Feed-microbiome-host interactions in Atlantic salmon over life stages
Low dosage of betamannan
Shashank Gupta
2023-02-03
Step 1. Omics-Integration: Liver
Load packages
library("stringr")
library("dplyr")
library("DESeq2")
library("ggplot2")
library("tibble")
library("readr")
library("dplyr")
library("BiocParallel")
library("DT")
library("WGCNA")
library("cowplot")
library("gridExtra")
library("phyloseq")take_average = T
cluster_the_columns = F
col_cut <- 1
row_cut <- 1
prefix_genes <- "h"
prefix_OTUs <- "m"
filter.sig.mod <- 2Import omics data
wgcna_metatranscriptomics <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_MetaTranscriptomics.rds")
wgcna_transcriptomics_Liver <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_Transcriptomics_Liver.rds")
wgcna_metagenomics <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_Metagenomics.rds")
wgcna_metabolomics_HILIC <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_HILIC.rds")
wgcna_metabolomics_RP <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_RP.rds")
wgcna_metabolomics_Lipidomics <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_Lipidomics.rds")
wgcna_metaprotomics <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/stage2results_Proteomics.rds")
psdata <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/Input/psdata.rds")Function to remove and average modules
remove_and_average_MEs <- function(input_data, take_average) {
idx <- which(colnames(input_data$modules$MEs) == "ME0")
input_data$modules$MEs <- input_data$modules$MEs[,-idx]
if(take_average){
input_data$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_[0-9]{1,2}$", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> input_data_eigengenes
input_data_eigengenes <- as.data.frame(input_data_eigengenes)
} else{
input_data_eigengenes <- input_data$modules$MEs
}
rownames(input_data_eigengenes) <- input_data_eigengenes$common
input_data_eigengenes$common <- NULL
return(input_data_eigengenes)
}Step 2. Transcriptomics (Liver)
wgcna_transcriptomics_Liver_eigengenes <- remove_and_average_MEs(input_data = wgcna_transcriptomics_Liver, take_average = T)# Create a dendrogram of the transcriptomics eigengenes to organise the final plots.
X_ME_dendro <- hclust(as.dist(1 - WGCNA::bicor(wgcna_transcriptomics_Liver_eigengenes, maxPOutliers = 0.05)), method = "ward.D2")
# "..palettes that vary from one hue to another via white may have a more symmetrical appearance in RGB space"
if(take_average){
heatmap_colors <- colorRampPalette(c("#18b29f","#FFFFFF","#ac6721"), interpolate = "spline", space = "rgb")(51)
} else{
heatmap_colors <- colorRampPalette(c("#18b29f","#FFFFFF","#ac6721"), interpolate = "spline", space = "rgb")(51)
}
annotation_col <-
rownames(wgcna_transcriptomics_Liver_eigengenes) %>%
as.data.frame() %>%
dplyr::rename(common = ".") %>%
tidyr::separate(col = common, into = c("Time", "New_Diet"), remove = F, extra = "drop") %>% # If replicates are present, we drop them here
dplyr::select(common,"Time", "New_Diet") %>%
tibble::column_to_rownames(var = "common")
colnames(annotation_col) <- c("Time" , "New_Diet")
annotation_colors <- list(
Time = c(`T1` = "navy", `T2` = "darkgreen", `T3` = "red"),
New_Diet = c(`CTR` = "#1D88AF", `MC1` = "darkblue", `MC2` = "darkgreen", `MN3` = "#F08A46"))
wgcna_transcriptomics_Liver_eigengenes_to_plot <-
dplyr::inner_join(annotation_col %>%
rownames_to_column(var = "common"),
wgcna_transcriptomics_Liver_eigengenes %>%
rownames_to_column(var = "common"),
by = "common") %>%
#dplyr::arrange(Feed, Water, Day) %>% # The order at which the columns should appear, given that there is no clustering.
dplyr::select(common, starts_with("ME")) %>%
tibble::column_to_rownames(var = "common") %>%
t()
moduleLabels = wgcna_transcriptomics_Liver$modules$colors
moduleColors = labels2colors(wgcna_transcriptomics_Liver$modules$colors)
table(moduleLabels)## moduleLabels
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 21281 1014 790 649 632 485 433 416 409 396 387 373 353
## 13 14 15 16 17 18 19 20 21 22 23 24 25
## 336 306 289 260 258 246 230 214 207 206 200 192 172
## 26 27 28 29 30 31 32 33 34 35 36 37 38
## 167 159 152 152 151 134 113 111 107 105 101 100 95
## 39 40 41 42 43 44 45 46 47 48 49 50 51
## 92 91 89 87 75 74 73 70 64 58 51 51 51
## 52 53 54 55 56 57 58 59 60 61 62 63 64
## 50 49 49 49 48 48 47 47 46 45 40 40 40
## 65 66 67 68 69 70 71 72 73 74 75 76
## 39 37 36 36 32 31 31 23 23 22 21 20table(moduleColors)## moduleColors
## antiquewhite4 bisque4 black blue blue2
## 45 64 416 790 20
## brown brown2 brown4 coral1 coral2
## 649 21 70 46 40
## cyan darkgreen darkgrey darkmagenta darkolivegreen
## 306 206 192 107 111
## darkolivegreen4 darkorange darkorange2 darkred darkseagreen4
## 22 167 73 207 47
## darkslateblue darkturquoise firebrick4 floralwhite green
## 58 200 23 74 485
## greenyellow grey grey60 honeydew1 indianred4
## 373 21281 258 47 23
## ivory lavenderblush3 lightcoral lightcyan lightcyan1
## 75 48 31 260 87
## lightgreen lightpink4 lightsteelblue lightsteelblue1 lightyellow
## 246 48 31 89 230
## magenta maroon mediumorchid mediumpurple2 mediumpurple3
## 396 49 40 32 91
## midnightblue navajowhite2 orange orangered3 orangered4
## 289 49 172 36 92
## paleturquoise palevioletred3 pink plum plum1
## 134 49 409 36 95
## plum2 purple red royalblue saddlebrown
## 51 387 433 214 152
## salmon salmon4 sienna3 skyblue skyblue1
## 336 50 105 152 37
## skyblue2 skyblue3 steelblue tan thistle1
## 40 100 151 353 51
## thistle2 turquoise violet white yellow
## 51 1014 113 159 632
## yellow4 yellowgreen
## 39 101Transcriptomics heatmap
#heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
heatmap_colors <- colorRampPalette(c("#18b29f","#FFFFFF","#ac6721"), interpolate = "spline", space = "rgb")(51)
pheatmap::pheatmap(wgcna_transcriptomics_Liver_eigengenes_to_plot,
cluster_cols = F,
