 Night 1 Below salinity maximum Below MLD Below DCM
O2_THRESHOLD Depth DIST_OUTFLOW DIC NH4
S11_N1 Below oxygen minimum 1.0656158 -0.3419148 0.6121780 -1.06454524
S11_N10 Above oxygen minimum -0.8498515 -0.3419148 -0.1416923 -0.40242311
S11_N11 Above oxygen minimum -0.9092012 -0.3419148 -1.5790889 -0.30310479
S11_N12 Above oxygen minimum -0.9483176 -0.3419148 -1.9992678 -0.83280249
S11_N4 Below oxygen minimum 0.4165933 -0.3419148 0.8090325 -0.03825594
S11_N5 Below oxygen minimum 0.1425326 -0.3419148 NA -0.83280249
NO2 NO3 Oxygen_CTD PO4 SIL Salinity
S11_N1 0.07507059 0.6630853 1.36650383 0.6131333 1.135296 -0.09578975
S11_N10 4.26400958 -1.1921350 0.09008943 -1.1882518 -1.188307 1.15859224
S11_N11 -0.46543766 -1.2909892 -0.96215622 -1.3308075 -1.226399 0.57653588
S11_N12 -0.46543766 -1.2909892 -0.25902583 -1.3696863 -1.178784 -1.27651201
S11_N4 -0.46543766 0.9937355 0.34978219 1.0148811 1.163865 -0.12106515
S11_N5 -0.46543766 1.1513908 -0.20757727 1.1055983 1.078158 -0.13550824
TA Temperature pH SIL_NO3 T_S
S11_N1 -1.01867175 -0.9599997 -0.4956247 -0.4005353 -0.9333484
S11_N10 0.59845104 0.7378415 0.6733987 -0.4005414 0.6144269
S11_N11 0.03073905 1.3989757 1.3606220 NA 1.2817223
S11_N12 -0.13764584 1.5769990 1.4945189 NA 1.6635245
S11_N4 -1.14888438 -0.8786557 -0.7451080 -0.4087931 -0.8540955
S11_N5 NA -0.8160761 NA -0.4139334 -0.7931227# all_samples <- table_supp %>% ungroup()
# all_samples_list <- as.character(unique(table_supp$STN_NISKIN))
# asvs_all_samples <- asv_long_avg_wtax_clean %>%
# filter(STN_NISKIN %in% all_samples_list) %>% ungroup()
#
# all_rda <- run_rda(all_samples, asvs_all_samples,
# "All samples", features = "include")
# surf_table <- table_supp %>%
# filter(grepl("Above",DCM_THRESHOLD)) %>%
# ungroup()
# surf_subset <- as.character(unique(surf_table$STN_NISKIN))
# surf_asv <- asv_long_avg_wtax_clean %>%
# filter(STN_NISKIN %in% surf_subset) %>% ungroup()
# above_dcm_rda <- run_rda(surf_table, surf_asv, "Above DCM", features = "exclude")10.3 RDA compile from HPRC
fxn-runRDA_1000_HPRC.R RDA_Robjects_06132026.RData
# # install.packages("gt")
# library(gt)load("output-tables/df_rda_output_06162026.RData", verbose = TRUE)Loading objects:
df_rda_resultshead(df_rda_results)# A tibble: 6 × 24
TEST PERC_CONSTRAINED ADJ_R_SQUARED FORMULA WATER_MASS OilRig_Count DAY_NIGHT
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
1 All … 0.448 0.159 clr_as… 0.001 0.565 0.235
2 Abov… 0.839 0.0866 clr_as… NA 0.494 0.585
3 Belo… 0.370 0.118 clr_as… NA 0.192 0.091
4 Abov… 0.839 0.0866 clr_as… NA 0.537 0.592
5 Belo… 0.370 0.118 clr_as… NA 0.164 0.084
6 All … 0.453 0.167 clr_as… 0.001 0.342 0.29
# ℹ 17 more variables: SAL_THRESHOLD <dbl>, MLD_THRESHOLD <dbl>,
# DCM_THRESHOLD <dbl>, O2_THRESHOLD <dbl>, DIC_TA <dbl>, DIST_OUTFLOW <dbl>,
# Depth <dbl>, NH4 <dbl>, NO2 <dbl>, NO3 <dbl>, Oxygen_CTD <dbl>, PO4 <dbl>,
# SIL_NO3 <dbl>, T_S <dbl>, Temperature <dbl>, pH <dbl>, Residual <dbl>rda_summary_table <- gt(df_rda_results %>%
select(-FORMULA, -Residual, `Input data` = TEST,
`Percent constrained` = PERC_CONSTRAINED,
`Adjusted R-squared` = ADJ_R_SQUARED,
`MLD` = MLD_THRESHOLD, `DCM` = DCM_THRESHOLD,
`Salinity maximum` = SAL_THRESHOLD,
`Oxygen minimum depth` = O2_THRESHOLD,
`Depth (m)` = Depth,
`Distance to shore` = DIST_OUTFLOW,
`Water mass` = WATER_MASS, `Oil Rigs` = OilRig_Count, `Time of day` = DAY_NIGHT, `Dissolved oxygen` = Oxygen_CTD, `DIC:TA` = DIC_TA, Temperature, `Temp:salinity` = T_S, `Silicate:nitrate` = SIL_NO3, NH4, NO2, NO3, PO4, pH)) %>%
# `Day vs. night` = DAY_NIGHT,
# `Number of oil rigs` = OilRig_Count)) %>%
data_color(columns = -c(`Input data`, `Percent constrained`, `Adjusted R-squared`),
palette = "Reds", apply_to = c("fill"),
na_color = "grey30",
fn = scales::col_bin(
palette = c("transparent", "#730303", "#d62d2d", "#bd6666", "transparent" ,"transparent"),
bins = c(0, 0.001, 0.01, 0.05, 0.1, 1),
na.color = "grey20"
))
