In ningzhibin/rmdocpu: Metalab report upon provided templates
htmltools::img(src = "https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/rmd/iMetaReport.png",
alt = 'logo',
style = 'position:absolute; top:0; right:0; padding:10px;width:150px;height:150px;')
source("https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/subfunctions_general.r")
source("https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/subfunctions_general_update.r")
# enviroment setup
knitr::opts_chunk$set(echo = FALSE,warning = FALSE, message = FALSE, cache = FALSE)
library(tidyverse)
library(ggplot2)
library(plotly)
library(readxl)
library(pheatmap)
library(reshape2)
library(vegan)
library(ggdendro)
# library(gridExtra)
#library(plotly)
# library(DT)
# for interactive data table display
# local usage test and debug
# rmarkdown::render("MQ_report_function_ocpu.Rmd", params = list(summary_file_tbl = your_readin_tbl))
# on local machine, your_readin_tbl is a data table, while on ocpu, your_readin_tbl is a json formatted table
# todo
# check the metafile, if meta is not qualified, do not use it.
# version control
# 20190821 created function rmd
# 20190904 added heatmap and PCA, can show meta info if provided
# input
if(is.null(params$input_datatable)){
# test with local test with local files in the same dir,
data_fun <- read.csv("functions.csv", header = TRUE, sep = ",")
}else{
# opencpu render from data table by parametrized input
data_fun <- params$input_datatable
}
# Note: The folling analysis with meta info assumes that
# 1st columns as sample name, 2nd column as experiment name, 3rd column and after as grouping
meta_table <- params$meta_table
# if there is any null value in the meta colum, set the meta as NULL
if(any(is.na(meta_table$meta1))){
meta_table <- NULL
}
#meta_table <- read.delim("metadata.txt", header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
Intro
This report provides some basic description and visualization of the MetaLab function results.
The report is based on the function.csv, without defined experimental design/grouping information.
Users can use this to quickly check the functional profile of the dataset.
Sample overview
Protein groups annotation | Number (percentage)
--------------------- | -----------------------
Protein groups in your sample | r nrow(data_fun)
Protein groups with COG annotation: | r paste(nrow(data_fun[data_fun$COG.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$COG.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with NOG annotation: | r paste(nrow(data_fun[data_fun$NOG.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$NOG.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with KEGG annotation: | r paste(nrow(data_fun[data_fun$KEGG.name != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$KEGG.name != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOBP annotation: | r paste(nrow(data_fun[data_fun$GOBP.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOBP.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOCC annotation: | r paste(nrow(data_fun[data_fun$GOCC.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOCC.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOMF annotation: | r paste(nrow(data_fun[data_fun$GOMF.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOMF.accession != "",])/nrow(data_fun)),"%"),")")
Unique COG accessions annotated: | r length(unique(data_fun$COG.accession))-1
Unique NOG accessions annotated: | r length(unique(data_fun$NOG.accession))-1
Unique KEGG accessions annotated: | r length(unique(data_fun$KEGG.accession))-1
Meta/grouping info provided: | r if( !is.null(meta_table)){unique(meta_table[,3])}else{ "No meta information provided"}
Overview with pie charts
Overview of COG categories
The figure below displays the composition of COG categories in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Get the columns out of the table