cluster_rows = X_ME_dendro,
treeheight_row = 20,
#cluster_rows = T,
cutree_rows = row_cut,
cutree_cols = col_cut,
color = heatmap_colors,
fontsize = 10,
fontsize_col = ifelse(take_average, 10, 6),
fontsize_row = 8,
annotation_colors = annotation_colors,
annotation_col = annotation_col,
show_colnames = F,
silent = F,annotation_legend = F,
labels_row = paste0(prefix_genes, rownames(wgcna_transcriptomics_Liver_eigengenes_to_plot)),
main = paste("Gene Module 'expression'\n", ifelse(take_average, "mean values", ""))) -> X_plot
Step 3. Metagenomics
wgcna_metagenomics_eigengenes <- remove_and_average_MEs(input_data = wgcna_metagenomics, take_average = T)
dim(wgcna_metagenomics_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 36 7## [1] 36 76Correlate modules from transcriptomics and metagenomics
# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metagenomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes))
if(!same_order){
wgcna_metagenomics_eigengenes <- wgcna_metagenomics_eigengenes[order(rownames(wgcna_metagenomics_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes <- wgcna_transcriptomics_Liver_eigengenes[order(rownames(wgcna_transcriptomics_Liver_eigengenes)),]
same_order <- all(rownames(wgcna_metagenomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples matchp.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metagenomics_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes),
colnames(wgcna_metagenomics_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes)){
for(j in 1:ncol(wgcna_metagenomics_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes[,i], wgcna_metagenomics_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes), colnames(wgcna_metagenomics_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Y_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)Metagenomics heatmap
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
wgcna_metagenomics$hubs %>%
as.data.frame() %>%
dplyr::rename("OTU_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubOTUs
otu_taxonomy <- data.frame(tax_table(psdata))
dplyr::left_join(hubOTUs,
(otu_taxonomy %>%
tibble::rownames_to_column(var = "OTU_name")),
by = "OTU_name") -> hubOTUs_Before
hubOTUs_Before$ModuleOTU <- paste0("ME" ,hubOTUs_Before$Module)
test <- Y_corr_X$correlation
hubOTUs_Before <- hubOTUs_Before[, c(10, 8)]
#hubOTUs_Before$Genus <- replace(hubOTUs_Before$Genus, hubOTUs_Before$Genus=="Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium", "Rhizobium")
hubOTUs_Before$Genus <- replace(hubOTUs_Before$Genus, hubOTUs_Before$Genus=="Burkholderia_Caballeronia_Paraburkholderia", "BCP")
hubOTUs_Before$Genus <- replace(hubOTUs_Before$Genus, hubOTUs_Before$Genus=="BBMC_4", "Family_Fibrobacterales")
hubOTUs_Before$Genus <- replace(hubOTUs_Before$Genus, hubOTUs_Before$Genus=="Prochlorococcus_MIT9313", "Prochlorococcus")
#match(hubOTUs_Before[, "ModuleOTU"], colnames(test))
colnames(test)[match(hubOTUs_Before[,"ModuleOTU"], colnames(test))] = hubOTUs_Before[,"Genus"]
Y_corr_X$correlation <- test
pheatmap::pheatmap(Y_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Y_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
show_rownames = F, legend = F,
labels_row = paste0(prefix_OTUs, rownames(Y_corr_X$correlation)),
labels_col = paste0(colnames(Y_corr_X$correlation)),
main = paste("Eigen\n", "ASVs" )) -> Y_corr_X_plot
Step 4. Traits formatting
#Z_traits_backup <- Z_traits
#Correlate transcriptomics with traits
sample_info <- read.csv("/Users/shashankgupta/Desktop/ImprovAFish/ImprovAFish/sample_info.csv", row.names = 1)
sample_info <- as_tibble(sample_info)
Z_traits <- sample_info %>% dplyr::select(ID_New, Time, New_Diet)
Z_traits <- Z_traits %>%
mutate(Time = factor(Time, levels = c("T1", "T2", "T3")),
New_Diet = factor(New_Diet),
Day = ifelse(Time == "T1", paste0("T1_", New_Diet),
ifelse(Time == "T2", paste0("T2_", New_Diet), paste0("T3_", New_Diet))))
Z_traits$Time <- factor(Z_traits$Time, levels = c("T1", "T2", "T3"))
Z_traits$Day <- factor(Z_traits$Day)
# One-hot encoding the nominal variables
nominal_variables <- Z_traits %>%
dplyr::select(one_of("Day", "New_Diet", "Time" )) %>%
model.matrix(~.-1, data = .) %>%
as_tibble()
ordinal_and_other_variables <- Z_traits %>%
dplyr::select(one_of("Day", "New_Diet", "Time" , "ID_New"))
Z_traits <- nominal_variables
row.names(Z_traits) <- ordinal_and_other_variables$ID_New
Z_traits$ID_New <- row.names(Z_traits)
Z_traits$TimeT1 <- ifelse(grepl("T1", Z_traits$ID_New), 1, 0)
Z_traits$New_Dietctr <- ifelse(grepl("ctr", Z_traits$ID_New), 1, 0)
row.names(Z_traits) <- Z_traits$ID_New
take_average <- TRUE
if (take_average) {
Z_traits <- Z_traits %>%
mutate(ID_New = sub("_[0-9]{1,2}$", "", ID_New)) %>%
group_by(ID_New) %>%
summarise_all(list(mean), na.rm = TRUE) %>% # Remove NA values
ungroup() %>%
tibble::column_to_rownames(var = "ID_New")
Z_traits <- as.data.frame(Z_traits)
}
Z_traits <- Z_traits[,grepl(c("New_Diet|Time"), colnames(Z_traits))]
Z_traits <- Z_traits[, c(1,2,3,6,4,5)]
colnames(Z_traits)[1:6] <- c("Diet_MC1", "Diet_MC2", "Diet_MN3", "Time_T1", "Time_T2", "Time_T3")# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(Z_traits) == rownames(wgcna_transcriptomics_Liver_eigengenes))
if(!same_order){
wgcna_metagenomics_eigengenes <- wgcna_metagenomics_eigengenes[order(rownames(wgcna_metagenomics_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes <- wgcna_transcriptomics_Liver_eigengenes[order(rownames(wgcna_transcriptomics_Liver_eigengenes)),]
same_order <- all(rownames(wgcna_metagenomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples matchp.value_matr <- corr.value_matr <- matrix(ncol = ncol(Z_traits),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes),
colnames(Z_traits)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes)){
for(j in 1:ncol(Z_traits)){