rda_summary_table| Input data | Percent constrained | Adjusted R-squared | Water mass | Oil Rigs | Time of day | Salinity maximum | MLD | DCM | Oxygen minimum depth | DIC:TA | Distance to shore | Depth (m) | NH4 | NO2 | NO3 | Dissolved oxygen | PO4 | Silicate:nitrate | Temp:salinity | Temperature | pH |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All samples | 0.4478239 | 0.159186421 | 0.001 | 0.565 | 0.235 | 0.302 | 0.085 | 0.005 | 0.619 | 0.359 | 0.027 | 0.022 | 0.234 | 0.069 | 0.344 | 0.187 | 0.635 | 0.059 | 0.054 | 0.187 | 0.605 |
| Above DCM | 0.8388040 | 0.086556182 | NA | 0.494 | 0.585 | NA | NA | NA | NA | 0.001 | 0.430 | 0.122 | 0.609 | 0.469 | 0.268 | 0.485 | 0.762 | 0.659 | 0.100 | 0.780 | 0.154 |
| Below DCM | 0.3696670 | 0.117533744 | NA | 0.192 | 0.091 | NA | NA | NA | NA | 0.001 | 0.001 | 0.001 | 0.226 | 0.147 | 0.009 | 0.097 | 0.042 | 0.070 | 0.154 | 0.068 | 0.844 |
| Above O2 | 0.8388040 | 0.086556182 | NA | 0.537 | 0.592 | NA | NA | NA | NA | 0.002 | 0.443 | 0.113 | 0.591 | 0.484 | 0.279 | 0.460 | 0.775 | 0.667 | 0.104 | 0.749 | 0.141 |
| Below O2 | 0.3696670 | 0.117533744 | NA | 0.164 | 0.084 | NA | NA | NA | NA | 0.001 | 0.001 | 0.001 | 0.203 | 0.132 | 0.015 | 0.117 | 0.039 | 0.099 | 0.132 | 0.082 | 0.816 |
| All dinoflagellates (no Syn) | 0.4532549 | 0.167456363 | 0.001 | 0.342 | 0.290 | 0.099 | 0.003 | 0.034 | 0.715 | 0.572 | 0.076 | 0.477 | 0.402 | 0.051 | 0.611 | 0.294 | 0.683 | 0.017 | 0.086 | 0.020 | 0.348 |
| Dinoflagellates (no Syn) above DCM | 0.8576477 | 0.193337020 | NA | 0.207 | 0.494 | NA | NA | NA | NA | 0.007 | 0.499 | 0.016 | 0.380 | 0.149 | 0.133 | 0.303 | 0.156 | 0.351 | 0.030 | 0.219 | 0.246 |
| Dinoflagellates (no Syn) below DCM | 0.3422841 | 0.079197738 | NA | 0.078 | 0.065 | NA | NA | NA | NA | 0.001 | 0.014 | 0.130 | 0.511 | 0.113 | 0.076 | 0.280 | 0.131 | 0.226 | 0.047 | 0.847 | 0.673 |
| Syndiniales | 0.4422593 | 0.150713034 | 0.001 | 0.741 | 0.366 | 0.342 | 0.086 | 0.001 | 0.778 | 0.371 | 0.047 | 0.073 | 0.411 | 0.197 | 0.266 | 0.222 | 0.594 | 0.256 | 0.036 | 0.402 | 0.853 |
| Syndiniales above DCM | 0.8225921 | -0.005311708 | NA | 0.828 | 0.782 | NA | NA | NA | NA | 0.004 | 0.472 | 0.287 | 0.831 | 0.806 | 0.333 | 0.545 | 0.803 | 0.849 | 0.332 | 0.955 | 0.145 |
| Syndiniales below DCM | 0.3771555 | 0.128017648 | NA | 0.182 | 0.145 | NA | NA | NA | NA | 0.001 | 0.002 | 0.002 | 0.366 | 0.153 | 0.018 | 0.158 | 0.018 | 0.042 | 0.109 | 0.009 | 0.697 |
| Radiolaria | 0.4468976 | 0.157775955 | 0.001 | 0.833 | 0.577 | 0.514 | 0.918 | 0.029 | 0.625 | 0.422 | 0.084 | 0.002 | 0.222 | 0.451 | 0.066 | 0.297 | 0.387 | 0.845 | 0.371 | 0.768 | 0.974 |
| Radiolaria above DCM | 0.8126706 | -0.061533486 | NA | 0.906 | 0.764 | NA | NA | NA | NA | 0.010 | 0.484 | 0.256 | 0.682 | 0.805 | 0.796 | 0.791 | 0.888 | 0.784 | 0.241 | 0.989 | 0.275 |
| Radiolaria below DCM | 0.3867869 | 0.141501715 | NA | 0.540 | 0.314 | NA | NA | NA | NA | 0.001 | 0.020 | 0.001 | 0.348 | 0.154 | 0.003 | 0.092 | 0.009 | 0.332 | 0.308 | 0.029 | 0.768 |
| Ciliate | 0.3939905 | 0.077212773 | 0.001 | 0.653 | 0.552 | 0.679 | 0.122 | 0.106 | 0.752 | 0.280 | 0.025 | 0.508 | 0.318 | 0.226 | 0.635 | 0.266 | 0.851 | 0.025 | 0.306 | 0.179 | 0.276 |
| Ciliate above DCM | 0.8660340 | 0.240859481 | NA | 0.044 | 0.141 | NA | NA | NA | NA | 0.001 | 0.215 | 0.022 | 0.033 | 0.098 | 0.015 | 0.036 | 0.614 | 0.044 | 0.136 | 0.187 | 0.040 |
| Ciliate below DCM | 0.3210901 | 0.049526130 | NA | 0.148 | 0.387 | NA | NA | NA | NA | 0.001 | 0.001 | 0.536 | 0.530 | 0.261 | 0.210 | 0.590 | 0.229 | 0.198 | 0.194 | 0.666 | 0.784 |
| Gyrista | 0.5182773 | 0.266467772 | 0.001 | 0.010 | 0.008 | 0.262 | 0.147 | 0.044 | 0.089 | 0.288 | 0.001 | 0.420 | 0.022 | 0.001 | 0.313 | 0.106 | 0.768 | 0.009 | 0.008 | 0.049 | 0.167 |
| Gyrista above DCM | 0.9047966 | 0.460513955 | NA | 0.003 | 0.019 | NA | NA | NA | NA | 0.003 | 0.006 | 0.098 | 0.066 | 0.002 | 0.064 | 0.058 | 0.427 | 0.302 | 0.039 | 0.054 | 0.066 |
| Gyrista below DCM | 0.4332193 | 0.206507017 | NA | 0.233 | 0.006 | NA | NA | NA | NA | 0.001 | 0.003 | 0.156 | 0.002 | 0.047 | 0.087 | 0.297 | 0.134 | 0.025 | 0.150 | 0.060 | 0.754 |