if(any(grepl("intensity.", colnames(data_fun), ignore.case=TRUE))){
intensity_columns <- data_fun[,grep("intensity.", colnames(data_fun), ignore.case=TRUE), drop = FALSE]
colnames(intensity_columns)<-gsub("intensity.", "", colnames(intensity_columns), ignore.case=TRUE)
}
# Calculate subtotal for each category
intensity_columns_C <- cbind(data_fun$COG.category, intensity_columns)
data_fun_COG <- aggregate(. ~ data_fun$COG.category, data = intensity_columns_C[,-1, drop = FALSE], FUN = sum)
data_fun_COG$`data_fun$COG.category` <- as.character(data_fun_COG$`data_fun$COG.category`)
data_fun_COG$`data_fun$COG.category`[1] <- "Unmatched"
data_fun_COG <- data_fun_COG[which(rowSums(data_fun_COG[,-1,drop = FALSE])>0),]
# Prepare data for plotting
Intensity <- rowSums(data_fun_COG[,-1, drop = FALSE])
rowSum_COG <- as.data.frame(Intensity)
COG_cat <- data_fun_COG[,1]
rowSum_COG <- cbind(COG_cat, rowSum_COG)
#Draw pie chart
plot_ly(rowSum_COG, labels = ~COG_cat, values = ~Intensity, type = 'pie') %>%
layout(title = 'Composition of COG categories',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Overview of NOG categories
The figure below displays the composition of NOG categories in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Calculate subtotal for each category
intensity_columns_N <- cbind(data_fun$NOG.category, intensity_columns)
data_fun_NOG <- aggregate(. ~ data_fun$NOG.category, data = intensity_columns_N[,-1,drop = FALSE], FUN = sum)
data_fun_NOG$`data_fun$NOG.category` <- as.character(data_fun_NOG$`data_fun$NOG.category`)
data_fun_NOG$`data_fun$NOG.category`[1] <- "Unmatched"
data_fun_NOG <- data_fun_NOG[which(rowSums(data_fun_NOG[,-1,drop = FALSE])>0),]
# Prepare data for plotting
IntensityN <- rowSums(data_fun_NOG[,-1,drop = FALSE])
rowSum_NOG <- as.data.frame(IntensityN)
NOG_cat <- data_fun_NOG[,1]
rowSum_NOG <- cbind(NOG_cat, rowSum_NOG)
#Draw pie chart
plot_ly(rowSum_NOG, labels = ~NOG_cat, values = ~IntensityN, type = 'pie') %>%
layout(title = 'Composition of NOG categories',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Overview of KEGG pathways
The figure below displays the composition of KEGG pathways in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Calculate subtotal for each category
intensity_columns_K <- cbind(data_fun$KEGG.name, intensity_columns)
data_fun_KEGG <- aggregate(. ~ data_fun$KEGG.name, data = intensity_columns_K[,-1,drop = FALSE], FUN = sum)
data_fun_KEGG$`data_fun$KEGG.name` <- as.character(data_fun_KEGG$`data_fun$KEGG.name`)
data_fun_KEGG$`data_fun$KEGG.name`[1] <- "Unmatched"
data_fun_KEGG <- data_fun_KEGG[which(rowSums(data_fun_KEGG[,-1,drop = FALSE])>0),]
# Prepare data for plotting
IntensityK <- rowSums(data_fun_KEGG[,-1,drop = FALSE])
rowSum_KEGG <- as.data.frame(IntensityK)
KEGG_cat <- data_fun_KEGG[,1]
rowSum_KEGG <- cbind(KEGG_cat, rowSum_KEGG)
#Draw pie chart
plot_ly(rowSum_KEGG, labels = ~KEGG_cat, values = ~IntensityK, type = 'pie') %>%
layout(title = 'Composition of KEGG pathways',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Functional composition in each sample
COG composition in each sample
The figure below displays the composition of COG categories in each of your sample. Intensities in the bar plot are based on summed proteinGroup intensities corresponding to each category.
# Prepare data for plotting
colnames(data_fun_COG)[colnames(data_fun_COG)=="data_fun$COG.category"] <- "Name"
data_fun_COG_gg <- melt(data = data_fun_COG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity")
#Draw stacked column bar
ggplotly(ggplot(data_fun_COG_gg, aes(x = Sample, y = Intensity, fill = Name)) +
geom_bar(stat='identity') + theme_bw() +
ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
) %>% style(legendgroup = NULL)
NOG composition in each sample
Similar to the COG summary, the figure below displays the composition of NOG categories in each of your sample.
