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EXIS_Global.R
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EXIS_Global.R
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############# #### #### #### #########
############# #### #### #### ##########
##### ########### #### #####
##### ####### #### #####
############# ########### #### ########
############# #### #### #### ########
##### #### #### #### ######
##### #####
#####################################################
#####################################################
#Isoform expression analysis
#CREATED BY JARNO KOETSIER
#EXIS 1.0.0 (June, 2021)
###############################################################################################################################
#INSTRUCTIONS GLOBAL SCRIPT
###############################################################################################################################
#1. Source run this script before using the EXIS app.
# IMPORTANT: The current analysis depends on the modified affy package from Brainarray.
# If you have installed the original affy package previously,
# please remove this package from your package library before source running this script.
#2. Make sure that the exon/transcript-specific CDF file is in your working directory
# OR has been installed as a CDF package previously.
#3. When you would like to use the exon-specific probe set definition,
# also make sure to place exon annotation file in your working directory
#################################################################################################################################
#Install and load packages
#################################################################################################################################
pkg <- installed.packages()[, "Package"]
#Modified affy package from Brainarray
if(!('affy' %in% pkg)) {
install.packages("http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/25.0.0/affy_1.68.0.tar.gz", type = "source", repos = NULL)
}
#Bioconductor packages
s_requiredpackages =
c(
"biomaRt",
"GEOquery",
"ArrayExpress",
"limma"
)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager", ask = F)
for (i in s_requiredpackages) {
if (!requireNamespace(i, quietly = TRUE))
BiocManager::install(i, ask = F)
require(as.character(i), character.only = TRUE)
}
#Other packages
if(!('tidyverse' %in% pkg)){
install.packages("tidyverse")
}
if(!('fuzzyjoin' %in% pkg)){
install.packages("fuzzyjoin")
}
if(!("plotly" %in% pkg)){
install.packages("plotly")
}
if(!("makecdfenv" %in% pkg)){
install.packages("makecdfenv")
}
#Load packages
library(affy)
library(GEOquery)
library(ArrayExpress)
library(biomaRt)
library(limma)
library(fuzzyjoin)
library(tidyverse)
library(plotly)
library(makecdfenv)
###################################################################################################################################
#get_grouping
###################################################################################################################################
get_grouping <- function(gset, database = "GEO") {
if (database == "GEO"){
GEO_accession <- str_remove(names(gset), "_series_matrix.txt.gz")
grouping <- as.data.frame(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data)[1]
for (l in 1:length(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data)){
if (((length(unique(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data[[l]]))) > 1) &
((length(unique(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data[[l]]))) < length(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data[[l]]))){
grouping <- cbind(grouping, as.data.frame(gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data)[l])
}
}
grouping <- as.data.frame(grouping[,-1])
grouping <- grouping %>%
mutate(across(everything(), as.character))
}
if (database == "ArrayExpress"){
grouping <- as.data.frame(gset@phenoData@data)[1]
for (l in 1:length(gset@phenoData@data)){
if (((length(unique(gset@phenoData@data[[l]]))) > 1) &
((length(unique(gset@phenoData@data[[l]]))) < length(gset@phenoData@data[[l]]))){
grouping <- cbind(grouping, as.data.frame(gset@phenoData@data)[l])
}
}
grouping <- as.data.frame(grouping[,-1])
grouping <- grouping %>%
mutate(across(everything(), as.character))
}
#remove columns with the same information
uni = c(1)
suppressWarnings(
for (i in 1:(ncol(grouping)-1)) {
for (j in (i+1):ncol(grouping)) {
if (all(str_detect(grouping[,i], grouping[,j]))) {
if (length(unique(str_remove(grouping[,i], grouping[,j]))) == 1){
uni <- c(uni, i)
}
}
if (all(str_detect(grouping[,j], grouping[,i]))) {
if (length(unique(str_remove(grouping[,j], grouping[,i]))) == 1) {
if (length(unique(str_remove(grouping[,i], grouping[,j]))) != 1){
uni <- c(uni, j)
}
}
}
}
}
)
uni <- unique(uni[-1])
grouping <- grouping[, -uni]
rownames(grouping) <- NULL
return(grouping)
}
###################################################################################################################################
#auto_group
###################################################################################################################################
auto_group <- function(groups, attempt = 1){
participant_group <- NULL
n_ch <- NULL
for (l in 1:length(groups)){
if ((length(grep("control|non|healthy|treat", groups[[l]], ignore.case = TRUE)) > 0) &
(length(unique(groups[[l]]))) < length(groups[[l]])){
n_ch <- rbind(n_ch, c(l,nchar(groups[[l]][1])))
}
}
if (!is.null(n_ch)){
if (attempt <= nrow(n_ch)) {
y = 1
repeat {
if (y == attempt) {
break
}
y = y + 1
n_ch = n_ch[-which.min(n_ch[,2]),]
}
int1 <- n_ch[which.min(n_ch[,2]),1]
participant_group <- as.data.frame(groups)[int1]
print(noquote("Grouping has been done automatically by default. Please check whether grouping has occured correctly."))
