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functions.R
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functions.R
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ensembl_to_symbol <- function(dataframe, ensembl_column){
dataframe_tmp <- dataframe %>%
select(ensembl_column)
data <- unlist(dataframe_tmp)
data <- as.vector(data)
annots <- AnnotationDbi::select(org.Hs.eg.db, keys=data,
columns=c("SYMBOL","GENENAME"), keytype = "ENSEMBL")
result <- merge(dataframe, annots, by.x=ensembl_column, by.y="ENSEMBL")
return(result)
}
setClass(Class="filter_genes_return",
representation(
res="data.frame",
sig="data.frame"
)
)
filter_genes <- function(result, name, species='human'){
test <- as.data.frame(result)
data <- as.vector(rownames(test))
if(species == 'human'){
annots <- AnnotationDbi::select(org.Hs.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'mouse'){
annots <- AnnotationDbi::select(org.Mm.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'macaque'){
annots <- AnnotationDbi::select(org.Mmu.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'rabbit'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="ocuniculus_gene_ensembl",
host="uswest.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'hgnc_symbol'), mart=ensembl))
colnames(annots) <- c('ENSEMBL','SYMBOL')
}else if(species == 'pig'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="sscrofa_gene_ensembl",
host="useast.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'hgnc_symbol'), mart=ensembl))
colnames(annots) <- c('ENSEMBL','SYMBOL')
}else{
print('please supply a valid species within the config.yml file')
}
result <- merge(test, annots, by.x="row.names", by.y="ENSEMBL")
res <- result %>%
dplyr::select(log2FoldChange, SYMBOL, baseMean, padj, Row.names) %>%
na.omit()
sig <- res %>%
dplyr::filter(log2FoldChange > 1 | log2FoldChange < -1) %>%
dplyr::filter(padj < 0.05)
dir.create(file.path("results"), showWarnings = FALSE)
sig_name = paste("results/", name,"_sig.csv", sep="")
sig_name_tsv = paste("results/", name,"_sig.tsv", sep="")
res_name = paste("results/",name,"_res.csv", sep="")
res_name_tsv = paste("results/", name,"_res.tsv", sep="")
write_csv(sig, sig_name)
write_csv(res, res_name)
write_tsv(sig, sig_name_tsv)
write_tsv(res, res_name_tsv)
return(new("filter_genes_return",
res=res,
sig=sig))
}
filter_genes_single <- function(result, name){
test <- as.data.frame(result)
data <- as.vector(rownames(test))
annots <- AnnotationDbi::select(org.Hs.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
result <- merge(test, annots, by.x="row.names", by.y="ENSEMBL")
res <- result %>%
dplyr::select(log2FoldChange, SYMBOL, baseMean, padj, Row.names) %>%
na.omit()
sig <- res %>%
filter(log2FoldChange > 2 | log2FoldChange < -2)
sig_name = paste("results/", name,"_sig.csv", sep="")
sig_name_tsv = paste("results/", name,"_sig.tsv", sep="")
res_name = paste("results/",name,"_res.csv", sep="")
res_name_tsv = paste("results/", name,"_res.tsv", sep="")
write_csv(sig, sig_name)
write_csv(res, res_name)
write_tsv(sig, sig_name_tsv)
write_tsv(res, res_name_tsv)
return(list("sig"= sig, "res"= res))
}
run_deseq2_full <- function(df_mRNA, meta_data, model){
dds<- DESeqDataSetFromMatrix(countData=df_mRNA,
colData=meta_data,
design=as.formula(model))
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds <- DESeq(dds, parallel=FALSE)
return(dds)
}
setClass(Class="DESeq2_return",
representation(
res="DESeqResults",
dds="DESeqDataSet"
)
)
run_deseq2 <- function(df_mRNA, meta_data, control="untreated", test="treated", value, model){
df_mRNA = df_mRNA[,rownames(meta_data)]
dds<- DESeqDataSetFromMatrix(countData=df_mRNA,
colData=meta_data,
design=as.formula(model))