if(colnames(Z_traits)[j] == "Diet_MC1"){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes[,i], Z_traits[,j], method = "spearman", exact = FALSE)
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
} else{
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes[,i], Z_traits[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
}Traits heatmap
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes), colnames(Z_traits))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Z_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
pheatmap::pheatmap(Z_corr_X$correlation,
color = heatmap_colors,
cluster_cols = F,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Z_corr_X$signif_matrix,
fontsize_number = 8, # 10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
legend = F,
show_rownames = F) -> Z_corr_X_plot
Step 5. Module–trait associations
# Define numbers of genes and samples
omics_data_host <- readRDS("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/OmicsIntegration/omics_data_host_Liver.rds")
nGenes = ncol(omics_data_host);
nSamples = nrow(omics_data_host);
moduleLabels1 = wgcna_transcriptomics_Liver$modules$colors
moduleColors1 = labels2colors(wgcna_transcriptomics_Liver$modules$colors)
MEs = wgcna_transcriptomics_Liver$modules$MEs
table(moduleColors1)## moduleColors1
## antiquewhite4 bisque4 black blue blue2
## 45 64 416 790 20
## brown brown2 brown4 coral1 coral2
## 649 21 70 46 40
## cyan darkgreen darkgrey darkmagenta darkolivegreen
## 306 206 192 107 111
## darkolivegreen4 darkorange darkorange2 darkred darkseagreen4
## 22 167 73 207 47
## darkslateblue darkturquoise firebrick4 floralwhite green
## 58 200 23 74 485
## greenyellow grey grey60 honeydew1 indianred4
## 373 21281 258 47 23
## ivory lavenderblush3 lightcoral lightcyan lightcyan1
## 75 48 31 260 87
## lightgreen lightpink4 lightsteelblue lightsteelblue1 lightyellow
## 246 48 31 89 230
## magenta maroon mediumorchid mediumpurple2 mediumpurple3
## 396 49 40 32 91
## midnightblue navajowhite2 orange orangered3 orangered4
## 289 49 172 36 92
## paleturquoise palevioletred3 pink plum plum1
## 134 49 409 36 95
## plum2 purple red royalblue saddlebrown
## 51 387 433 214 152
## salmon salmon4 sienna3 skyblue skyblue1
## 336 50 105 152 37
## skyblue2 skyblue3 steelblue tan thistle1
## 40 100 151 353 51
## thistle2 turquoise violet white yellow
## 51 1014 113 159 632
## yellow4 yellowgreen
## 39 101table(moduleLabels1)## moduleLabels1
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 21281 1014 790 649 632 485 433 416 409 396 387 373 353
## 13 14 15 16 17 18 19 20 21 22 23 24 25
## 336 306 289 260 258 246 230 214 207 206 200 192 172
## 26 27 28 29 30 31 32 33 34 35 36 37 38
## 167 159 152 152 151 134 113 111 107 105 101 100 95
## 39 40 41 42 43 44 45 46 47 48 49 50 51
## 92 91 89 87 75 74 73 70 64 58 51 51 51
## 52 53 54 55 56 57 58 59 60 61 62 63 64
## 50 49 49 49 48 48 47 47 46 45 40 40 40
## 65 66 67 68 69 70 71 72 73 74 75 76
## 39 37 36 36 32 31 31 23 23 22 21 20# Recalculate MEs with color labels
MEs0 = moduleEigengenes(omics_data_host, moduleLabels1)$eigengenes
#MEs = orderMEs(MEs0)
DT::datatable(MEs0)MEs <- MEs0[, c(2:77)]
#colnames(MEs)<- paste("h", colnames(MEs), sep = "")
#host_gut_mapping_file <- read.table("../host_data/host_gut_mapping.txt", sep = "\t", row.names = 1, header = T)
sample_info1 <- read.csv("/Users/shashankgupta/Desktop/ImprovAFish/ImprovAFish/sample_info.csv", row.names = 1)
sample_info1[1:7] <- NULL
sample_info1$ID_New <- NULL
sample_info1$common <- NULL
# Find the samples that are present in both MEs and sample_info1
common_samples <- intersect(row.names(MEs), row.names(sample_info1))
# Subset MEs to keep only the common samples and reorder it
MEs <- MEs[common_samples, ]
reorder_idx <- match(row.names(MEs), row.names(sample_info1))
sample_info1 <- sample_info1[reorder_idx , ]
all(row.names(sample_info1) == row.names(MEs))## [1] TRUEmoduleTraitCor = cor(MEs, sample_info1, use = "p")
X_ME_dendro_row <- rownames(wgcna_transcriptomics_Liver_eigengenes_to_plot)[X_plot$tree_row$order]
moduleTraitCor <- moduleTraitCor[order(match(rownames(moduleTraitCor), X_ME_dendro_row)), ]
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)Trait heatmap
# Display the correlation values within a heatmap plot
labeledHeatmap(Matrix = moduleTraitCor,
xLabels = names(sample_info1),
yLabels = rownames(moduleTraitCor),
ySymbols =rownames(moduleTraitCor),
colorLabels = FALSE,
colors = blueWhiteRed(50),
textMatrix = textMatrix,
setStdMargins = FALSE,
cex.text = 0.5,
zlim = c(-1,1),
main = paste("Module-trait relationships"))signif_matrix <- rep("", length(moduleTraitPvalue))
three_star <- which( moduleTraitPvalue <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((moduleTraitPvalue <= 0.01) & (moduleTraitPvalue > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((moduleTraitPvalue <= 0.05) & (moduleTraitPvalue > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(moduleTraitPvalue) # Give textMatrix the correct dimensions
pheatmap::pheatmap(moduleTraitCor,
color = heatmap_colors,
yLabels = names(MEs),
ySymbols = names(MEs),
cluster_cols = F,
#treeheight_col = 0,
#treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = F,
#cutree_rows = row_cut,
display_numbers = signif_matrix,
fontsize_number = 8, # 10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
legend = F,
show_rownames = F,
main = "Traits and external variables") -> MT_plot
Step 6. MetaTranscriptomics
idx <- which(colnames(wgcna_metatranscriptomics$modules$MEs) == "ME0")
wgcna_metatranscriptomics$modules$MEs <- wgcna_metatranscriptomics$modules$MEs[,-idx]
take_average <- TRUE
if(take_average){
wgcna_metatranscriptomics$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_HGm[0-9]{1,2}$", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> wgcna_metatranscriptomics_eigengenes