# Prepare data for plotting
colnames(data_fun_NOG)[colnames(data_fun_NOG)=="data_fun$NOG.category"] <- "Name"
data_fun_NOG_gg <- melt(data = data_fun_NOG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity")
#Draw stacked column bar
ggplotly(ggplot(data_fun_NOG_gg, aes(x = Sample, y = Intensity, fill = Name)) +
geom_bar(stat='identity') + theme_bw() +
ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
)
KEGG composition in each sample
The figure below displays the composition of KEGG pathways in each of your sample.
# Prepare data for plotting
colnames(data_fun_KEGG)[colnames(data_fun_KEGG)=="data_fun$KEGG.name"] <- "Name"
data_fun_KEGG_gg <- melt(data = data_fun_KEGG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity")
#Draw stacked column bar
ggplotly(ggplot(data_fun_KEGG_gg, aes(x = Sample, y = Intensity, fill = Name)) +
geom_bar(stat='identity') + theme_bw() +
ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
)
r if(ncol(data_fun_COG) > 2){ "# Heatmap of functional composition"}
r if(ncol(data_fun_COG) > 2){ "## Heatmap of COG composition"}
if(ncol(data_fun_COG) > 2){
rownames(data_fun_COG) <- data_fun_COG[,1]
if(is.null(meta_table)){
d3heatmap::d3heatmap(data_fun_COG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row")
}else{
meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used
rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE])
pheatmap::pheatmap(data_fun_COG[,-1,drop = FALSE], annotation_col = meta, scale = "row")
}
}
r if(ncol(data_fun_NOG) > 2){ "## Heatmap of NOG composition"}
if(ncol(data_fun_NOG) > 2){
rownames(data_fun_NOG) <- data_fun_NOG[,1]
if(is.null(meta_table)){
d3heatmap::d3heatmap(data_fun_NOG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row")
}else{
meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used
rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE])
pheatmap::pheatmap(data_fun_NOG[,-1,drop = FALSE], annotation_col = meta, scale = "row")
}
}
r if(ncol(data_fun_KEGG) > 2){ "## Heatmap of KEGG composition"}
if(ncol(data_fun_KEGG) > 2){
rownames(data_fun_KEGG) <- data_fun_KEGG[,1]
if(is.null(meta_table)){
d3heatmap::d3heatmap(data_fun_KEGG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row")
}else{
meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used
rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE])
pheatmap:: pheatmap(data_fun_KEGG[,-1,drop = FALSE], annotation_col = meta, scale = "row")
}
}
r if(ncol(data_fun_COG) > 3){ "# PCA plots"}
r if(ncol(data_fun_COG) > 3){ "## PCA plots of COG composition"}
if(ncol(data_fun_COG) > 3){
df_COG_log10 <- log10(data_fun_COG[,-1,drop = FALSE]+1)
if(is.null(meta_table)){
PCA_result <- prcomp(t(df_COG_log10))
loading <- as.data.frame(PCA_result$x)
# for screen plot
sd <- PCA_result$sde
var <- sd^2
var.percent <- var/sum(var) * 100
PCs <- paste0("PC", 1:length(var.percent))
df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1))
PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)")
plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>%
add_markers() %>%
add_text(text = row.names(loading)) %>%
layout(
scene = list(
xaxis = list(title = PCs[1]),
yaxis = list(title = PCs[2]),
zaxis = list(title = PCs[3])
))
}else{
PCA_result <- prcomp(t(df_COG_log10))
loading <- as.data.frame(PCA_result$x)
meta_3D_PCA <- cbind(rownames(loading), meta_table[,3])
PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA)
}
}
r if(ncol(data_fun_NOG) > 3){ "## PCA plots of NOG composition"}
if(ncol(data_fun_NOG) > 3){
df_NOG_log10 <- log10(data_fun_NOG[,-1,drop = FALSE]+1)
if(is.null(meta_table)){
PCA_result <- prcomp(t(df_NOG_log10))
loading <- as.data.frame(PCA_result$x)
# for screen plot
sd <- PCA_result$sde
var <- sd^2
var.percent <- var/sum(var) * 100