}
if (attempt > nrow(n_ch)) {
print(noquote("grouping information not found. Upload grouping data manually at grouping_column"))
}
}
if (is.null(n_ch)) {
print(noquote("grouping information not found. Upload grouping data manually at grouping_column"))
}
return(colnames(participant_group))
}
###################################################################################################################################
#get_meta
###################################################################################################################################
get_meta <- function(gset, grouping_column, pairing_column = NULL, file_name = NULL, database = "GEO") {
#Get participant info
if(database == "GEO"){
GEO_accession <- str_remove(names(gset), "_series_matrix.txt.gz")
participants <- gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data[["geo_accession"]]
}
if(database == "ArrayExpress"){
participants <- rownames(gset@phenoData@data)
}
#use grouping column
if (length(grouping_column[1]) > 0){
if (!is.vector(grouping_column)){
if (ncol(grouping_column) > 1) {
participant_group <- unite(grouping_column, col = grouping_column, sep = ".")
}
if (ncol(grouping_column) == 1){
participant_group <- grouping_column
}
}
if (is.vector(grouping_column)){
participant_group <- grouping_column
}
}
#independ samples
if (is.null(pairing_column) | length(pairing_column) < 1) {
if (exists("participant_group")){
meta <- as.data.frame(cbind(participants, participant_group))
rownames(meta) <- NULL
colnames(meta) <- c("Sample ID", "Grouping")
return(meta)
}
}
#dependent samples
if (!is.null(pairing_column) & length(pairing_column) >= 1){
if (!is.vector(pairing_column)){
if (ncol(pairing_column) > 1) {
pairs <- unite(pairing_column, col = pairing_column, sep = ".")