wgcna_metatranscriptomics_eigengenes <- as.data.frame(wgcna_metatranscriptomics_eigengenes)
}
wgcna_metatranscriptomics_eigengenes$common <- gsub("mc1", "MC1", wgcna_metatranscriptomics_eigengenes$common)
wgcna_metatranscriptomics_eigengenes$common <- gsub("mc2", "MC2", wgcna_metatranscriptomics_eigengenes$common)
wgcna_metatranscriptomics_eigengenes$common <- gsub("mn3", "MN3", wgcna_metatranscriptomics_eigengenes$common)
wgcna_metatranscriptomics_eigengenes$common <- gsub("ctr", "CTR", wgcna_metatranscriptomics_eigengenes$common)
rownames(wgcna_metatranscriptomics_eigengenes) <- wgcna_metatranscriptomics_eigengenes$common
wgcna_metatranscriptomics_eigengenes$common <- NULL
dim(wgcna_metatranscriptomics_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 36 11## [1] 36 76table(rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metatranscriptomics_eigengenes))##
## TRUE
## 36wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes[rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metatranscriptomics_eigengenes), ]
table(rownames(wgcna_transcriptomics_Liver_eigengenes_New) %in% rownames(wgcna_metatranscriptomics_eigengenes))##
## TRUE
## 36#Correlate modules from transcriptomics and metagenomics.
# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metatranscriptomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
wgcna_metatranscriptomics_eigengenes <- wgcna_metatranscriptomics_eigengenes[order(rownames(wgcna_metatranscriptomics_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes_New[order(rownames(wgcna_transcriptomics_Liver_eigengenes_New)),]
same_order <- all(rownames(wgcna_metatranscriptomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples match#####
p.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metatranscriptomics_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes_New),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes_New),
colnames(wgcna_metatranscriptomics_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes_New)){
for(j in 1:ncol(wgcna_metatranscriptomics_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes_New[,i], wgcna_metatranscriptomics_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes_New), colnames(wgcna_metatranscriptomics_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
MetaTrans_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
#rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
#Annotation for Eigen Metatranscriptomics
meta_info <- read_tsv("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/MetaTranscriptomics/Total.DRAM.STx.bac.k2.total.annotations.tsv")
kraken_info <- read_tsv("/Users/shashankgupta/Desktop/ImprovAFish/github/ImprovaFish/MetaTranscriptomics/Kraken2_result.tsv")
rownames(meta_info) <- meta_info$...1
wgcna_metatranscriptomics$hubs %>%
as.data.frame() %>%
dplyr::rename("metatranscriptomics_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubmetatranscriptomics
hubmetatranscriptomics$new <- sub("^[^_]*_|_[^_]*$", "", hubmetatranscriptomics$metatranscriptomics_name)
hubmetatranscriptomics$new <- sub("_[^_]*$", "", hubmetatranscriptomics$new)
hubmetatranscriptomics_Before <- merge(hubmetatranscriptomics, kraken_info, by.y= "metatranscriptomics_name", by.x="new", all.x =T)
hubmetatranscriptomics_Before$Modulemetatranscriptomic <- paste0("ME" ,hubmetatranscriptomics_Before$Module)
hubmetatranscriptomics_Before$new <- NULL
hubmetatranscriptomics_Before$Module <- NULL
hubmetatranscriptomics_Before$kraken2 <- sub("\\(.*", "", hubmetatranscriptomics_Before$kraken2_taxonomy)
#Match with Eigen Metatranscriptomics
test <- MetaTrans_corr_X$correlation
#match(hubmetatranscriptomics_Before[, "Modulemetatranscriptomic"], colnames(test))
colnames(test)[match(hubmetatranscriptomics_Before[,"Modulemetatranscriptomic"], colnames(test))] = hubmetatranscriptomics_Before[,"kraken2"]
MetaTrans_corr_X$correlation <- testMetaTranscriptomics heatmap
pheatmap::pheatmap(MetaTrans_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = MetaTrans_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
show_rownames = F, legend = F,
labels_row = paste0(prefix_OTUs, rownames(MetaTrans_corr_X$correlation)),
labels_col = paste0(colnames(MetaTrans_corr_X$correlation)),
main = paste("Eigen\n", "Metatranscriptomics" )) -> MetaTrans_corr_X_plot
Step 7. MetaProteomics
#rownames(stage2results_Proteomics$modules$MEs) <- gsub("-MC1-TK1-", "_MC1_tk1_", rownames(stage2results_Proteomics$modules$MEs))
# Remove ME0 from analysis
idx <- which(colnames(wgcna_metaprotomics$modules$MEs) == "ME0")
wgcna_metaprotomics$modules$MEs <- wgcna_metaprotomics$modules$MEs[,-idx]
take_average<- TRUE
if(take_average){
wgcna_metaprotomics$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_HGm.*", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> wgcna_metaprotomics_eigengenes
wgcna_metaprotomics_eigengenes <- as.data.frame(wgcna_metaprotomics_eigengenes)
}
wgcna_metaprotomics_eigengenes$common <- gsub("mc1", "MC1", wgcna_metaprotomics_eigengenes$common)
wgcna_metaprotomics_eigengenes$common <- gsub("mc2", "MC2", wgcna_metaprotomics_eigengenes$common)
wgcna_metaprotomics_eigengenes$common <- gsub("mn3", "MN3", wgcna_metaprotomics_eigengenes$common)
wgcna_metaprotomics_eigengenes$common <- gsub("ctr", "CTR", wgcna_metaprotomics_eigengenes$common)
rownames(wgcna_metaprotomics_eigengenes) <- wgcna_metaprotomics_eigengenes$common
wgcna_metaprotomics_eigengenes$common <- NULL
dim(wgcna_metaprotomics_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 8 14## [1] 36 76table(rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metaprotomics_eigengenes))##
## FALSE TRUE
## 28 8wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes[rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metaprotomics_eigengenes), ]
table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) %in% rownames(wgcna_metaprotomics_eigengenes))##