| Bigyra | 0.4244846 | 0.123646961 | 0.001 | 0.480 | 0.771 | 0.291 | 0.018 | 0.066 | 0.786 | 0.384 | 0.315 | 0.016 | 0.554 | 0.019 | 0.232 | 0.264 | 0.862 | 0.029 | 0.026 | 0.192 | 0.946 |
| Bigyra above DCM | 0.8224583 | -0.006069663 | NA | 0.787 | 0.722 | NA | NA | NA | NA | 0.008 | 0.426 | 0.481 | 0.876 | 0.945 | 0.785 | 0.330 | 0.606 | 0.407 | 0.087 | 0.665 | 0.539 |
| Bigyra below DCM | 0.3601419 | 0.104198714 | NA | 0.081 | 0.337 | NA | NA | NA | NA | 0.003 | 0.066 | 0.001 | 0.424 | 0.123 | 0.032 | 0.367 | 0.088 | 0.021 | 0.222 | 0.455 | 0.700 |
| Haptista | 0.5089883 | 0.252323141 | 0.001 | 0.327 | 0.436 | 0.031 | 0.003 | 0.036 | 0.770 | 0.251 | 0.402 | 0.815 | 0.828 | 0.031 | 0.786 | 0.118 | 0.965 | 0.035 | 0.011 | 0.016 | 0.286 |
| Haptista above DCM | 0.8629726 | 0.223511170 | NA | 0.331 | 0.320 | NA | NA | NA | NA | 0.006 | 0.537 | 0.096 | 0.829 | 0.742 | 0.187 | 0.163 | 0.063 | 0.456 | 0.006 | 0.325 | 0.141 |
| Haptista below DCM | 0.3747821 | 0.124694992 | NA | 0.410 | 0.161 | NA | NA | NA | NA | 0.001 | 0.163 | 0.088 | 0.796 | 0.072 | 0.217 | 0.860 | 0.153 | 0.076 | 0.188 | 0.179 | 0.890 |
gtsave(rda_summary_table, filename = "output-tables/RDA-gt-table.html")
gtsave(rda_summary_table, filename = "output-tables/RDA-gt-table.docx")11 Indicator Species
11.1 indicspecies
https://cran.r-project.org/web/packages/indicspecies/vignettes/IndicatorSpeciesAnalysis.html
# install.packages("indicspecies")
# library(indicspecies)multipatt is the name of the command from the indicspecies package. The mulitpatt command results in lists of species that are associated with a particular group of samples. If your group has more than 2 categories, multipatt will also identify species that are statistically more abundant in combinations of categories.
The parameters for multipatt are as follows:
the community abundance matrix:“abund” the vector that contains your sample grouping information: “time” the function that multipatt is using to identify indicator species: func = “r.g” the number of permutations used in the statistical test: control = how(nperm=9999)
https://cran.r-universe.dev/indicspecies/doc/manual.html
# list_top
range(table_supp$Oxygen_CTD)[1] 2.202 4.871Isolate the top taxa noted before performing Indicator Species analysis.
# | eval: false
# | echo: true
indic_input <- asv_long_avg_wtax_clean %>%
left_join(table_supp) %>%
# filter(Supergroup_simplified != "TSAR-NA") %>%
# filter(Supergroup_simplified %in% list_top) %>%
filter(!grepl("_X", Supergroup_simplified)) %>%
# Add additional bins
mutate(SAL_BIN = case_when(
Salinity < 29 ~ "< 29",
Salinity >= 29 & Salinity < 31 ~ "29-31",
Salinity >= 31 & Salinity < 34 ~ "31-34",
Salinity >= 34 ~ "> 34")) %>%
mutate(O2_BIN = case_when(
Oxygen_CTD <= 3 ~ "< 3",
Oxygen_CTD > 3 ~ "> 3")) %>%
# Add additional bins
mutate(TEMP_BIN = case_when(
Temperature < 5 ~ "< 5",
Temperature >=5 & Temperature < 15 ~ "5-15",
Temperature >= 15 & Temperature < 20 ~ "15-20",
Temperature >= 20 & Temperature < 25 ~ "20-25",
Temperature >= 25 ~ "> 25")) %>%
mutate(NO3_BIN = case_when(
NO3 < 10 ~ "< 10",
NO3 >= 10 & NO3 < 20 ~ "10-20",
NO3 >= 20 ~ "> 20")) %>%
mutate(OIL_BIN = case_when(
OilRig_Count > 0 ~ "Oil Rig",
TRUE ~ "No oil rig"
)) %>% select(STN_NISKIN, Station, CLUSTER, DEPTH_BIN, ends_with("_THRESHOLD"),
SAL_BIN, TEMP_BIN, NO3_BIN, OIL_BIN, WATER_MASS, OilRig_Count, O2_BIN,
MEAN_REPS_seq, FeatureID) %>%
distinct() %>%
pivot_wider(names_from = FeatureID, names_prefix = "ASV_", values_from = MEAN_REPS_seq, values_fill = 0)Joining with `by = join_by(STN_NISKIN)`“Indval” two species are not strongly correlated and their frequency in the data set is larger than the frequency of sites belonging to the site group, the joint occurrence of the two species has higher positive predictive value for the site group than the two species taken independently
‘d’ of species as the product of the relative frequency and relative average abundance in clusters.