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds <- DESeq(dds)
res <- results(dds, contrast = c(value, test, control))
res_names <- resultsNames(dds)
resLFC <- lfcShrink(dds, coef=res_names[2], type="apeglm")
return(new("DESeq2_return",
res=res,
dds=dds))
}
setClass(Class="DESeq2_lrt_return",
representation(
resLFC="DESeqResults",
res="DESeqResults",
dds="DESeqDataSet"
)
)
run_deseq2_lrt <- function(df_mRNA, meta_data, control="untreated", test="treated", value, model,
reduced){
df_mRNA = df_mRNA[,rownames(meta_data)]
dds<- DESeqDataSetFromMatrix(countData=df_mRNA,
colData=meta_data,
design=as.formula(model))
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds <- DESeq(dds, test="LRT", reduced =as.formula(reduced))
res <- results(dds, contrast = c(value, test,control))
return(new("DESeq2_lrt_return",
res=res,
dds=dds))
}
plot_volcano <- function(res, species="human"){
test <- as.data.frame(res)
data <- as.vector(rownames(test))
if(species == 'human'){
annots <- AnnotationDbi::select(org.Hs.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'mouse'){
annots <- AnnotationDbi::select(org.Mm.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'macaque'){
annots <- AnnotationDbi::select(org.Mmu.eg.db, keys=data,
columns="SYMBOL", keytype = "ENSEMBL")
}else if(species == 'rabbit'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="ocuniculus_gene_ensembl",
host="uswest.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'hgnc_symbol'), mart=ensembl))
colnames(annots) <- c('ENSEMBL','SYMBOL')
}else if(species == 'pig'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="sscrofa_gene_ensembl",
host="useast.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'hgnc_symbol'), mart=ensembl))
colnames(annots) <- c('ENSEMBL','SYMBOL')
}else{
print('please supply a valid species within the config.yml file')
}
result <- merge(test, annots, by.x="row.names", by.y="ENSEMBL")
res <- result %>%
dplyr::select(log2FoldChange, SYMBOL, baseMean, padj, Row.names) %>%
na.omit()
mutateddf <- mutate(res, sig=ifelse(res$padj<0.01, "padj<0.01", "Not Sig")) #Will have different colors depending on significance
input <- cbind(gene=rownames(mutateddf), mutateddf )
input <- input %>%
arrange(input$padj)
symbol_data <- head(input, 30)
#convert the rownames to a column
volc = ggplot(input, aes(log2FoldChange, -log10(padj))) + #volcanoplot with log2Foldchange versus pvalue
geom_point(aes(col=sig)) + #add points colored by significance
geom_point(data=symbol_data, aes(log2FoldChange, -log10(padj)), colour="red") +
ggtitle("Volcano") #e.g. 'Volcanoplot DESeq2'
#setEPS()
#postscript("MUG_volcano.eps")
volcano <- volc+geom_text_repel(data=symbol_data, aes(label=`SYMBOL`)) + scale_colour_Publication() + theme_bw()#adding text for the genes
return(volcano)
}
plotPCA34 <- function (object, ...)
{
.local <- function (object, intgroup = "condition", ntop = 500,
returnData = FALSE)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup %in% names(colData(object)))) {
stop("the argument 'intgroup' should specify columns of colData(dds)")
}
intgroup.df <- as.data.frame(colData(object)[, intgroup,
drop = FALSE])
group <- if (length(intgroup) > 1) {
factor(apply(intgroup.df, 1, paste, collapse = " : "))
}
else {
colData(object)[[intgroup]]
}
d <- data.frame(PC3 = pca$x[, 3], PC4 = pca$x[, 4], group = group,
intgroup.df, name = colnames(object))
if (returnData) {
attr(d, "percentVar") <- percentVar[1:2]
return(d)
}
ggplot(data = d, aes_string(x = "PC3", y = "PC4", color = "group")) +
geom_point(size = 3) + xlab(paste0("PC3: ", round(percentVar[1] *
100), "% variance")) + ylab(paste0("PC4: ", round(percentVar[2] *
100), "% variance")) + coord_fixed()
}
.local(object, ...)