## TRUE
## 8table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) == rownames(wgcna_metaprotomics_eigengenes))##
## TRUE
## 8#Correlate modules from transcriptomics and metagenomics.
# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metaprotomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
wgcna_metaprotomics_eigengenes <- wgcna_metaprotomics_eigengenes[order(rownames(wgcna_metaprotomics_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes_New[order(rownames(wgcna_transcriptomics_Liver_eigengenes_New)),]
same_order <- all(rownames(wgcna_metaprotomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples match#####
p.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metaprotomics_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes_New),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes_New),
colnames(wgcna_metaprotomics_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes_New)){
for(j in 1:ncol(wgcna_metaprotomics_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes_New[,i], wgcna_metaprotomics_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes_New), colnames(wgcna_metaprotomics_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Proteomics_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
#rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
wgcna_metaprotomics$hubs %>%
as.data.frame() %>%
dplyr::rename("Proteomics_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubProteomics
hubProteomics -> hubProteomics_Before
hubProteomics_Before$ModuleProteomics <- paste0("ME" ,hubProteomics_Before$Module)
test <- Proteomics_corr_X$correlation
hubProteomics_Before$Proteomics_name <- sub("XP_.*", "", hubProteomics_Before$Proteomics_name)
hubProteomics_Before$Proteomics_name <- sub("NP_.*", "", hubProteomics_Before$Proteomics_name)
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="cytochrome.c.oxidase.subunit.5B..mitochondrial", "Cytochrome c oxidase subunit 5B, mitochondrial")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="myosin.7.like.isoform.X1", "myosin-7 isoform X1")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="creatine.kinase.S.type..mitochondrial", "Creatine kinase S-type, mitochondrial")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="X3.hydroxyisobutyrate.dehydrogenase..mitochondrial", "3-hydroxyisobutyrate dehydrogenase, mitochondrial")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="LOW.QUALITY.PROTEIN..zonadhesin.like", "Zonadhesin like")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="cytosolic.non.specific.dipeptidase", "Cytosol nonspecific dipeptidas")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="protocadherin.Fat.4", "Protocadherin Fat 4")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="cadherin.17.precursor", "Cadherins 17 precursor")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="sushi.domain.containing.protein.2.isoform.X2", "Sushi domain-containing protein 2")
hubProteomics_Before$Proteomics_name <- replace(hubProteomics_Before$Proteomics_name, hubProteomics_Before$Proteomics_name=="intestinal.type.alkaline.phosphatase", "Intestinal-type alkaline phosphatase")
#match(hubProteomics_Before[, "ModuleProteomics"], colnames(test))
colnames(test)[match(hubProteomics_Before[,"ModuleProteomics"], colnames(test))] = hubProteomics_Before[,"Proteomics_name"]
Proteomics_corr_X$correlation <- testMetaProteomics heatmap
pheatmap::pheatmap(Proteomics_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Proteomics_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
show_rownames = F,
legend = F,
labels_row = paste0(prefix_OTUs, rownames(Proteomics_corr_X$correlation)),
labels_col = paste0(colnames(Proteomics_corr_X$correlation)),
main = paste("Eigen\n", "Meta-proteins" )
) -> Proteomics_corr_X_plot
MEs = moduleEigengenes(Proteomics_host, moduleLabels1)$eigengenes
MEs <- orderMEs(MEs)
module_order = names(MEs) %>% gsub("ME","", .)