11.2 Fxn summary of indicator species
# INV$sign[1]
summary_fxn_indic_spp <- function(clust, abun, param){
df_inv <- multipatt(abun, clust,
func = "IndVal",
duleg = TRUE,
control = how(nperm = 99))
#
prelim <- (as.data.frame(df_inv$sign))
max_index <- max(prelim$index)
summary_inv_df <- (as.data.frame(df_inv$sign)) %>%
rownames_to_column(var = "FeatureID") %>%
pivot_longer(cols = starts_with("s."), names_to = "Indicative_of", values_to = "P_A") %>%
filter(P_A == 1) %>%
filter(p.value <= 0.05) %>%
mutate(indic_index = (index/max_index),
FeatureID = str_remove_all(FeatureID, "ASV_")) %>%
select(Indicative_of, FeatureID, indic_index, p.value, stat) %>%
add_column(PARAM_INDV = param)
}
# ?multipatt()summary_fxn_indic_spp(clust, abun, param)
abun <- indic_input %>%
select(STN_NISKIN, starts_with("ASV_")) %>%
column_to_rownames(var = "STN_NISKIN")All depths and stations
cluster_indic_bin <- summary_fxn_indic_spp((indic_input$CLUSTER), abun, "Cluster")
# cluster_indic_bin
watermass_indic_bin <- summary_fxn_indic_spp((indic_input$WATER_MASS), abun, "Water mass")
# watermass_indic_bintax_annot <- asv_long_avg_wtax_clean %>%
select(FeatureID, Supergroup_simplified, SUPERGROUP_CLASS, Taxon) %>% distinct()11.3 Cluster-specific indicator results
# head(cluster_indic_bin)
indicator_barplot_cluster <- cluster_indic_bin %>%
left_join(tax_annot) %>%
group_by(Supergroup_simplified, Indicative_of, PARAM_INDV) %>%
summarise(COUNT = n()) %>%
mutate(indic_cluster = str_replace_all(Indicative_of,"s\\.", "")) %>%
mutate(cluster_order = factor(indic_cluster, levels = rev(CLUSTER_NUM))) %>%
mutate(tax_order = factor(Supergroup_simplified, levels = (tax_simplified_list))) %>%
ggplot(aes(y = (cluster_order), x = COUNT, fill = tax_order)) +
geom_bar(stat = "identity", position = "stack",
color = "grey20", width = 0.8, linewidth = 0.3) +
scale_fill_manual(values = tax_color) +
# scale_y_discrete(position = "right") +
scale_y_discrete(position = "left") +
scale_x_continuous(expand = c(0,0), limits = c(0,300)) +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
strip.background = element_blank(),
# axis.ticks = element_blank(),
axis.title = element_text(color = "black", size = 8),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 0, size = 8),
panel.grid.major = element_line(color = "grey70", linetype = "dashed")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Number of indicator ASVs", y = "k-means cluster")Joining with `by = join_by(FeatureID)`
`summarise()` has regrouped the output.11.4 Water mass-specific indicator results
# head(watermass_indic_bin)
indicator_bar_watermass <- watermass_indic_bin %>%
left_join(tax_annot) %>%
group_by(Supergroup_simplified, Indicative_of, PARAM_INDV) %>%
summarise(COUNT = n()) %>%
mutate(water_mass = str_replace_all(Indicative_of,"s\\.", "")) %>%
mutate(water_order = factor(water_mass, levels = rev(water_mass_order))) %>%
mutate(tax_order = factor(Supergroup_simplified, levels = (tax_simplified_list))) %>%
ggplot(aes(y = (water_order), x = COUNT, fill = tax_order)) +
geom_bar(stat = "identity", position = "stack",
color = "grey20", width = 0.6, linewidth = 0.3) +
scale_fill_manual(values = tax_color) +
scale_y_discrete(position = "left") +
scale_x_continuous(expand = c(0,0), limits = c(0,300)) +
theme_classic() + theme(
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black"),
strip.background = element_blank(),
axis.title = element_text(color = "black", size = 8),
axis.text.y = element_text(color = "black", vjust = 0.5, hjust = 1, size = 8),
panel.grid.major = element_line(color = "grey70", linetype = "dashed")) +
guides(fill = guide_legend(reverse = TRUE, ncol = 1)) +
labs(x = "Number of indicator ASVs", y = "Water mass")Joining with `by = join_by(FeatureID)`
`summarise()` has regrouped the output.Supplemental figure for water masses and kmean clustering indicator species.
(indicator_barplot_cluster + theme(legend.position = "none")) + indicator_bar_watermass + plot_layout(ncol = 2)Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_bar()`).Warning: Removed 10 rows containing missing values or values outside the scale range
(`geom_bar()`).
ggsave("figures/indicator-2panel.svg", width = 10, height = 4, device = "svg", limitsize = FALSE)Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_bar()`).
Removed 10 rows containing missing values or values outside the scale range
(`geom_bar()`).12 Dinoflagellate & ciliate functional traits
tmp <- asv_long_avg_wtax_clean %>%
filter(Supergroup_simplified == "TSAR-Dinoflagellata") #%>%
# filter(Class == "Syndiniales")
length(unique(asv_long_avg_wtax_clean$FeatureID))[1] 10563length(unique(tmp$FeatureID))[1] 5521# 10563 # all ASVs
# 3611 # Syndiniales Class
# 5521 # Dinoflagellates
# 5521 / 10563 # Dino percentage of ASVs
# 3611 / 5521 # Syndiniales percentage of dino ASVs
head(asv_long_avg_wtax_clean)# A tibble: 6 × 15
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
2 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
3 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
4 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
5 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
6 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
# ℹ 9 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>non_syn_dinos_cil <- asv_long_avg_wtax_clean %>%
filter((Supergroup_simplified == "TSAR-Dinoflagellata") | (Supergroup_simplified == "TSAR-Ciliophora")) %>%
filter(Class != "Syndiniales") %>%
select(FeatureID, Supergroup_simplified, Order, Family, Genus, Species) %>% distinct() %>%
drop_na()
table(non_syn_dinos_cil$Order)