}
theme_Publication <- function(base_size=14, base_family="arial") {
library(grid)
library(ggthemes)
(theme_foundation(base_size=base_size, base_family=base_family)
+ theme(plot.title = element_text(face = "bold",
size = rel(1.2), hjust = 0.5),
text = element_text(),
panel.background = element_rect(colour = NA),
plot.background = element_rect(colour = NA),
panel.border = element_rect(colour = NA),
axis.title = element_text(face = "bold",size = rel(1)),
axis.title.y = element_text(angle=90,vjust =2),
axis.title.x = element_text(vjust = -0.2),
axis.text = element_text(),
axis.line = element_line(colour="black"),
axis.ticks = element_line(),
panel.grid.major = element_line(colour="#f0f0f0"),
panel.grid.minor = element_blank(),
legend.key = element_rect(colour = NA),
legend.position = "bottom",
legend.direction = "horizontal",
legend.key.size= unit(0.2, "cm"),
legend.title = element_text(face="italic"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
strip.text = element_text(face="bold")
))
}
scale_fill_Publication <- function(...){
library(scales)
discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}
scale_colour_Publication <- function(...){
library(scales)
discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
}
pca <- function(vsd, gene_prop = 1, batch = NA,
remove_batch_effect = FALSE){
##Using PCAtools
###Number of genes to use in PCA
if(gene_prop < 0 | gene_prop > 1){stop("gene_prop must be between 0 and 1")}
selectgenes <- floor(gene_prop * nrow(vsd))
##To correct for batch effect:
if(is.na(batch) & remove_batch_effect){stop("Batch column must be provided to remove batch effect")}
if(remove_batch_effect){
assay(vsd) <- limma::removeBatchEffect(assay(vsd), vsd[[batch]])
}
##Run PCA
pca_data <- PCAtools::pca(mat = assay(vsd),
metadata = colData(vsd),
removeVar = (1-gene_prop))
return(pca_data)
}
pca_biplot <- function(pca_data, vsd, gene_prop = 1, design, batch = NA, title,
remove_batch_effect = FALSE){
selectgenes <- floor(gene_prop * nrow(vsd))
##plot PC1 vs PC2
Biplot <- biplot(pca_data,
showLoadings = FALSE,
lab = NULL,
#lab = pca_data$metadata$rowid,
#drawConnectors = TRUE,
#boxedLabels = TRUE,
#labhjust = 2,
#labvjust = 4,
colby = design,
#shape = NULL,
shape = if(!is.na(batch)){batch},
#encircle = TRUE,
#ellipse = TRUE,
#ellipseConf = 0.95,
#ellipseFill = TRUE,
#ellipseAlpha = 1/4,
#ellipseLineSize = 1.0,
#showLoadingsNames = TRUE,
#boxedLoadingsNames = TRUE,
#ntopLoadings = 10,
#sizeLoadingsNames = 2,
#colLoadingsNames = "black",
#fillBoxedLoadings = "white",
#drawConnectorsLoadings = TRUE,
#alphaLoadingsArrow = 1,
#lengthLoadingsArrowsFactor = 1,
#legendPosition = 'right',
legendLabSize = 10,
legendIconSize = 4.0) %>%
ggpar( title = title,
caption = paste0("PCA using: ", selectgenes, " genes") ,
legend = "right",
legend.title = list(color = design, shape = batch),
palette = c("red4", "blue4", "green4", "orange", "black", "brown", "violet"),
shapekey = c(20, 4, 6),
font.title = c("bold", "brown", 18),
font.subtitle = c("bold", "dark grey", 12),
font.caption = c("bold.italic", "royal blue", 10),
font.legend = c("bold", "black", 8),