PCs <- paste0("PC", 1:length(var.percent))
df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1))
PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)")
plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>%
add_markers() %>%
add_text(text = row.names(loading)) %>%
layout(
scene = list(
xaxis = list(title = PCs[1]),
yaxis = list(title = PCs[2]),
zaxis = list(title = PCs[3])
))
}else{
PCA_result <- prcomp(t(df_NOG_log10))
loading <- as.data.frame(PCA_result$x)
meta_3D_PCA <- cbind(rownames(loading), meta_table[,3])
PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA)
}
}
r if(ncol(data_fun_KEGG) > 3){ "## PCA plots of KEGG composition"}
if(ncol(data_fun_KEGG) > 3){
df_KEGG_log10 <- log10(data_fun_KEGG[,-1,drop = FALSE]+1)
if(is.null(meta_table)){
PCA_result <- prcomp(t(df_KEGG_log10))
loading <- as.data.frame(PCA_result$x)
# for screen plot
sd <- PCA_result$sde
var <- sd^2
var.percent <- var/sum(var) * 100
PCs <- paste0("PC", 1:length(var.percent))
df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1))
PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)")
plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>%
add_markers() %>%
add_text(text = row.names(loading)) %>%
layout(
scene = list(
xaxis = list(title = PCs[1]),
yaxis = list(title = PCs[2]),
zaxis = list(title = PCs[3])
))
}else{
PCA_result <- prcomp(t(df_KEGG_log10))
loading <- as.data.frame(PCA_result$x)
meta_3D_PCA <- cbind(rownames(loading), meta_table[,3])
PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA)
}
}
ningzhibin/rmdocpu documentation built on Feb. 3, 2022, 9:30 p.m.
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htmltools::img(src = "https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/rmd/iMetaReport.png", alt = 'logo', style = 'position:absolute; top:0; right:0; padding:10px;width:150px;height:150px;')
source("https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/subfunctions_general.r") source("https://raw.githubusercontent.com/ningzhibin/rmdocpu/master/inst/subfunctions_general_update.r") # enviroment setup knitr::opts_chunk$set(echo = FALSE,warning = FALSE, message = FALSE, cache = FALSE) library(tidyverse) library(ggplot2) library(plotly) library(readxl) library(pheatmap) library(reshape2) library(vegan) library(ggdendro) # library(gridExtra) #library(plotly) # library(DT) # for interactive data table display # local usage test and debug # rmarkdown::render("MQ_report_function_ocpu.Rmd", params = list(summary_file_tbl = your_readin_tbl)) # on local machine, your_readin_tbl is a data table, while on ocpu, your_readin_tbl is a json formatted table # todo # check the metafile, if meta is not qualified, do not use it. # version control # 20190821 created function rmd # 20190904 added heatmap and PCA, can show meta info if provided
# input if(is.null(params$input_datatable)){ # test with local test with local files in the same dir, data_fun <- read.csv("functions.csv", header = TRUE, sep = ",") }else{ # opencpu render from data table by parametrized input data_fun <- params$input_datatable } # Note: The folling analysis with meta info assumes that # 1st columns as sample name, 2nd column as experiment name, 3rd column and after as grouping meta_table <- params$meta_table # if there is any null value in the meta colum, set the meta as NULL if(any(is.na(meta_table$meta1))){ meta_table <- NULL } #meta_table <- read.delim("metadata.txt", header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
Intro
This report provides some basic description and visualization of the MetaLab function results. The report is based on the function.csv, without defined experimental design/grouping information. Users can use this to quickly check the functional profile of the dataset.