Apostomatia Choreotrichida Choreotrichida-Tintinnina
19 99 23
Ciliophora_XX CONTH_6_X CONTH_8_X
5 2 2
CONThreeP_X Cyrtophoria_1 Cyrtophoria_8
97 34 3
Dinophyceae_X Dinophyta_XX Euplotia
672 14 2
Gonyaulacales Gymnodiniales Heterotrichea_X
14 77 1
Hymenostomatia Noctilucales OLIGO5
1 1 120
Oligohymenophorea_X Oligotrichida Peniculia
1 142 2
Peridiniales Peritrichia_2 Prorocentrales
27 2 23
Prorodontida Suctoria Suessiales
1 4 1 # Export for manual curation
# write.csv(non_syn_dinos_cil, "non_syn_dinos_cil.csv")Manual curation done, compared to Jones et al. table.
manual_curated_fxn <- read.csv("non_syn_dinos_cil.csv")
jones_db <- read.csv("../tax_annotation_proj/TMDv1.1_Jones.csv")
trait_asv <- asv_long_avg_wtax_clean %>%
left_join(manual_curated_fxn) %>%
mutate(Phylum = case_when(
Class == "Syndiniales" ~ "Dinoflagellata",
TRUE ~ Phylum
),
`Grazer.Taxonomic.ID` = case_when(
Class == "Syndiniales" ~ "Syndiniales (Class)",
TRUE ~ `Grazer.Taxonomic.ID`
)) %>%
left_join(jones_db) %>%
filter((Supergroup_simplified == "TSAR-Dinoflagellata") | (Supergroup_simplified == "TSAR-Ciliophora")) %>%
mutate(MORPHOTYPE = case_when(
Morphotype == "PA" ~ "Parasite",
Morphotype == "CM" ~ "Constitutive mixotroph",
Morphotype == "CM*" ~ "Constitutive mixotroph",
Morphotype == "CM/PA" ~ "Constitutive mixotroph/parasite",
Morphotype == "GH/PA" ~ "Generalist heterotroph/parasite",
Morphotype == "GH" ~ "Generalist Heterotroph",
Morphotype == "SH" ~ "Selective heterotroph",
Morphotype == "CV" ~ "Generalist, carnivore heterotroph",
Morphotype == "pSNCM" ~ "Plastid bearing Specialist Non-Constitutive Mixotroph",
grepl("GNCM", Morphotype) ~ "Generalist non-constitutive mixotroph",
`Feeding.Mode` == "Autotroph" ~ "Autotroph",
TRUE ~ Morphotype))Joining with `by = join_by(FeatureID, Supergroup_simplified, Order, Family,
Genus, Species)`
Joining with `by = join_by(Phylum, Grazer.Taxonomic.ID)`glimpse(trait_asv)Rows: 59,502
Columns: 39
$ FeatureID <chr> "00170876c27b5c06fb835ad64c2125e1", "00170876c2…
$ Taxon <chr> "Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin…
$ DIVISION <chr> "Dinoflagellata", "Dinoflagellata", "Dinoflagel…
$ SUPERGROUP_CLASS <chr> "Dinoflagellata-Syndiniales", "Dinoflagellata-S…
$ Supergroup_simplified <chr> "TSAR-Dinoflagellata", "TSAR-Dinoflagellata", "…
$ Supergroup <chr> "TSAR", "TSAR", "TSAR", "TSAR", "TSAR", "TSAR",…
$ Division <chr> "Alveolata", "Alveolata", "Alveolata", "Alveola…
$ Subdivision <chr> "Dinoflagellata", "Dinoflagellata", "Dinoflagel…
$ Class <chr> "Syndiniales", "Syndiniales", "Syndiniales", "S…
$ Order <chr> "Dino-Group-II", "Dino-Group-II", "Dino-Group-I…
$ Family <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Genus <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Species <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ STN_NISKIN <chr> "S11_N4", "S11_N7", "S14_N4", "S14_N9", "S15_N8…
$ MEAN_REPS_seq <dbl> 57.0, 11.0, 26.0, 19.5, 136.5, 64.5, 40.0, 38.0…
$ Phylum <chr> "Dinoflagellata", "Dinoflagellata", "Dinoflagel…
$ Grazer.Taxonomic.ID <chr> "Syndiniales (Class)", "Syndiniales (Class)", "…
$ Synonym.Basionym <chr> "Dino-Groups (I-V); Amoebophrya sp., Syndinium …
$ Feeding.Mode <chr> "Heterotroph", "Heterotroph", "Heterotroph", "H…
$ Morphotype <chr> "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA",…
$ ESD <chr> "< 5 µm", "< 5 µm", "< 5 µm", "< 5 µm", "< 5 µm…
$ Prey_MaxESD <chr> "> 200 µm", "> 200 µm", "> 200 µm", "> 200 µm",…
$ Unknown <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Bacteria <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Detritus <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Diatoms <chr> "+", "+", "+", "+", "+", "+", "+", "+", "+", "+…
$ Haptophytes <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Chlorophytes <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Cryptophytes <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Raphidophytes <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Pelagophytes <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Dinoflagellates <chr> "+", "+", "+", "+", "+", "+", "+", "+", "+", "+…
$ Ciliates <chr> "+", "+", "+", "+", "+", "+", "+", "+", "+", "+…
$ Copepods <chr> "+", "+", "+", "+", "+", "+", "+", "+", "+", "+…
$ Copepod.Eggs...Nauplii <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ Metazoan.Host <chr> "+", "+", "+", "+", "+", "+", "+", "+", "+", "+…
$ Other <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
$ References <chr> "Coats & Heisler (1989); Coats & Park (2002); K…
$ MORPHOTYPE <chr> "Parasite", "Parasite", "Parasite", "Parasite",…# write.csv(trait_asv, file = "output-tables/fxntrait-ciliate-dino-summary.csv")12.1 Get stats on functional trait profiles
head(trait_asv)# A tibble: 6 × 39
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
2 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
3 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
4 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
5 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
6 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
# ℹ 33 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>, Phylum <chr>, Grazer.Taxonomic.ID <chr>,
# Synonym.Basionym <chr>, Feeding.Mode <chr>, Morphotype <chr>, ESD <chr>,
# Prey_MaxESD <chr>, Unknown <chr>, Bacteria <chr>, Detritus <chr>,
# Diatoms <chr>, Haptophytes <chr>, Chlorophytes <chr>, Cryptophytes <chr>,
# Raphidophytes <chr>, Pelagophytes <chr>, Dinoflagellates <chr>, …tmp <- trait_asv %>% select(Feeding.Mode, FeatureID, DIVISION) %>% distinct() %>%
mutate(assigned = case_when(
is.na(Feeding.Mode) ~ "no",
TRUE ~ "yes"
)) %>% select(-Feeding.Mode, -FeatureID)
table(tmp) assigned
DIVISION no yes
Ciliophora 562 237
Dinoflagellata 1805 3716237/(562 + 237)[1] 0.29662083716/(1805+3716)[1] 0.6730665tmp <- trait_asv %>%
# filter(Class == "Syndiniales") %>%
filter(Subdivision == "Dinoflagellata") %>%
distinct()
length(unique(tmp$FeatureID))[1] 552112.1.1 Total ASVs, trophic mode, location
head(trait_asv)# A tibble: 6 × 39
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
2 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
3 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
4 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
5 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
6 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
# ℹ 33 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>, Phylum <chr>, Grazer.Taxonomic.ID <chr>,
# Synonym.Basionym <chr>, Feeding.Mode <chr>, Morphotype <chr>, ESD <chr>,
# Prey_MaxESD <chr>, Unknown <chr>, Bacteria <chr>, Detritus <chr>,
# Diatoms <chr>, Haptophytes <chr>, Chlorophytes <chr>, Cryptophytes <chr>,
# Raphidophytes <chr>, Pelagophytes <chr>, Dinoflagellates <chr>, …unique(trait_asv$Class) [1] "Syndiniales" "Dinophyceae" "Oligohymenophorea"
[4] "Phyllopharyngea" "Dinophyta_X" "Spirotrichea"
[7] NA "Ciliophora_X" "CONThreeP"
[10] "Litostomatea" "CONTH_6" "Heterotrichea"
[13] "Prostomatea" "Prostomatea_1" "CONTH_8"
[16] "Noctilucophyceae" "Colpodea" # unique(trait_asv$SUPERGROUP_CLASS)
blues <- c("#dde4f0","#b7cae8","#96b3e3",
"#6b94d6","#3473d9", "#0b4fbd",
"#0b429c", "#07214a", "#081730")
names(blues) <- depth_bin
trait_asv %>%
left_join(table_wstn_wdepth) %>%
mutate(TAX_CLASS = case_when(
Class == "Syndiniales" ~ "Syndiniales",
TRUE ~ Subdivision
)) %>%
group_by(TAX_CLASS, MORPHOTYPE, DEPTH_BIN) %>%
summarise(ASV_TOTAL = n()) %>%
drop_na() %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = depth_order, x = ASV_TOTAL, fill = MORPHOTYPE)) +
geom_bar(stat = "identity", position = "dodge", color = "grey20") +
# scale_fill_manual(values = blues) +
facet_grid(cols = vars(TAX_CLASS), scales = "free") +
theme_classic() +
labs(x = "Total ASVs", y = "Depth")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.