MEs0 <- MEs
MEs0$Sample.ID = row.names(MEs)
mME = MEs0 %>%
pivot_longer(-Sample.ID) %>%
mutate(
name = gsub("ME", "", name),
name = factor(name, levels = module_order)
)
library(tidyverse)
setwd("/Users/shashankgupta/Desktop/ImprovAFish/ImprovAFish")
sample_info3 <- read.csv("sample_info.csv", row.names = 1)
sample_info3$Sample.ID <- sample_info3$ID_New
mME_meta <- merge(mME, sample_info3, by = "Sample.ID")
colnames(mME_meta)[2] <-"Module"
mME_meta$New_Diet <- factor(mME_meta$New_Diet, levels = c("CTR", "MC1", "MC2", "MN3"))
mME_meta$Module <- factor(mME_meta$Module, levels = c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20","21","22","23", "24"))
expression_plots<-mME_meta%>%
# group_by(Module) %>%
ggplot(aes(x=Time, y=value, fill=New_Diet)) +
facet_wrap(~ Module)+
ylab("Mean Module Eigenegene Value") +
geom_hline(yintercept = 0, linetype="dashed", color = "grey")+
geom_boxplot(width=.5, outlier.shape= NA, position = position_dodge(width = 0.5), alpha = 0.7) +
stat_summary(fun=mean, geom="line", aes(New_Diet=New_Diet, color = New_Diet), position = position_dodge(width = 0.5)) +
geom_point(pch = 21, position = position_dodge(width = 0.5)) +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3")) +
scale_color_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3")) +
#xlab("Hours Post-Fertilization") + #Axis titles
theme_bw() + theme(panel.border = element_rect(color="black", fill=NA, size=0.75), panel.grid.major = element_blank(), #Makes background theme white
panel.grid.minor = element_blank(), axis.line = element_blank()) +
theme(axis.text = element_text(size = 11 , color = "black"),
axis.title = element_text(size = 16, color = "black"),
axis.text.x = element_text(size=11, color="black"),
legend.title=element_blank(),
legend.text=element_text(color="black", size=12)); expression_plotsStep 8. Metabolomics
HILIC
rownames(wgcna_metabolomics_HILIC$modules$MEs) <- gsub("-MC1-TK1-", "_mc1_tk1_", rownames(wgcna_metabolomics_HILIC$modules$MEs))
# Remove ME0 from analysis
idx <- which(colnames(wgcna_metabolomics_HILIC$modules$MEs) == "ME0")
wgcna_metabolomics_HILIC$modules$MEs <- wgcna_metabolomics_HILIC$modules$MEs[,-idx]
if(take_average){
wgcna_metabolomics_HILIC$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_VFA.*", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> wgcna_metabolomics_HILIC_eigengenes
wgcna_metabolomics_HILIC_eigengenes <- as.data.frame(wgcna_metabolomics_HILIC_eigengenes)
}
wgcna_metabolomics_HILIC_eigengenes$common <- gsub("mc1", "MC1", wgcna_metabolomics_HILIC_eigengenes$common)
wgcna_metabolomics_HILIC_eigengenes$common <- gsub("mc2", "MC2", wgcna_metabolomics_HILIC_eigengenes$common)
wgcna_metabolomics_HILIC_eigengenes$common <- gsub("mn3", "MN3", wgcna_metabolomics_HILIC_eigengenes$common)
wgcna_metabolomics_HILIC_eigengenes$common <- gsub("ctr", "CTR", wgcna_metabolomics_HILIC_eigengenes$common)
rownames(wgcna_metabolomics_HILIC_eigengenes) <- wgcna_metabolomics_HILIC_eigengenes$common
wgcna_metabolomics_HILIC_eigengenes$common <- NULL
dim(wgcna_metabolomics_HILIC_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 24 2## [1] 36 76table(rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_HILIC_eigengenes))##
## FALSE TRUE
## 12 24wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes[rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_HILIC_eigengenes), ]
table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) %in% rownames(wgcna_metabolomics_HILIC_eigengenes))##
## TRUE
## 24# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metabolomics_HILIC_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
wgcna_metabolomics_HILIC_eigengenes <- wgcna_metabolomics_HILIC_eigengenes[order(rownames(wgcna_metabolomics_HILIC_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes_New[order(rownames(wgcna_transcriptomics_Liver_eigengenes_New)),]
same_order <- all(rownames(wgcna_metabolomics_HILIC_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples match#####
p.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metabolomics_HILIC_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes_New),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes_New),
colnames(wgcna_metabolomics_HILIC_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes_New)){
for(j in 1:ncol(wgcna_metabolomics_HILIC_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes_New[,i], wgcna_metabolomics_HILIC_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes_New), colnames(wgcna_metabolomics_HILIC_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Metabolites_1_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
wgcna_metabolomics_HILIC$hubs %>%
as.data.frame() %>%
dplyr::rename("metabolites_HILIC_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubmetabolites_HILIC
hubmetabolites_HILIC -> hubmetabolites_HILIC_Before
hubmetabolites_HILIC_Before$Modulemetabolites_HILIC <- paste0("ME" ,hubmetabolites_HILIC_Before$Module)
test <- Metabolites_1_corr_X$correlation
#hubmetabolites_HILIC_Before$metabolites_HILIC_name <- replace(hubmetabolites_HILIC_Before$metabolites_HILIC_name, hubmetabolites_HILIC_Before$metabolites_HILIC_name=="Ile.Leu...244.17825.Da.264.70.s", "Ile-Leu")
#hubmetabolites_HILIC_Before$metabolites_HILIC_name <- replace(hubmetabolites_HILIC_Before$metabolites_HILIC_name, hubmetabolites_HILIC_Before$metabolites_HILIC_name=="Di.N.octyl.phthalate...390.27623.Da.707.55.s", "Di-N-octylphthalate")
hubmetabolites_HILIC_Before$metabolites_HILIC_name <- replace(hubmetabolites_HILIC_Before$metabolites_HILIC_name, hubmetabolites_HILIC_Before$metabolites_HILIC_name=="Asp.Asp_248.06507Da665.09s", "Asp-Asp")
hubmetabolites_HILIC_Before$metabolites_HILIC_name <- replace(hubmetabolites_HILIC_Before$metabolites_HILIC_name, hubmetabolites_HILIC_Before$metabolites_HILIC_name=="Glu.Thr_248.10146Da623.48s", "Glu-Thr")
#colnames(test)[1:2] <- c("D-Arabinonic acid", "Thr-Thr")
match(hubmetabolites_HILIC_Before[, "Modulemetabolites_HILIC"], colnames(test))## [1] 1 2colnames(test)[match(hubmetabolites_HILIC_Before[,"Modulemetabolites_HILIC"], colnames(test))] = hubmetabolites_HILIC_Before[,"metabolites_HILIC_name"]
Metabolites_1_corr_X$correlation <- test