font.x = c("bold", "black", "11"),
font.y = c("bold", "black", "11"),
font.tickslab = c("bold", "black", "8"),
font.family = "Arial",
x.text.angle = 0,
ggtheme = theme_pubr()
)
#Bp <<- Biplot
#return(Bp)
ifelse(remove_batch_effect,
Biplot$labels$subtitle <- paste0(Biplot$labels$subtitle, ", batch corrected"),
FALSE)
return(Biplot)
#ggsave(paste0(plots_dir, title, ifelse(remove_batch_effect, "_batch_corrected_", "_"), "PCA_all_samples.png"))
}
Plot_pca_loadings <- function(vsd,
gene_prop = 1,
nPCs = 6,
title = Plot_title,
batch = NA,
remove_batch_effect = FALSE,
results_dir = "results_pca/"
) {
dir.create(file.path(results_dir), showWarnings = FALSE)
##Using PCAtools
###Number of genes to use in PCA
if(gene_prop < 0 | gene_prop > 1){stop("gene_prop must be between 0 and 1")}
selectgenes <- floor(gene_prop * nrow(vsd))
##To correct for batch effect:
if(is.na(batch) & remove_batch_effect){stop("Batch column must be provided to remove batch effect")}
if(remove_batch_effect){
assay(vsd) <- limma::removeBatchEffect(assay(vsd), vsd[[batch]])
}
##Run PCA
pca_data <- PCAtools::pca(mat = assay(vsd),
metadata = colData(vsd),
removeVar = (1-gene_prop))
##Create a data.frame of selected PC loadings
Loads_PCs <- PCAtools::getLoadings(pca_data, 1) %>%
as.data.frame() %>%
tibble::rownames_to_column("ensembl_gene_id")
for (i in 2:nPCs) {
Loads_PCs <- Loads_PCs %>% dplyr::mutate(getLoadings(pca_data, i))
}
##save in results folder
write.table(Loads_PCs,
paste0(results_dir, title, ifelse(remove_batch_effect, "_batch_corrected_", "_"),
"PCA_loadings.tsv"),
col.name=TRUE,
sep="\t",
na = "NA",
row.names=FALSE,
quote=FALSE)
##Make a loadings plot for selected loadings
PLoadings <- plotloadings(pca_data,
components = getComponents(pca_data, 1:nPCs),
rangeRetain = 0.1,
labSize = 3.0,
#title = 'Loadings plot',
#subtitle = 'PC1, PC2',
#caption = 'Top 10% variables',
shape = 23,
shapeSizeRange = 1,1,
col = c("dark red", "white", "dark blue"),
colMidpoint = 0,
drawConnectors = TRUE,
lengthConnectors = unit(0.005, "npc"),
positionConnectors = "right",
#labvjust = 0.5,
) %>%
ggpar( title = title,
subtitle = "All samples",
caption = "PC Loadings - Top 10% variables: ",
legend = "right",
font.title = c("bold", "brown", 18),
font.subtitle = c("bold", "dark grey", 14),
font.caption = c("bold.italic", "royal blue", 10),
font.legend = c("bold", "black", 8),
font.x = c("bold", "black", "11"),
font.y = c("bold", "black", "11"),
font.tickslab = c("bold", "black", "8"),
font.family = "Comic Sans MS",
x.text.angle = 0,
ggtheme = theme_pubr()
)
return(PLoadings)
##ggsave(paste0(plots_dir, title, ifelse(remove_batch_effect, "_batch_corrected", FALSE), "_PCA_all_samples.png"))
}
GSEA_plots <- function(pathways, title, results_dir){
##read in the annotated results table
results_annotated <- results_dir
##create ranked gene list for fgsea
gseaInput <- results_annotated %>%
dplyr::filter(!is.na(ENTREZID), !is.na(log2FoldChange)) %>%
arrange(log2FoldChange)
ranks <- pull(gseaInput,log2FoldChange)
names(ranks) <- gseaInput$SYMBOL
###choose pathway databases to load.