trait_asv %>%
left_join(table_wstn_wdepth) %>%
mutate(TAX_CLASS = case_when(
Class == "Syndiniales" ~ "Syndiniales",
TRUE ~ Subdivision
)) %>%
group_by(TAX_CLASS, MORPHOTYPE, DEPTH_BIN) %>%
summarise(ASV_TOTAL = n()) %>%
drop_na() %>%
filter(TAX_CLASS == "Ciliophora") %>%
mutate(depth_order = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = depth_order, x = ASV_TOTAL, fill = MORPHOTYPE)) +
geom_bar(stat = "identity", position = "dodge", color = "grey20") +
scale_fill_manual(values = c("#F6CD86","#93CBF9", "#6F9A49", "#C97E28", "#808D7E")) +
scale_x_continuous(position = "top", expand = c(0,0)) +
# facet_grid(cols = vars(TAX_CLASS), scales = "free") +
theme_classic() +
theme(panel.grid.major = element_line(color = "grey80")) +
labs(x = "Total ASVs", y = "Depth")Joining with `by = join_by(STN_NISKIN)`
`summarise()` has regrouped the output.
12.2 Explore specific traits within list
head(trait_asv)# A tibble: 6 × 39
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
2 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
3 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
4 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
5 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
6 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
# ℹ 33 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>, Phylum <chr>, Grazer.Taxonomic.ID <chr>,
# Synonym.Basionym <chr>, Feeding.Mode <chr>, Morphotype <chr>, ESD <chr>,
# Prey_MaxESD <chr>, Unknown <chr>, Bacteria <chr>, Detritus <chr>,
# Diatoms <chr>, Haptophytes <chr>, Chlorophytes <chr>, Cryptophytes <chr>,
# Raphidophytes <chr>, Pelagophytes <chr>, Dinoflagellates <chr>, …unique(trait_asv$Order) [1] "Dino-Group-II" "Dino-Group-I"
[3] "Gymnodiniales" "Peridiniales"
[5] NA "Apostomatia"
[7] "Dinophyceae_X" "Cyrtophoria_1"
[9] "Scuticociliatia_1" "Gonyaulacales"
[11] "Dinophyta_XX" "Choreotrichida"
[13] "Dino-Group-IV" "OLIGO5"
[15] "Oligotrichida" "Suessiales"
[17] "Ciliophora_XX" "Prorocentrales"
[19] "Dino-Group-III" "Choreotrichida-Tintinnina"
[21] "Cyrtophoria_4" "Dinophysiales"
[23] "Synhymenia" "CONThreeP_X"
[25] "Oligohymenophorea_X" "Litostomatea_X"
[27] "Cyrtophoria_8" "Hypotrichia"
[29] "Peritrichia_2" "Cyrtophoria_2"
[31] "CONTH_6_X" "Peniculia"
[33] "Suctoria" "Dino-Group-V"
[35] "Heterotrichea_X" "Haptoria_5"
[37] "Hymenostomatia" "Euplotia"
[39] "Prorodontida" "Prostomatea_1_X"
[41] "CONTH_8_X" "Haptoria_6"
[43] "Noctilucales" "Colpodea_X" # unique(trait_asv$)prey_types_df <- trait_asv %>%
select(SUPERGROUP_CLASS, Order, Feeding.Mode, MORPHOTYPE, STN_NISKIN, Grazer.Taxonomic.ID, Synonym.Basionym, Unknown, Bacteria, Detritus, Diatoms, Haptophytes, Chlorophytes, Cryptophytes, Raphidophytes, Pelagophytes, Dinoflagellates, Ciliates, Copepods, `Copepod.Eggs...Nauplii`, `Metazoan.Host`, Other) %>%
distinct() %>%
pivot_longer(cols = c(Unknown, Bacteria, Detritus, Diatoms, Haptophytes, Chlorophytes, Cryptophytes, Raphidophytes, Pelagophytes, Dinoflagellates, Ciliates, Copepods, `Copepod.Eggs...Nauplii`, `Metazoan.Host`, Other), names_to = "PUTATIVE PREY", values_to = "option", values_drop_na = TRUE) %>%
mutate(PREY_EVIDENCE = case_when(
option == "+" ~ 1,
TRUE ~ NA
)) %>%
filter(!is.na(PREY_EVIDENCE)) %>% select(-option) %>% distinct() %>%
left_join(table_wstn_wdepth)Joining with `by = join_by(STN_NISKIN)`Bubble plot putative prey types
prey_types_df %>%
group_by(SUPERGROUP_CLASS, `PUTATIVE PREY`) %>%
summarise(SUM = sum(PREY_EVIDENCE)) %>%
ggplot(aes(y = SUPERGROUP_CLASS, x = `PUTATIVE PREY`, fill = `PUTATIVE PREY`)) +
geom_point(aes(size = SUM)) +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 90, size = 8,
hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = "black", size = 8),
plot.title = element_text(color = "black", size = 8, face = "bold"),
legend.position = "right") +
labs(x = "Prey", y = "Predator")`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by SUPERGROUP_CLASS and PUTATIVE PREY.
ℹ Output is grouped by SUPERGROUP_CLASS.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(SUPERGROUP_CLASS, PUTATIVE PREY))` for per-operation
grouping (`?dplyr::dplyr_by`) instead.
prey_types_df %>%
group_by(SUPERGROUP_CLASS, `PUTATIVE PREY`, DEPTH_BIN) %>%
summarise(SUM = sum(PREY_EVIDENCE)) %>%
mutate(DEPTH_BIN_ORDER = factor(DEPTH_BIN, levels = rev(depth_bin))) %>%
ggplot(aes(y = DEPTH_BIN_ORDER, x = `PUTATIVE PREY`, fill = `PUTATIVE PREY`)) +
geom_point(aes(size = SUM)) +
facet_grid(cols = vars(SUPERGROUP_CLASS)) +
theme_classic() +
theme(axis.text.x = element_text(color = "black", angle = 90, size = 8,
hjust = 1, vjust = 0.5),
axis.text.y = element_text(color = "black", size = 8),
plot.title = element_text(color = "black", size = 8, face = "bold"),
legend.position = "right") +
labs(x = "", y = "")`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by SUPERGROUP_CLASS, PUTATIVE PREY, and
DEPTH_BIN.