pheatmap::pheatmap(Metabolites_1_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Metabolites_1_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
legend = F,
show_rownames = F,
labels_row = paste0(prefix_OTUs, rownames(Metabolites_1_corr_X$correlation)),
labels_col = paste0(colnames(Metabolites_1_corr_X$correlation)),
#main = "Eigen metabolites_HILIC"
) -> Metabolites_1_corr_X_plot
RP
rownames(wgcna_metabolomics_RP$modules$MEs) <- gsub("-MC1-TK1-", "_mc1_tk1_", rownames(wgcna_metabolomics_RP$modules$MEs))
# Remove ME0 from analysis
idx <- which(colnames(wgcna_metabolomics_RP$modules$MEs) == "ME0")
wgcna_metabolomics_RP$modules$MEs <- wgcna_metabolomics_RP$modules$MEs[,-idx]
if(take_average){
wgcna_metabolomics_RP$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_VFA.*", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> wgcna_metabolomics_RP_eigengenes
wgcna_metabolomics_RP_eigengenes <- as.data.frame(wgcna_metabolomics_RP_eigengenes)
}
wgcna_metabolomics_RP_eigengenes$common <- gsub("mc1", "MC1", wgcna_metabolomics_RP_eigengenes$common)
wgcna_metabolomics_RP_eigengenes$common <- gsub("mc2", "MC2", wgcna_metabolomics_RP_eigengenes$common)
wgcna_metabolomics_RP_eigengenes$common <- gsub("mn3", "MN3", wgcna_metabolomics_RP_eigengenes$common)
wgcna_metabolomics_RP_eigengenes$common <- gsub("ctr", "CTR", wgcna_metabolomics_RP_eigengenes$common)
rownames(wgcna_metabolomics_RP_eigengenes) <- wgcna_metabolomics_RP_eigengenes$common
wgcna_metabolomics_RP_eigengenes$common <- NULL
dim(wgcna_metabolomics_RP_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 24 3## [1] 36 76table(rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_RP_eigengenes))##
## FALSE TRUE
## 12 24wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes[rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_RP_eigengenes), ]
table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) %in% rownames(wgcna_metabolomics_RP_eigengenes))##
## TRUE
## 24# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metabolomics_RP_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
wgcna_metabolomics_RP_eigengenes <- wgcna_metabolomics_RP_eigengenes[order(rownames(wgcna_metabolomics_RP_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes_New[order(rownames(wgcna_transcriptomics_Liver_eigengenes_New)),]
same_order <- all(rownames(wgcna_metabolomics_RP_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples match#####
p.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metabolomics_RP_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes_New),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes_New),
colnames(wgcna_metabolomics_RP_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes_New)){
for(j in 1:ncol(wgcna_metabolomics_RP_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes_New[,i], wgcna_metabolomics_RP_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes_New), colnames(wgcna_metabolomics_RP_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Metabolites_2_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
wgcna_metabolomics_RP$hubs %>%
as.data.frame() %>%
dplyr::rename("metabolites_RP_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubmetabolites_RP
hubmetabolites_RP -> hubmetabolites_RP_Before
hubmetabolites_RP_Before$Modulemetabolites_RP <- paste0("ME" ,hubmetabolites_RP_Before$Module)
test <- Metabolites_2_corr_X$correlation
hubmetabolites_RP_Before$metabolites_RP_name <- replace(hubmetabolites_RP_Before$metabolites_RP_name, hubmetabolites_RP_Before$metabolites_RP_name=="Ile.Leu...244.17825.Da.264.70.s", "Ile-Leu")
hubmetabolites_RP_Before$metabolites_RP_name <- replace(hubmetabolites_RP_Before$metabolites_RP_name, hubmetabolites_RP_Before$metabolites_RP_name=="Di.N.octyl.phthalate...390.27623.Da.707.55.s", "Di-N-octylphthalate")
hubmetabolites_RP_Before$metabolites_RP_name <- replace(hubmetabolites_RP_Before$metabolites_RP_name, hubmetabolites_RP_Before$metabolites_RP_name=="X2_.Deoxyadenosine...251.10146.Da.111.43.s", "2-Deoxyadenosine")
match(hubmetabolites_RP_Before[, "Modulemetabolites_RP"], colnames(test))## [1] 2 1 3colnames(test)[match(hubmetabolites_RP_Before[,"Modulemetabolites_RP"], colnames(test))] = hubmetabolites_RP_Before[,"metabolites_RP_name"]
Metabolites_2_corr_X$correlation <- testpheatmap::pheatmap(Metabolites_2_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Metabolites_2_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
legend = F,
show_rownames = F,
labels_row = paste0(prefix_OTUs, rownames(Metabolites_2_corr_X$correlation)),
labels_col = paste0(colnames(Metabolites_2_corr_X$correlation)),
main = paste("Eigen\n", "Metabolites" )
) -> Metabolites_2_corr_X_plot
Lipidomics
if(take_average){
wgcna_metabolomics_Lipidomics$modules$MEs %>%
rownames_to_column(var = "common") %>%
mutate(common = sub("_VFA.*", "", common)) %>%
group_by(common) %>%
summarise_all(list(mean)) %>%
ungroup() -> wgcna_metabolomics_Lipidomics_eigengenes
wgcna_metabolomics_Lipidomics_eigengenes <- as.data.frame(wgcna_metabolomics_Lipidomics_eigengenes)
}
wgcna_metabolomics_Lipidomics_eigengenes$common <- gsub("mc1", "MC1", wgcna_metabolomics_Lipidomics_eigengenes$common)
wgcna_metabolomics_Lipidomics_eigengenes$common <- gsub("mc2", "MC2", wgcna_metabolomics_Lipidomics_eigengenes$common)
wgcna_metabolomics_Lipidomics_eigengenes$common <- gsub("mn3", "MN3", wgcna_metabolomics_Lipidomics_eigengenes$common)
wgcna_metabolomics_Lipidomics_eigengenes$common <- gsub("ctr", "CTR", wgcna_metabolomics_Lipidomics_eigengenes$common)
rownames(wgcna_metabolomics_Lipidomics_eigengenes) <- wgcna_metabolomics_Lipidomics_eigengenes$common
wgcna_metabolomics_Lipidomics_eigengenes$common <- NULL
dim(wgcna_metabolomics_Lipidomics_eigengenes); dim(wgcna_transcriptomics_Liver_eigengenes)## [1] 24 2## [1] 36 76table(rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_Lipidomics_eigengenes))##
## FALSE TRUE
## 12 24wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes[rownames(wgcna_transcriptomics_Liver_eigengenes) %in% rownames(wgcna_metabolomics_Lipidomics_eigengenes), ]
table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) %in% rownames(wgcna_metabolomics_Lipidomics_eigengenes))##
## TRUE
## 24table( rownames(wgcna_transcriptomics_Liver_eigengenes_New) == rownames(wgcna_metabolomics_Lipidomics_eigengenes))##