##REACTOME GENE SETS
HS_CP_REACTOME <- msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:REACTOME")
HS_CP_REACTOME <- split(x = HS_CP_REACTOME$gene_symbol, f = HS_CP_REACTOME$gs_name)
##HALLMARKS GENE SETS
HS_HALLMARK <- msigdbr(species = "Homo sapiens", category = "H")
HS_HALLMARK <- split(x = HS_HALLMARK$gene_symbol, f = HS_HALLMARK$gs_name)
##KEGG GENE SETS
HS_CP_KEGG <- msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:KEGG")
HS_CP_KEGG <- split(x = HS_CP_KEGG$gene_symbol, f = HS_CP_KEGG$gs_name)
##Choose gene sets list
#pathways <- HS_HALLMARK
pathway_dbs <- list("HS_HALLMARK" = HS_HALLMARK, "HS_CP_KEGG" = HS_CP_KEGG, "HS_CP_REACTOME" = HS_CP_REACTOME)
a <-c("HS_HALLMARK", "HS_CP_KEGG", "HS_CP_REACTOME")
b <- a[str_which(a, str_to_upper(pathways))]
pathways <- pathway_dbs[[b]]
##GSEA analysis using the "ranks" table against the pathways in the database.
fgseaRes <- fgsea(pathways, ranks, minSize=15, maxSize = 500, nperm=1000)
## Show results in a nice table
fgseaResTidy <- fgseaRes %>%
as_tibble() %>%
arrange(desc(NES)) %>%
dplyr::filter(padj <= 0.05) %>%
dplyr::mutate(Pathway = str_replace_all(pathway, "_", " "),
Adjusted_p_value = padj,
Normalised_Enrichment_Score = NES,
Size = size,
Key_genes = leadingEdge) %>%
dplyr::select(Pathway, Normalised_Enrichment_Score, Size, Adjusted_p_value)
#%>% dplyr::filter(abs(Normalised_Enrichment_Score) > 2.3)
##save results table
#return(fgseaResTidy)
write.table(fgseaResTidy,
file = paste0("results_gsea/", title, "fgsea_", b ,"_pathways.tsv"),
col.name=TRUE,
sep="\t",
na = "NA",
append = FALSE,
row.names=FALSE,
quote=FALSE)
if(dim(fgseaResTidy)[1] != 0){
FGSEA_Results <- fgseaResTidy %>% kbl(col.names = c("Pathway", "Normalised Enrichment Score", "Size", "Adjusted p-value"),
caption = "L363 Car-resistant cells, Top Differentially Expressed Pathways",
digits = 2,
escape = F,
align = "c"
) %>%
kable_paper(html_font = "Comic Sans MS",
full_width = T, bootstrap_options = c("striped", "bordered", "hover")) %>%
column_spec(2, color = "white", background = ifelse(fgseaResTidy$Normalised_Enrichment_Score < 0, "red", "green")) %>% footnote("Using Kable")
###Plots enrichment scores
FGSEA_plot <- (ggbarplot(fgseaResTidy,
x = "Pathway",
y = "Normalised_Enrichment_Score",
fill = "Adjusted_p_value",
#fill = "Size",
#palette = "jco",
sort.val = "desc",
label = FALSE,
xlab = FALSE,
size = 1,
width = 1,
)
+ scale_x_discrete(labels = function(x) str_wrap(x, 15))
+ scale_fill_steps(
low = "#132B43",
high = "#56B1F7",
space = "Lab",
na.value = "grey50",
guide = "coloursteps",
aesthetics = "fill",
name = "P-value",
n.breaks = 5
)) %>%
ggpar(#legend.title = list(color = "P-values"),
rotate = TRUE,
title = title,
submain = paste0(control, " vs ", test),
caption = paste0("GSEA with ", pathways, " gene sets from MSigDB"),
ggtheme = theme_pubr(),
#ggtheme = theme(legend.direction = "vertical"),
xlab = "",
ylab = "Normalized Enrichment Score",
font.title = c("bold", "brown", 18),
font.subtitle = c("bold", "dark grey", 14),
font.caption = c("bold.italic", "royal blue", 12),
font.legend = c("bold", "black", 6),
font.x = c("bold", "black", "11"),
#x.text.angle = 90,
font.y = c("bold", "black", "11"),
font.tickslab = c("bold", "black", "8"),
font.family = "Calibri",
)
#ggsave(plots_dir, title, "_", pathways, "_FGSEA_plot.png")
GSEA_return <- list(FGSEA_plot)
FGSEA_plot
}
##kable table of results
}
Annotate_genes_results <- function(res, species='human'){
if(species=='human'){
res$GENENAME <- mapIds(org.Hs.eg.db,
keys=row.names(res),
column="GENENAME",
keytype="ENSEMBL",
multiVals="first")
