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06_DA_testing_Staph.R
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# General Information -----------------------------------------------------
# Authors: Mirja Thomsen & Axel Künstner
# Date: 2022-05-31
# changed: 2023-08-21 (revision)
# Libraries ---------------------------------------------------------------
library(phyloseq)
library(tidyverse)
library(corncob)
library(ANCOMBC)
library(patchwork)
# Get data ----------------------------------------------------------------
ps <- readRDS(file = "data/phyloseq.OTU.RDS")
sample_data(ps)$group <- gsub(pattern = "healthy",
replacement = "Healthy",
x = sample_data(ps)$group)
# Staph
stap <- openxlsx::read.xlsx(xlsxFile = "../data_sintax_skin_stool/staphOTUs.xlsx")
rownames(stap) <- stap$OTU
# Add Staph to tax table --------------------------------------------------
ps_staph <- subset_taxa(physeq = ps, Genus == "Staphylococcus" )
rownames(tax_table(ps_staph)) == stap$OTU
stap <- stap[ rownames(tax_table(ps_staph)), ]
rownames(tax_table(ps_staph)) == stap$OTU
tt <- tax_table(ps_staph) %>% data.frame()
tt$Species <- stap$Species
tax_table(ps_staph) <- as.matrix(tt)
# Setup -------------------------------------------------------------------
siglevel <- 0.1
TAXLEVEL <- 'Species'
my_da_list <- list()
location_table <- ps_staph %>%
sample_data %>%
pull(location) %>% table()
location_table
# abdomen
# frontPaw
# lips -> perilabial area
# tail -> ventral tail
# Process data ------------------------------------------------------------
# subsets
ps.ADpre_healthy <- subset_samples(ps_staph, group == "Healthy" | group == "ADpre")
ps.ADpost_ADpre <- subset_samples(ps_staph, group == "ADpost" | group == "ADpre")
ps.ADtreat_healthy <- subset_samples(ps_staph, group == "Healthy" | group == "ADtreatment")
sample_data(ps.ADpre_healthy)$group <- sample_data(ps.ADpre_healthy)$group %>%
fct_relevel(c("Healthy", "ADpre"))
sample_data(ps.ADpost_ADpre)$group <- sample_data(ps.ADpost_ADpre)$group %>%
fct_relevel(c("ADpre", "ADpost"))
sample_data(ps.ADtreat_healthy)$group <- sample_data(ps.ADtreat_healthy)$group %>%
fct_relevel(c("Healthy", "ADtreatment"))
# DA: abdomen ---------------------------------------------------------------
(test_location <- names( location_table )[1]) # "abdomen"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: axilla ---------------------------------------------------------------
(test_location <- names( location_table )[2]) # "axilla"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: ear ---------------------------------------------------------------
(test_location <- names( location_table )[3]) # "ear"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: elbow ---------------------------------------------------------------
(test_location <- names( location_table )[4]) # "elbow"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: flank ---------------------------------------------------------------
(test_location <- names( location_table )[5]) # "flank"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: frontPaw ---------------------------------------------------------------
(test_location <- names( location_table )[6]) # "frontPaw"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: groin ---------------------------------------------------------------
(test_location <- names( location_table )[7]) # "groin"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: hindPaw ---------------------------------------------------------------
(test_location <- names( location_table )[8]) # "hindPaw"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpost vs ADpre",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpost vs ADpre")]] <- da_results
# ADtreatment vs healthy
da_results <- NULL
ps_da <- ps.ADtreat_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE, neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADtreat vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADtreat vs Healthy")]] <- da_results
# DA: lips ---------------------------------------------------------------
(test_location <- names( location_table )[9]) # "lips"
# ADpre vs healthy
da_results <- NULL
ps_da <- ps.ADpre_healthy %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,
formula = "group+sex", group = "group",
p_adj_method = "BH", alpha = siglevel,
# zero_cut = 1, # no prev filtering necessary anymore
lib_cut = 0, struc_zero = TRUE,
neg_lb = FALSE, tol = 1e-5, max_iter = 10000,
conserve = FALSE, # TRUE if small sample sizes
global = FALSE
)
da_results <- data.frame(
Feature = da_ancombc$res$q_val[[1]],
Species = corncob::otu_to_taxonomy(OTU = da_ancombc$res$q_val[,1], data = ps_da, level = TAXLEVEL),
Location = test_location,
Comparison = "ADpre vs Healthy",
p_fdr = da_ancombc$res$q_val[[3]] %>% data.frame() %>% pull(),
Effect = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull(),
Error_min = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() -
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
Error_max = da_ancombc$res$lfc[[3]] %>% data.frame() %>% pull() +
da_ancombc$res$se[[3]] %>% data.frame() %>% pull(),
W = da_ancombc$res$W[[3]] %>% data.frame() %>% pull(),
diff_abn = da_ancombc$res$diff_abn[[3]] %>% data.frame() %>% pull(),
row.names = NULL
)
my_da_list[[paste0(test_location, " ADpre vs Healthy")]] <- da_results
# ADpost vs ADpre
da_results <- NULL
ps_da <- ps.ADpost_ADpre %>%
subset_samples(physeq = ., location == test_location) %>%
tax_glom(physeq = ., taxrank = "Species")
# ANCOM-BC
da_ancombc <- ancombc(
phyloseq = ps_da,