}
if (ncol(pairing_column) == 1){
pairs <- pairing_column
}
}
if (is.vector(pairing_column)){
pairs <- pairing_column
}
meta <- as.data.frame(cbind(participants, participant_group, pairs))
rownames(meta) <- NULL
colnames(meta) <- c("Sample ID", "Grouping", "Pairing")
return(meta)
}
#file upload
if (!is.null(file_name)){
meta <- read_excel(file_name)
return(meta)
}
}
###################################################################################################################################
#get_chiptype
###################################################################################################################################
get_chiptype <- function(gset, database = "GEO"){
if (database == "GEO"){
GEO_accession <- str_remove(names(gset), "_series_matrix.txt.gz")
info <- gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data
}
if (database == "ArrayExpress"){
info <- gset@phenoData@data
}
chiptype = "unknown"
organism = "unknown"
for (j in 1:length(info)) {
if (length(grep("huex|A-AFFY-143", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "huex10st"
organism = "hs"
}
}
for (j in 1:length(info)) {
if (length(grep("hugene-1_0|human gene 1.0|A-AFFY-141", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "hugene10st"
organism = "hs"
}
}
for (j in 1:length(info)) {
if (length(grep("hugene-2_0|human gene 2.0", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "hugene20st"
organism = "hs"
}
}
if (chiptype == "unknown") {
print(noquote("Chip type could not be found. Please select chip type and organism manually"))
}
if (chiptype != "unknown") {
print(noquote(paste(chiptype, "chip type was selected.",
"If this is the incorrect chip type, please select the chip type manually", sep = " ")))
}
return(chiptype)
}
###################################################################################################################################
#get_organism
###################################################################################################################################
get_organism <- function(gset, database = "GEO"){
if (database == "GEO"){
GEO_accession <- str_remove(names(gset), "_series_matrix.txt.gz")
info <- gset[[paste0(GEO_accession,"_series_matrix.txt.gz")]]@phenoData@data
}
if (database == "ArrayExpress"){
info <- gset@phenoData@data
}
chiptype = "unknown"
organism = "unknown"
for (j in 1:length(info)) {
if (length(grep("huex|A-AFFY-143", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "huex10st"
organism = "hs"
}
}
for (j in 1:length(info)) {
if (length(grep("hugene-1_0|human gene 1.0|A-AFFY-141", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "hugene10st"
organism = "hs"
}
}
for (j in 1:length(info)) {
if (length(grep("hugene-2_0|human gene 2.0", info[[j]], ignore.case = TRUE)) > 0) {
chiptype = "hugene20st"
organism = "hs"
}
}
if (chiptype == "unknown") {
print(noquote("Chip type could not be found. Please select chip type and organism manually"))
}
if (chiptype != "unknown") {
print(noquote(paste(organism, " was selected.",
"If this is the incorrect organism, please select the organism manually", sep = " ")))
}
return(organism)
}
###################################################################################################################################
#readcels1
###################################################################################################################################
readcels1 <- function(accession, database = "GEO", chiptype, organism , version = 25, annotation = "enst", robust = TRUE, outliers = NULL) {
pkg <- installed.packages()[, "Package"]
exdir = paste0("data_", accession)
if (database == "GEO"){
#Download data
if (!file.exists(exdir)){
getGEOSuppFiles(accession)
tarfile = paste0(accession, "/", accession, "_RAW.tar")
untar(tarfile, exdir = exdir)
}
celfiles = list.files(paste0(exdir, "/"), pattern = "CEL", full.names = TRUE)
if (!is.null(outliers)){
outliers_select <- outliers[1]
if (length(outliers) > 1){
for (i in 1:(length(outliers)-1)){
outliers_select <- paste(outliers_select, outliers[1+i], sep = "|")
}
}
celfiles = celfiles[-grep(outliers_select, celfiles)]
}
}
if (database == "ArrayExpress"){
if (!file.exists(exdir)) {
getAE(accession, type = "raw", extract = FALSE)
unzip(paste0(accession, ".raw.1.zip"), exdir = exdir)
}
celfiles = list.files(paste0(exdir, "/"), pattern = "CEL", full.names = TRUE)
if (!is.null(outliers)){
outliers_select <- outliers[1]
if (length(outliers) > 1){
for (i in 1:(length(outliers)-1)){
outliers_select <- paste(outliers_select, outliers[1+i], sep = "|")
}
}
celfiles = celfiles[-grep(outliers_select, celfiles)]
}
}
#Get CDF
if (annotation == "ense") {
cdf <- paste0(chiptype,"ense", version, "cdf")
}
if (annotation == "enst") {
if (robust == TRUE){
cdf <- paste0(chiptype, "enst", "robust", version, "cdf")
}
if (robust == FALSE){
cdf <- paste0(chiptype, "enst", "all", version, "cdf")
}
}
if(!(cdf %in% pkg)) {
if (annotation == "ense") {
cdf.file = paste0(chiptype, "ense", version)
}
if (annotation == "enst") {
if (robust == FALSE) {
cdf.file = paste0(chiptype, "enst", "all", version)
}
if (robust == TRUE) {
cdf.file = paste0(chiptype, "enst", "robust", version)
}
}
if (exists(cdf.file)) {
make.cdf.package(paste0(cdf.file, ".cdf"), species = "Homo_sapiens")
install.packages(paste0(cdf.file, "cdf/"), repos = NULL, type = "source")
}
if (!exists(cdf.file)) {
print("Could not find correct CDF package or file. Please use the makeCDFfile function.")