res$SYMBOL <- mapIds(org.Hs.eg.db,
keys=row.names(res),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$ENTREZID <- mapIds(org.Hs.eg.db,
#EnsDb.Hsapiens.v86,
keys=row.names(res),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
return(res)
}else if(species=='mouse'){
res$GENENAME <- mapIds(org.Mm.eg.db,
keys=row.names(res),
column="GENENAME",
keytype="ENSEMBL",
multiVals="first")
res$SYMBOL <- mapIds(org.Mm.eg.db,
keys=row.names(res),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$ENTREZID <- mapIds(org.Mm.eg.db,
#EnsDb.Hsapiens.v86,
keys=row.names(res),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
return(res)
}else if(species=='macaque'){
res$GENENAME <- mapIds(org.Mmu.eg.db,
keys=row.names(res),
column="GENENAME",
keytype="ENSEMBL",
multiVals="first")
res$SYMBOL <- mapIds(org.Mmu.eg.db,
keys=row.names(res),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$ENTREZID <- mapIds(org.Mmu.eg.db,
#EnsDb.Hsapiens.v86,
keys=row.names(res),
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
return(res)
}else if(species=='pig'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="sscrofa_gene_ensembl",
host="useast.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'entrezgene_id'), mart=ensembl))
colnames(annots) <- c('ENSEMBL', 'ENTREZID')
res <- merge(as.data.frame(res), annots, by.x='Row.names', by.y='ENSEMBL')
rownames(res) <- make.unique(res$Row.names)
return(res)
}else if(species=='rabbit'){
ensembl = useMart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="ocuniculus_gene_ensembl",
host="useast.ensembl.org")
annots <- as.data.frame(getBM(attributes = c('ensembl_gene_id', 'entrezgene_id'), mart=ensembl))
colnames(annots) <- c('ENSEMBL', 'ENTREZID')
res <- merge(as.data.frame(res), annots, by.x='Row.names', by.y='ENSEMBL')
rownames(res) <- make.unique(res$Row.names)
return(res)
}else{
print('please supply a valid species within the config.yml file')
}
}
gsea_rnk <- function(results_annotated, results_dir, test="test", control="control"){
gseaInput <- results_annotated %>%
dplyr::filter(!is.na(ENTREZID), !is.na(log2FoldChange)) %>%
arrange(log2FoldChange)
gsea_input_rnk <- dplyr::select(gseaInput, ENTREZID, log2FoldChange)
write.table(gsea_input_rnk, results_dir,col.name=FALSE,sep="\t",row.names=FALSE,quote=FALSE)
}
GSEA_plots <- function(pathways, title, results_dir, control="control", test="test"){
##read in the annotated results table
results_annotated <- results_dir
##create ranked gene list for fgsea
gseaInput <- results_annotated %>%
dplyr::filter(!is.na(ENTREZID), !is.na(log2FoldChange)) %>%
arrange(log2FoldChange)
ranks <- pull(gseaInput,log2FoldChange)
names(ranks) <- gseaInput$SYMBOL
###choose pathway databases to load.
##REACTOME GENE SETS
HS_CP_REACTOME <- msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:REACTOME")
HS_CP_REACTOME <- split(x = HS_CP_REACTOME$gene_symbol, f = HS_CP_REACTOME$gs_name)
##HALLMARKS GENE SETS
HS_HALLMARK <- msigdbr(species = "Homo sapiens", category = "H")
HS_HALLMARK <- split(x = HS_HALLMARK$gene_symbol, f = HS_HALLMARK$gs_name)
##KEGG GENE SETS
HS_CP_KEGG <- msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:KEGG")
HS_CP_KEGG <- split(x = HS_CP_KEGG$gene_symbol, f = HS_CP_KEGG$gs_name)
##Choose gene sets list
#pathways <- HS_HALLMARK
pathway_dbs <- list("HS_HALLMARK" = HS_HALLMARK, "HS_CP_KEGG" = HS_CP_KEGG, "HS_CP_REACTOME" = HS_CP_REACTOME)
a <-c("HS_HALLMARK", "HS_CP_KEGG", "HS_CP_REACTOME")
b <- a[str_which(a, str_to_upper(pathways))]
pathways <- pathway_dbs[[b]]
##GSEA analysis using the "ranks" table against the pathways in the database.