## TRUE
## 24#Correlate modules from transcriptomics and metagenomics.
# Check that the samples are in the same order.
# If they are not in order, change their order to match; If they do not match one-to-one, call an error.
same_order <- all(rownames(wgcna_metabolomics_Lipidomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
wgcna_metabolomics_Lipidomics_eigengenes <- wgcna_metabolomics_Lipidomics_eigengenes[order(rownames(wgcna_metabolomics_Lipidomics_eigengenes)),]
wgcna_transcriptomics_Liver_eigengenes_New <- wgcna_transcriptomics_Liver_eigengenes_New[order(rownames(wgcna_transcriptomics_Liver_eigengenes_New)),]
same_order <- all(rownames(wgcna_metabolomics_Lipidomics_eigengenes) == rownames(wgcna_transcriptomics_Liver_eigengenes_New))
if(!same_order){
stop("Sample names do not match. Samples should be identical.", call. = F)
}
} else{cat("Samples match")}## Samples match#####
p.value_matr <- corr.value_matr <- matrix(ncol = ncol(wgcna_metabolomics_Lipidomics_eigengenes),
nrow = ncol(wgcna_transcriptomics_Liver_eigengenes_New),
dimnames = list(colnames(wgcna_transcriptomics_Liver_eigengenes_New),
colnames(wgcna_metabolomics_Lipidomics_eigengenes)))
for(i in 1:ncol(wgcna_transcriptomics_Liver_eigengenes_New)){
for(j in 1:ncol(wgcna_metabolomics_Lipidomics_eigengenes)){
cor.res <- cor.test(wgcna_transcriptomics_Liver_eigengenes_New[,i], wgcna_metabolomics_Lipidomics_eigengenes[,j])
p.value_matr[i, j] <- cor.res$p.value
corr.value_matr[i, j] <- cor.res$estimate
}
}
# Correct for number of tests
p.value_matr.adjust <- p.adjust(p.value_matr, method = "fdr")
dim(p.value_matr.adjust) <- dim(p.value_matr)
dimnames(p.value_matr.adjust) <- list(colnames(wgcna_transcriptomics_Liver_eigengenes_New), colnames(wgcna_metabolomics_Lipidomics_eigengenes))
# Add significance level.
# One star means a p-value of less than 0.05; Two stars is less than 0.01, and three, is less than 0.001.
signif_matrix <- rep("", length(p.value_matr))
three_star <- which( p.value_matr <= 0.001)
signif_matrix[three_star] <- "***"
two_star <- which((p.value_matr <= 0.01) & (p.value_matr > 0.001))
signif_matrix[two_star] <- "**"
one_star <- which((p.value_matr <= 0.05) & (p.value_matr > 0.01))
signif_matrix[one_star] <- "*"
dim(signif_matrix) = dim(p.value_matr) # Give textMatrix the correct dimensions
# Collect all results into a list.
Lipidomics_corr_X <- list(p_value = p.value_matr,
p_value_adj = p.value_matr.adjust,
signif_matrix = signif_matrix,
correlation = corr.value_matr)
rm(p.value_matr, p.value_matr.adjust, signif_matrix, corr.value_matr)
heatmap_colors <- colorRampPalette(rev(RColorBrewer::brewer.pal(n = 6, name ="RdBu")))(51)
wgcna_metabolomics_Lipidomics$hubs %>%
as.data.frame() %>%
dplyr::rename("Lipidomics_name" = ".") %>%
tibble::rownames_to_column(var = "Module") -> hubLipidomics
hubLipidomics -> hubLipidomics_Before
hubLipidomics_Before$ModuleLipidomics <- paste0("ME" ,hubLipidomics_Before$Module)
test <- Lipidomics_corr_X$correlation
hubLipidomics_Before$Lipidomics_name <- replace(hubLipidomics_Before$Lipidomics_name, hubLipidomics_Before$Lipidomics_name=="X1.Oleoyl.sn.glycero.3.phosphocholine", "1_Oleoyl_sn_glycero_3_phosphocholine")
match(hubLipidomics_Before[, "ModuleLipidomics"], colnames(test))## [1] 1colnames(test)[match(hubLipidomics_Before[,"ModuleLipidomics"], colnames(test))] = hubLipidomics_Before[,"Lipidomics_name"]
Lipidomics_corr_X$correlation <- test
pheatmap::pheatmap(Lipidomics_corr_X$correlation,
color = heatmap_colors,
treeheight_col = 0,
treeheight_row = 0, # will be shown on the transcriptomics ME heatmap
cluster_rows = X_ME_dendro,
cutree_rows = row_cut,
display_numbers = Lipidomics_corr_X$signif_matrix,
fontsize_number = 8, #10
breaks = seq(from = -1, to = 1, length.out = 51),
silent = F,
show_rownames = F,
labels_row = paste0(prefix_OTUs, rownames(Lipidomics_corr_X$correlation)),
labels_col = paste0(colnames(Lipidomics_corr_X$correlation)),
#main = "Eigen Lipidomics"
) -> Lipidomics_corr_X_plot
Step 9. Integrated heatmap
cowplot::plot_grid(MT_plot$gtable,
Z_corr_X_plot$gtable,
X_plot$gtable,
MetaTrans_corr_X_plot$gtable,
Y_corr_X_plot$gtable,
Proteomics_corr_X_plot$gtable,
Metabolites_1_corr_X_plot$gtable,
Metabolites_2_corr_X_plot$gtable,
Lipidomics_corr_X_plot$gtable,
ncol = 9,
align = 'h',
rel_widths = c(1.4, 0.6, 4.5, 1.2, 0.8, 1.5 , 0.3, 0.3, 0.3)) +
ggplot2::theme(plot.margin = ggplot2::unit(c(8,3,7,2), "cm"))
```