cdf = NULL
}
}
#read microarray data
if (!is.null(celfiles) & !is.null(cdf)) {
data = ReadAffy(filenames=celfiles, cdfname = cdf)
return(data)
}
}
###################################################################################################################################
#readcels.wd
###################################################################################################################################
readcels.wd <- function(directory = getwd(), chiptype, organism , version = 25, annotation = "enst", robust = TRUE, outliers = NULL) {
#Get CEL files
celfiles = list.files(paste0(directory, "/"), pattern = "CEL", full.names = TRUE)
if (!is.null(outliers)){
outliers_select <- outliers[1]
if (length(outliers) > 1){
for (i in 1:(length(outliers)-1)){
outliers_select <- paste(outliers_select, outliers[1+i], sep = "|")
}
}
celfiles = celfiles[-grep(outliers_select, celfiles)]
}
#Get CDF
if (annotation == "ense") {
cdf <- paste0(chiptype,"ense", version, "cdf")
}
if (annotation == "enst") {
if (robust == TRUE){
cdf <- paste0(chiptype, "enst", "robust", version, "cdf")
}
if (robust == FALSE){
cdf <- paste0(chiptype, "enst", "all", version, "cdf")
}
}
if(!(cdf %in% pkg)) {
if (annotation == "ense") {
cdf.file = paste0(chiptype, "ense", version)
}
if (annotation == "enst") {
if (robust == FALSE) {
cdf.file = paste0(chiptype, "enst", "all", version)
}
if (robust == TRUE) {
cdf.file = paste0(chiptype, "enst", "robust", version)
}
}
if (exists(cdf.file)) {
make.cdf.package(paste0(cdf.file, ".cdf"), species = "Homo_sapiens")
install.packages(paste0(cdf.file, "cdf/"), repos = NULL, type = "source")
}
if (!exists(cdf.file)) {
print("Could not find correct CDF package or file. Please use the makeCDFfile function.")
cdf = NULL
}
}
#read microarray data
if (!is.null(celfiles) & !is.null(cdf)) {
data = ReadAffy(filenames=celfiles, cdfname = cdf)
return(data)
}
}
###################################################################################################################################
#pca.plot
###################################################################################################################################
pca.plot <- function(data.PC, meta, hpc = 1, vpc = 2) {
samples = as.data.frame(rownames(data.PC$x))
colnames(samples) <- "cel_names"
samples_group <- samples %>% fuzzyjoin::fuzzy_inner_join(meta, by = c("cel_names" = "Sample ID"), match_fun = str_detect)
Groups <- as.factor(samples_group[,3])
Sample <- samples_group[,2]
X = as.data.frame(data.PC$x)[,hpc]
Y = as.data.frame(data.PC$x)[,vpc]
pca <- ggplot(data = as.data.frame(data.PC$x), aes(x = X, y = Y, col = Groups, text = paste("Sample:", Sample))) +
geom_point() +
ggtitle("PCA plot") +
xlab(paste0("PC", hpc)) +
ylab(paste0("PC", vpc)) +
theme_light()
return(ggplotly(pca))
}
###################################################################################################################################
#get_contrasts
###################################################################################################################################
get_contrasts <- function(meta) {
levels1 <- unique(meta[,2])
contrast <- paste(make.names(levels1)[2], make.names(levels1)[1], sep = " - ")
for (m in 1:(length(levels1)-1)){
for (n in (m+1):length(levels1)) {
if (grepl("non|healthy|control", levels1[m])) {
contrast <- c(contrast, paste(make.names(levels1)[n], make.names(levels1)[m], sep = " - "))
}
if (!grepl("non|healthy|control", levels1[m])){
contrast <- c(contrast, paste(make.names(levels1)[m], make.names(levels1)[n], sep = " - "))
}
}
}
return(sort(contrast[-1]))
}
###################################################################################################################################
#auto_contrast
###################################################################################################################################
auto_contrasts <- function(meta) {
levels1 <- unique(meta[,2])
contrast <- paste(make.names(levels1)[2], make.names(levels1)[1], sep = " - ")
for (m in 1:(length(levels1)-1)){
for (n in (m+1):length(levels1)) {
if (grepl("non|healthy|control", levels1[m])) {
contrast <- c(contrast, paste(make.names(levels1)[n], make.names(levels1)[m], sep = " - "))
}
if (!grepl("non|healthy|control", levels1[m])){
contrast <- c(contrast, paste(make.names(levels1)[m], make.names(levels1)[n], sep = " - "))
}
}
}
contrast <- contrast[-1]
contrast <- str_replace_all(contrast, "_", "\\.")