Sample overview
Protein groups annotation | Number (percentage)
--------------------- | -----------------------
Protein groups in your sample | r nrow(data_fun)
Protein groups with COG annotation: | r paste(nrow(data_fun[data_fun$COG.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$COG.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with NOG annotation: | r paste(nrow(data_fun[data_fun$NOG.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$NOG.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with KEGG annotation: | r paste(nrow(data_fun[data_fun$KEGG.name != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$KEGG.name != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOBP annotation: | r paste(nrow(data_fun[data_fun$GOBP.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOBP.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOCC annotation: | r paste(nrow(data_fun[data_fun$GOCC.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOCC.accession != "",])/nrow(data_fun)),"%"),")")
Protein groups with GOMF annotation: | r paste(nrow(data_fun[data_fun$GOMF.accession != "",]),"(",paste0(round(100*nrow(data_fun[data_fun$GOMF.accession != "",])/nrow(data_fun)),"%"),")")
Unique COG accessions annotated: | r length(unique(data_fun$COG.accession))-1
Unique NOG accessions annotated: | r length(unique(data_fun$NOG.accession))-1
Unique KEGG accessions annotated: | r length(unique(data_fun$KEGG.accession))-1
Meta/grouping info provided: | r if( !is.null(meta_table)){unique(meta_table[,3])}else{ "No meta information provided"}
Overview with pie charts
Overview of COG categories
The figure below displays the composition of COG categories in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Get the columns out of the table if(any(grepl("intensity.", colnames(data_fun), ignore.case=TRUE))){ intensity_columns <- data_fun[,grep("intensity.", colnames(data_fun), ignore.case=TRUE), drop = FALSE] colnames(intensity_columns)<-gsub("intensity.", "", colnames(intensity_columns), ignore.case=TRUE) } # Calculate subtotal for each category intensity_columns_C <- cbind(data_fun$COG.category, intensity_columns) data_fun_COG <- aggregate(. ~ data_fun$COG.category, data = intensity_columns_C[,-1, drop = FALSE], FUN = sum) data_fun_COG$`data_fun$COG.category` <- as.character(data_fun_COG$`data_fun$COG.category`) data_fun_COG$`data_fun$COG.category`[1] <- "Unmatched" data_fun_COG <- data_fun_COG[which(rowSums(data_fun_COG[,-1,drop = FALSE])>0),] # Prepare data for plotting Intensity <- rowSums(data_fun_COG[,-1, drop = FALSE]) rowSum_COG <- as.data.frame(Intensity) COG_cat <- data_fun_COG[,1] rowSum_COG <- cbind(COG_cat, rowSum_COG) #Draw pie chart plot_ly(rowSum_COG, labels = ~COG_cat, values = ~Intensity, type = 'pie') %>% layout(title = 'Composition of COG categories', xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Overview of NOG categories
The figure below displays the composition of NOG categories in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Calculate subtotal for each category intensity_columns_N <- cbind(data_fun$NOG.category, intensity_columns) data_fun_NOG <- aggregate(. ~ data_fun$NOG.category, data = intensity_columns_N[,-1,drop = FALSE], FUN = sum) data_fun_NOG$`data_fun$NOG.category` <- as.character(data_fun_NOG$`data_fun$NOG.category`) data_fun_NOG$`data_fun$NOG.category`[1] <- "Unmatched" data_fun_NOG <- data_fun_NOG[which(rowSums(data_fun_NOG[,-1,drop = FALSE])>0),] # Prepare data for plotting IntensityN <- rowSums(data_fun_NOG[,-1,drop = FALSE]) rowSum_NOG <- as.data.frame(IntensityN) NOG_cat <- data_fun_NOG[,1] rowSum_NOG <- cbind(NOG_cat, rowSum_NOG) #Draw pie chart plot_ly(rowSum_NOG, labels = ~NOG_cat, values = ~IntensityN, type = 'pie') %>% layout(title = 'Composition of NOG categories', xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Overview of KEGG pathways
The figure below displays the composition of KEGG pathways in each of your sample. Intensities in the pie chart are based on summed proteinGroup intensities across all samples.