ℹ Output is grouped by SUPERGROUP_CLASS and PUTATIVE PREY.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(SUPERGROUP_CLASS, PUTATIVE PREY, DEPTH_BIN))` for
per-operation grouping (`?dplyr::dplyr_by`) instead.
head(trait_asv)# A tibble: 6 × 39
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
2 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
3 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
4 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
5 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
6 00170876c27b… Euka… Dinofla… Dinoflagellata-… TSAR-Dinoflagellata TSAR
# ℹ 33 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>, Phylum <chr>, Grazer.Taxonomic.ID <chr>,
# Synonym.Basionym <chr>, Feeding.Mode <chr>, Morphotype <chr>, ESD <chr>,
# Prey_MaxESD <chr>, Unknown <chr>, Bacteria <chr>, Detritus <chr>,
# Diatoms <chr>, Haptophytes <chr>, Chlorophytes <chr>, Cryptophytes <chr>,
# Raphidophytes <chr>, Pelagophytes <chr>, Dinoflagellates <chr>, …13 Network analysis
head(table_supp)# A tibble: 6 × 44
Station Niskin Depth Latitude Longitude Date Time_UTC DATE_GMT TIME_GMT
<int> <int> <dbl> <dbl> <dbl> <chr> <chr> <date> <chr>
1 1 5 18.6 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
2 1 10 2.48 28.8 89.5 7/30/20… 18:56:28 2023-07-30 18:56:28
3 2 1 353. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
4 2 3 269. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
5 2 6 150. 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
6 2 8 24.2 28.6 89.1 7/30/20… 21:36:00 2023-07-30 21:36:00
# ℹ 35 more variables: DAY_NIGHT <chr>, STN_ORDER <fct>, TRANSECT <fct>,
# DIST_OUTFLOW <dbl>, DEPTH_BIN <chr>, STN_NISKIN <chr>, OilRig_Count <int>,
# MLD <dbl>, SAL_MAX <dbl>, O2_MIN <dbl>, DCM <dbl>, STN_CATEGORY_3 <chr>,
# STN_CATEGORY_4 <chr>, CLUSTER <fct>, Watermass <chr>, WATER_MASS <chr>,
# SAL_THRESHOLD <chr>, MLD_THRESHOLD <chr>, DCM_THRESHOLD <chr>,
# O2_THRESHOLD <chr>, DIC <dbl>, NH4 <dbl>, NO2 <dbl>, NO3 <dbl>,
# Oxygen_CTD <dbl>, PO4 <dbl>, SIL <dbl>, Salinity <dbl>, TA <dbl>, …head(asv_long_avg_wtax_clean)# A tibble: 6 × 15
FeatureID Taxon DIVISION SUPERGROUP_CLASS Supergroup_simplified Supergroup
<chr> <chr> <chr> <chr> <chr> <chr>
1 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
2 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
3 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
4 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
5 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
6 000964551660… Euka… Radiola… Radiolaria-Acan… TSAR-Radiolaria TSAR
# ℹ 9 more variables: Division <chr>, Subdivision <chr>, Class <chr>,
# Order <chr>, Family <chr>, Genus <chr>, Species <chr>, STN_NISKIN <chr>,
# MEAN_REPS_seq <dbl>ASVs have to appear in at least 20% of the samples.
length(unique(table_supp$STN_NISKIN))[1] 90# length((table_supp$STN_NISKIN))
90*0.20[1] 18# Samples must be in at least 18 samples
tmp <- asv_long_avg_wtax_clean %>%
select(FeatureID, STN_NISKIN, MEAN_REPS_seq) %>% distinct() %>%
group_by(FeatureID) %>%
summarise(NUM_SAMPLES = n()) %>%
filter(NUM_SAMPLES >= 18)
# hist(tmp$NUM_SAMPLES)
length(tmp$FeatureID)[1] 1434# length(unique(tmp$FeatureID))
keep_ASVs_analysis <- as.character(unique(tmp$FeatureID))13.1 Import data as phyloseq object
# if(!requireNamespace("BiocManager")){
# install.packages("BiocManager")
# }
# BiocManager::install("phyloseq")
# library(phyloseq)phylo_asv_in <- asv_long_avg_wtax_clean %>%
filter(FeatureID %in% keep_ASVs_analysis) %>%
select(FeatureID, STN_NISKIN, MEAN_REPS_seq) %>% distinct() %>%
pivot_wider(names_from = STN_NISKIN, values_from = MEAN_REPS_seq, values_fill = 0) %>%
select((order(colnames(.)))) %>%
column_to_rownames(var = "FeatureID") %>%
as.matrix
phylo_tax_in <- asv_long_avg_wtax_clean %>%
filter(FeatureID %in% keep_ASVs_analysis) %>%
select(FeatureID, Taxon) %>% distinct() %>%
select((order(colnames(.)))) %>%
column_to_rownames(var = "FeatureID") %>%
as.matrix
# dim(phylo_tax_in)
# dim(phylo_asv_in)
# head(phylo_tax_in)
# head(phylo_asv_in)
# Feature IDs need to be in exact same order
# Set as phyloseq object
asv_table <- otu_table(phylo_asv_in, taxa_are_rows = TRUE)
tax_table <- tax_table(phylo_tax_in)
phylo_asv_tax <- phyloseq(asv_table, tax_table)13.2 SPIEC-EASI code
# save(phylo_asv_tax, file = "gom-asvs-for-network.R")SLURM script needs
# module load GCC/13.3.0 OpenMPI/5.0.3 R_tamu/4.4.2
# R --rtamuenvs=spiec_easi_apr26Rscript:
# install.packages(c('huge', 'pulsar', 'VGAM'))
# install.packages("../../SpiecEasi-1.1.1.tar.gz")
# if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
# BiocManager::install("SpiecEasi")
# library(SpiecEasi)
# Cross Domain approach
# se_GR <-spiec.easi(list(bac_phy, euk_phy), method = 'mb', nlambda = 40,
# lambda.min.ratio = 1e-2, pulsar.params = list(thresh = 0.05))
#
# # Check output getStability(se_GR) sum(getRefit(se_GR))/2
#
# # Extract weighted matrix se.beta <- as.matrix(symBeta(getOptBeta(se_GR)))
# df_beta <- as.data.frame(se.beta)
#
# # Extract adajency matrix adj.mat <- getRefit(se_GR) df_adj <-
# # as.data.frame(as.matrix(adj.mat))
#
# # Assign names from original dataframes colnames(df_beta) <-
# colnames(se_GR$est$data) colnames(df_adj) <- colnames(se_GR$est$data)
# row.names(df_adj) <- colnames(se_GR$est$data) row.names(df_beta) <-
# colnames(se_GR$est$data)
#
# # Save output
# save(df_adj, df_beta, se_GR, file = 'gr-spieceasi-output-20-08-2020.RData')Load spieceasi output:
load("input-data/spieceasi-output-04-30-2026.RData", verbose = TRUE)Loading objects:
df_adj
df_beta
seWarning: namespace 'SpiecEasi' is not available and has been replaced
by .GlobalEnv when processing object 'se'head(df_adj) 0031bd7a8b2eceaf8e92456e9cae584d
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0031bd7a8b2eceaf8e92456e9cae584d 0
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ffdde364b4a214b443c2c5f05244abd8
0031bd7a8b2eceaf8e92456e9cae584d 0
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006f5664d25385baa7bc38462247b4eb 0
008783681cef7d91238d312d6cb89eca 0
008c4bb09ae4bfcfbf1eb00cfb7463f5 0
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fffe762ccf23af75f41699f19f56dea2
0031bd7a8b2eceaf8e92456e9cae584d 0
003ff3e98dff52952a7036585a32c2f2 0
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008c4bb09ae4bfcfbf1eb00cfb7463f5 0
008d33f8013c49076f5a28a180b63e03 0hist(df_adj$`006f5664d25385baa7bc38462247b4eb`)
# head(tax_key)
tax_A <- tax_key
colnames(tax_A) <- paste("A_", colnames(tax_A), sep = "")
# head(tax_A)
tax_B <- tax_key
colnames(tax_B) <- paste("B_", colnames(tax_B), sep = "")
# head(tax_B)head(df_adj) 0031bd7a8b2eceaf8e92456e9cae584d
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ffdde364b4a214b443c2c5f05244abd8
0031bd7a8b2eceaf8e92456e9cae584d 0
003ff3e98dff52952a7036585a32c2f2 0
006f5664d25385baa7bc38462247b4eb 0
008783681cef7d91238d312d6cb89eca 0
008c4bb09ae4bfcfbf1eb00cfb7463f5 0
008d33f8013c49076f5a28a180b63e03 0
fffe762ccf23af75f41699f19f56dea2
0031bd7a8b2eceaf8e92456e9cae584d 0
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008d33f8013c49076f5a28a180b63e03 0df_adj %>%
rownames_to_column(var = "A_ASV") %>%
pivot_longer(cols = -c(A_ASV), names_to = "B_ASV", values_to = "ADJ") %>%
left_join(tax_A, by = c("A_ASV" = "A_FeatureID")) %>%
filter(ADJ == 1)# A tibble: 45,250 × 12
A_ASV B_ASV ADJ A_Taxon A_Supergroup A_Division A_Subdivision A_Class
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 0031bd7a8b… 02c4… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
2 0031bd7a8b… 05a4… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
3 0031bd7a8b… 0d6d… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
4 0031bd7a8b… 10a2… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
5 0031bd7a8b… 12b0… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
6 0031bd7a8b… 21ed… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
7 0031bd7a8b… 26c0… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
8 0031bd7a8b… 2c96… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
9 0031bd7a8b… 3f59… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
10 0031bd7a8b… 4aba… 1 Eukary… TSAR Rhizaria Radiolaria Polycy…
# ℹ 45,240 more rows
# ℹ 4 more variables: A_Order <chr>, A_Family <chr>, A_Genus <chr>,
# A_Species <chr>14 Session information
sessionInfo()R version 4.6.0 (2026-04-24)
Platform: aarch64-apple-darwin23
Running under: macOS Sequoia 15.7.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Los_Angeles
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] indicspecies_1.8.0 factoextra_2.0.0 cluster_2.1.8.2 anomalize_0.3.0
[5] treemapify_2.6.0 vegan_2.7-5 permute_0.9-10 viridis_0.6.5
[9] viridisLite_0.4.3 ggrepel_0.9.8 gt_1.3.0 ggupset_0.4.1
[13] patchwork_1.3.2 compositions_2.0-9 decontam_1.32.0 phyloseq_1.56.0
[17] lubridate_1.9.5 forcats_1.0.1 stringr_1.6.0 dplyr_1.2.1
[21] purrr_1.2.2 readr_2.2.0 tidyr_1.3.2 tibble_3.3.1
[25] ggplot2_4.0.3 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gridExtra_2.3 rlang_1.2.0 magrittr_2.0.5
[4] ade4_1.7-24 otel_0.2.0 compiler_4.6.0
[7] mgcv_1.9-4 systemfonts_1.3.2 vctrs_0.7.3
[10] reshape2_1.4.5 pkgconfig_2.0.3 crayon_1.5.3
[13] fastmap_1.2.0 backports_1.5.1 XVector_0.52.0
[16] labeling_0.4.3 utf8_1.2.6 rmarkdown_2.31
[19] ggfittext_0.10.3 tzdb_0.5.0 ragg_1.5.2
[22] xfun_0.58 jsonlite_2.0.0 biomformat_1.40.0
[25] broom_1.0.13 parallel_4.6.0 R6_2.6.1
[28] stringi_1.8.7 RColorBrewer_1.1-3 Rcpp_1.1.1-1.1
[31] Seqinfo_1.2.0 iterators_1.0.14 knitr_1.51
[34] IRanges_2.46.0 Matrix_1.7-5 splines_4.6.0
[37] igraph_2.3.2 timechange_0.4.0 tidyselect_1.2.1
[40] rstudioapi_0.19.0 yaml_2.3.12 codetools_0.2-20
[43] lattice_0.22-9 plyr_1.8.9 Biobase_2.72.0
[46] withr_3.0.2 S7_0.2.2 evaluate_1.0.5
[49] survival_3.8-6 bayesm_3.1-7 xml2_1.5.2
[52] Biostrings_2.80.1 pillar_1.11.1 tensorA_0.36.2.1
[55] foreach_1.5.2 stats4_4.6.0 generics_0.1.4
[58] S4Vectors_0.50.1 hms_1.1.4 scales_1.4.0
[61] glue_1.8.1 tools_4.6.0 ggnewscale_0.5.2
[64] robustbase_0.99-7 data.table_1.18.4 fs_2.1.0
[67] grid_4.6.0 ape_5.8-1 nlme_3.1-169
[70] cli_3.6.6 textshaping_1.0.5 svglite_2.2.2
[73] gtable_0.3.6 DEoptimR_1.2-0 sass_0.4.10
[76] digest_0.6.39 BiocGenerics_0.58.1 htmlwidgets_1.6.4
[79] farver_2.1.2 htmltools_0.5.9 multtest_2.68.0
[82] lifecycle_1.0.5 MASS_7.3-65