contrast1 <- contrast
for (k in 1:length(contrast)) {
a <- strsplit(strsplit(contrast[k], split= " - ")[[1]][1], split = "\\.")
a <- a[[1]]
b <- strsplit(strsplit(contrast[k], split= " - ")[[1]][2], split = "\\.")
b <- b[[1]]
c <- setdiff(a,b)
d <- setdiff(b,a)
length_c <- length(c)
length_d <- length(d)
if (length_c > 1) {
for (i in 1:(length_c-1)) {
for (j in (i+1):(length_c)) {
e <- paste(c[i], c[j], sep = ".")
if (length(grep(e, make.names(unique(meta[,2])))) == length(match(grep(c[i], make.names(unique(meta[,2]))), grep(c[j], contrast)))) {
length_c = length_c - 1
}
}
}
}
if (length_d > 1) {
for (i in 1:(length(d)-1)) {
for (j in (i+1):(length(d))) {
f <- paste(d[i], d[j], sep = ".")
if (length(grep(f, make.names(unique(meta[,2])))) == length(match(grep(d[i], make.names(unique(meta[,2]))), grep(d[j], contrast)))) {
length_d = length_d - 1
}
}
}
}
if (length_c <= 1 & length_d <= 1) {
}
if (length_c > 1 | length_d > 1) {
contrast1 <- contrast1[-which(contrast1 == contrast[k])]
}
}
return(contrast1)
}
###################################################################################################################################
#diff_expr
###################################################################################################################################
diff_expr <- function(data.expr, meta, comparisons) {
#get grouping variable in correct order
ph = as.data.frame(colnames(data.expr))
colnames(ph) <- "cel_names"
ph1 <- ph %>% fuzzyjoin::fuzzy_inner_join(meta, by = c("cel_names" = "Sample ID"), match_fun = str_detect)
#model design
source <- ph1[,3]
levels <- unique(ph1[,3])
f <- factor(source,levels=levels)
design <- model.matrix(~ 0 + f)
colnames(design) <- make.names(levels)
#fit linear model
data.fit <- lmFit(data.expr,design)
#get contrasts
contrast <- comparisons
#make top table for each comparison
top.table <- list()
for (i in contrast) {
contrast.matrix <- makeContrasts(contrasts = i,levels=design)
data.fit.con <- contrasts.fit(data.fit,contrast.matrix)
data.fit.eb <- eBayes(data.fit.con)
top.table[[i]] <- topTable(data.fit.eb, sort.by = "P", n = Inf)
}
return(top.table)
}
###################################################################################################################################
#transcript_selection
###################################################################################################################################
transcript_selection <- function(top.table, genes = NULL, transcripts = NULL, genome_wide = TRUE) {
mart <- useMart("ENSEMBL_MART_ENSEMBL")
mart <- useDataset("hsapiens_gene_ensembl", mart)
if (genome_wide == FALSE){
if (!is.null(genes)){
gene_info <- getBM(
mart=mart,
attributes=c(
"ensembl_transcript_id",
"ensembl_gene_id"
),
filter = "ensembl_gene_id",
values = genes, uniqueRows=TRUE)
select.top.table <- list()
for (i in names(top.table)){
transcript.top.table <- top.table[[i]]
transcript.top.table <- transcript.top.table[,1:4]
transcript.top.table <- cbind(rownames(transcript.top.table), transcript.top.table)
rownames(transcript.top.table) <- NULL