# Calculate subtotal for each category intensity_columns_K <- cbind(data_fun$KEGG.name, intensity_columns) data_fun_KEGG <- aggregate(. ~ data_fun$KEGG.name, data = intensity_columns_K[,-1,drop = FALSE], FUN = sum) data_fun_KEGG$`data_fun$KEGG.name` <- as.character(data_fun_KEGG$`data_fun$KEGG.name`) data_fun_KEGG$`data_fun$KEGG.name`[1] <- "Unmatched" data_fun_KEGG <- data_fun_KEGG[which(rowSums(data_fun_KEGG[,-1,drop = FALSE])>0),] # Prepare data for plotting IntensityK <- rowSums(data_fun_KEGG[,-1,drop = FALSE]) rowSum_KEGG <- as.data.frame(IntensityK) KEGG_cat <- data_fun_KEGG[,1] rowSum_KEGG <- cbind(KEGG_cat, rowSum_KEGG) #Draw pie chart plot_ly(rowSum_KEGG, labels = ~KEGG_cat, values = ~IntensityK, type = 'pie') %>% layout(title = 'Composition of KEGG pathways', xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Functional composition in each sample
COG composition in each sample
The figure below displays the composition of COG categories in each of your sample. Intensities in the bar plot are based on summed proteinGroup intensities corresponding to each category.
# Prepare data for plotting colnames(data_fun_COG)[colnames(data_fun_COG)=="data_fun$COG.category"] <- "Name" data_fun_COG_gg <- melt(data = data_fun_COG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity") #Draw stacked column bar ggplotly(ggplot(data_fun_COG_gg, aes(x = Sample, y = Intensity, fill = Name)) + geom_bar(stat='identity') + theme_bw() + ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ) %>% style(legendgroup = NULL)
NOG composition in each sample
Similar to the COG summary, the figure below displays the composition of NOG categories in each of your sample.
# Prepare data for plotting colnames(data_fun_NOG)[colnames(data_fun_NOG)=="data_fun$NOG.category"] <- "Name" data_fun_NOG_gg <- melt(data = data_fun_NOG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity") #Draw stacked column bar ggplotly(ggplot(data_fun_NOG_gg, aes(x = Sample, y = Intensity, fill = Name)) + geom_bar(stat='identity') + theme_bw() + ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) )
KEGG composition in each sample
The figure below displays the composition of KEGG pathways in each of your sample.
# Prepare data for plotting colnames(data_fun_KEGG)[colnames(data_fun_KEGG)=="data_fun$KEGG.name"] <- "Name" data_fun_KEGG_gg <- melt(data = data_fun_KEGG,id.vars = c("Name"), variable.name = "Sample", value.name = "Intensity") #Draw stacked column bar ggplotly(ggplot(data_fun_KEGG_gg, aes(x = Sample, y = Intensity, fill = Name)) + geom_bar(stat='identity') + theme_bw() + ylab("Intensity") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) )
r if(ncol(data_fun_COG) > 2){ "# Heatmap of functional composition"}
r if(ncol(data_fun_COG) > 2){ "## Heatmap of COG composition"}
if(ncol(data_fun_COG) > 2){ rownames(data_fun_COG) <- data_fun_COG[,1] if(is.null(meta_table)){ d3heatmap::d3heatmap(data_fun_COG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row") }else{ meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE]) pheatmap::pheatmap(data_fun_COG[,-1,drop = FALSE], annotation_col = meta, scale = "row") } }
r if(ncol(data_fun_NOG) > 2){ "## Heatmap of NOG composition"}
if(ncol(data_fun_NOG) > 2){ rownames(data_fun_NOG) <- data_fun_NOG[,1] if(is.null(meta_table)){ d3heatmap::d3heatmap(data_fun_NOG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row") }else{ meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE]) pheatmap::pheatmap(data_fun_NOG[,-1,drop = FALSE], annotation_col = meta, scale = "row") } }