colnames(transcript.top.table) <- c("Ensembl.ID", "logFC", "Average expression", "t", "P.value")
transcript.top.table <- inner_join(gene_info, transcript.top.table, by = c("ensembl_transcript_id" = "Ensembl.ID"))
if (nrow(transcript.top.table) > 0) {
transcript.top.table <- cbind(transcript.top.table, p.adjust(transcript.top.table[,"P.value"], method = "fdr"))
colnames(transcript.top.table) <- c("Ensembl.transcript.ID", "Ensembl.gene.ID", "logFC", "Average expression", "t", "P.value", "FDR")
transcript.top.table <- arrange(transcript.top.table, P.value)
}
select.top.table[[i]] <- transcript.top.table
}
}
#based on list of transcripts
if (!is.null(transcripts)) {
gene_info <- getBM(
mart=mart,
attributes=c(
"ensembl_transcript_id",
"ensembl_gene_id"
),
filter = "ensembl_transcript_id",
values = transcripts, uniqueRows=TRUE)
select.top.table <- list()
for (i in names(top.table)){
transcript.top.table <- top.table[[i]]
transcript.top.table <- transcript.top.table[,1:4]
transcript.top.table <- cbind(rownames(transcript.top.table), transcript.top.table)
rownames(transcript.top.table) <- NULL
colnames(transcript.top.table) <- c("Ensembl.ID", "logFC", "Average expression", "t", "P.value")
transcript.top.table <- inner_join(gene_info, transcript.top.table, by = c("ensembl_transcript_id" = "Ensembl.ID"))
transcript.top.table <- cbind(transcript.top.table, p.adjust(transcript.top.table[,"P.value"], method = "fdr"))
colnames(transcript.top.table) <- c("Ensembl.transcript.ID", "Ensembl.gene.ID", "logFC", "Average expression", "t", "P.value", "FDR")
transcript.top.table <- arrange(transcript.top.table, P.value)
select.top.table[[i]] <- transcript.top.table
}
}
}
if (genome_wide == TRUE){
gene_info <- getBM(
mart=mart,
attributes=c(
"ensembl_transcript_id",
"ensembl_gene_id"
),
filter = "ensembl_transcript_id",
values = rownames(top.table[[1]]), uniqueRows=TRUE)
select.top.table <- list()
for (i in names(top.table)){
transcript.top.table <- top.table[[i]]
transcript.top.table <- transcript.top.table[,1:4]
transcript.top.table <- cbind(rownames(transcript.top.table), transcript.top.table)
rownames(transcript.top.table) <- NULL
colnames(transcript.top.table) <- c("Ensembl.ID", "logFC", "Average expression", "t", "P.value")
transcript.top.table <- inner_join(gene_info, transcript.top.table, by = c("ensembl_transcript_id" = "Ensembl.ID"))
transcript.top.table <- cbind(transcript.top.table, p.adjust(transcript.top.table[,"P.value"], method = "fdr"))
colnames(transcript.top.table) <- c("Ensembl.transcript.ID", "Ensembl.gene.ID", "logFC", "Average expression", "t", "P.value", "FDR")
transcript.top.table <- arrange(transcript.top.table, P.value)
select.top.table[[i]] <- transcript.top.table
}
}
return(select.top.table)
}
###################################################################################################################################
#exon_selection
###################################################################################################################################
exon_selection <- function(top.table, annotated, genes = NULL, transcripts = NULL, genome_wide = TRUE, unique_exons = TRUE) {