r if(ncol(data_fun_KEGG) > 2){ "## Heatmap of KEGG composition"}
if(ncol(data_fun_KEGG) > 2){ rownames(data_fun_KEGG) <- data_fun_KEGG[,1] if(is.null(meta_table)){ d3heatmap::d3heatmap(data_fun_KEGG[,-1,drop = FALSE],show_grid = FALSE, color = "RdYlBu", scale = "row") }else{ meta <- meta_table[,3, drop = FALSE]# for the report, only the first meta is used rownames(meta) <- colnames(data_fun_COG[,-1,drop = FALSE]) pheatmap:: pheatmap(data_fun_KEGG[,-1,drop = FALSE], annotation_col = meta, scale = "row") } }
r if(ncol(data_fun_COG) > 3){ "# PCA plots"}
r if(ncol(data_fun_COG) > 3){ "## PCA plots of COG composition"}
if(ncol(data_fun_COG) > 3){ df_COG_log10 <- log10(data_fun_COG[,-1,drop = FALSE]+1) if(is.null(meta_table)){ PCA_result <- prcomp(t(df_COG_log10)) loading <- as.data.frame(PCA_result$x) # for screen plot sd <- PCA_result$sde var <- sd^2 var.percent <- var/sum(var) * 100 PCs <- paste0("PC", 1:length(var.percent)) df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1)) PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)") plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>% add_markers() %>% add_text(text = row.names(loading)) %>% layout( scene = list( xaxis = list(title = PCs[1]), yaxis = list(title = PCs[2]), zaxis = list(title = PCs[3]) )) }else{ PCA_result <- prcomp(t(df_COG_log10)) loading <- as.data.frame(PCA_result$x) meta_3D_PCA <- cbind(rownames(loading), meta_table[,3]) PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA) } }
r if(ncol(data_fun_NOG) > 3){ "## PCA plots of NOG composition"}
if(ncol(data_fun_NOG) > 3){ df_NOG_log10 <- log10(data_fun_NOG[,-1,drop = FALSE]+1) if(is.null(meta_table)){ PCA_result <- prcomp(t(df_NOG_log10)) loading <- as.data.frame(PCA_result$x) # for screen plot sd <- PCA_result$sde var <- sd^2 var.percent <- var/sum(var) * 100 PCs <- paste0("PC", 1:length(var.percent)) df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1)) PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)") plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>% add_markers() %>% add_text(text = row.names(loading)) %>% layout( scene = list( xaxis = list(title = PCs[1]), yaxis = list(title = PCs[2]), zaxis = list(title = PCs[3]) )) }else{ PCA_result <- prcomp(t(df_NOG_log10)) loading <- as.data.frame(PCA_result$x) meta_3D_PCA <- cbind(rownames(loading), meta_table[,3]) PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA) } }
r if(ncol(data_fun_KEGG) > 3){ "## PCA plots of KEGG composition"}
if(ncol(data_fun_KEGG) > 3){ df_KEGG_log10 <- log10(data_fun_KEGG[,-1,drop = FALSE]+1) if(is.null(meta_table)){ PCA_result <- prcomp(t(df_KEGG_log10)) loading <- as.data.frame(PCA_result$x) # for screen plot sd <- PCA_result$sde var <- sd^2 var.percent <- var/sum(var) * 100 PCs <- paste0("PC", 1:length(var.percent)) df_scree <- data.frame(PC = factor(PCs, levels = PCs), ratio = round(var.percent,1)) PCs <- paste0("PC", 1:length(var.percent), " (", df_scree[1:length(var.percent),2],"%)") plot_ly(loading, x = ~PC1, y = ~PC2, z = ~PC3) %>% add_markers() %>% add_text(text = row.names(loading)) %>% layout( scene = list( xaxis = list(title = PCs[1]), yaxis = list(title = PCs[2]), zaxis = list(title = PCs[3]) )) }else{ PCA_result <- prcomp(t(df_KEGG_log10)) loading <- as.data.frame(PCA_result$x) meta_3D_PCA <- cbind(rownames(loading), meta_table[,3]) PCA_plot_3d_interactive_3(prcomp_out = PCA_result, grouping = meta_3D_PCA) } }
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