fgseaRes <- fgsea(pathways, ranks, minSize=15, maxSize = 500, nperm=1000)
## Show results in a nice table
fgseaResTidy <- fgseaRes %>%
as_tibble() %>%
arrange(desc(NES)) %>%
dplyr::filter(padj <= 0.05) %>%
dplyr::mutate(Pathway = str_replace_all(pathway, "_", " "),
Adjusted_p_value = padj,
Normalised_Enrichment_Score = NES,
Size = size,
Key_genes = leadingEdge) %>%
dplyr::select(Pathway, Normalised_Enrichment_Score, Size, Adjusted_p_value)
#%>% dplyr::filter(abs(Normalised_Enrichment_Score) > 2.3)
##save results table
#return(fgseaResTidy)
write.table(fgseaResTidy,
file = paste0("results_gsea/", title, "_fgsea_", b ,"_pathways.tsv"),
col.name=TRUE,
sep="\t",
na = "NA",
append = FALSE,
row.names=FALSE,
quote=FALSE)
if(dim(fgseaResTidy)[1] == 0){
return("no data")
}
##kable table of results
FGSEA_Results <- fgseaResTidy %>% kbl(col.names = c("Pathway", "Normalised Enrichment Score", "Size", "Adjusted p-value"),
digits = 2,
escape = F,
align = "c"
) %>%
kable_paper(html_font = "Comic Sans MS",
full_width = T, bootstrap_options = c("striped", "bordered", "hover")) %>%
column_spec(2, color = "white", background = ifelse(fgseaResTidy$Normalised_Enrichment_Score < 0, "red", "green")) %>% footnote("Using Kable")
###Plots enrichment scores
FGSEA_plot <- (ggbarplot(fgseaResTidy,
x = "Pathway",
y = "Normalised_Enrichment_Score",
fill = "Adjusted_p_value",
#fill = "Size",
#palette = "jco",
sort.val = "desc",
label = FALSE,
xlab = FALSE,
size = 1,
width = 1,
)
+ scale_x_discrete(labels = function(x) str_wrap(x, 15))
+ scale_fill_steps(
low = "#132B43",
high = "#56B1F7",
space = "Lab",
na.value = "grey50",
guide = "coloursteps",
aesthetics = "fill",
name = "P-value",
n.breaks = 5
)) %>%
ggpar(#legend.title = list(color = "P-values"),
rotate = TRUE,
title = title,
submain = paste0(control, " vs ", test),
caption = paste0("GSEA with ", pathways, " gene sets from MSigDB"),
ggtheme = theme_pubr(),
#ggtheme = theme(legend.direction = "vertical"),
xlab = "",
ylab = "Normalized Enrichment Score",
font.title = c("bold", "brown", 18),
font.subtitle = c("bold", "dark grey", 14),
font.caption = c("bold.italic", "royal blue", 12),
font.legend = c("bold", "black", 6),
font.x = c("bold", "black", "11"),
#x.text.angle = 90,
font.y = c("bold", "black", "11"),
font.tickslab = c("bold", "black", "8"),
font.family = "Arial",
)
FGSEA_plot
#ggsave(plots_dir, title, "_", pathways, "_FGSEA_plot.png")
GSEA_return <- list(FGSEA_Results, FGSEA_plot)
return(GSEA_return)
}
' Function to visualise enrichment results using a barplot
#'
#' \code{xEnrichBarplot} is supposed to visualise enrichment results using a barplot. It returns an object of class "ggplot".
#'
#' @param eTerm an object of class "eTerm"
#' @param top_num the number of the top terms (sorted according to FDR or adjusted p-values). If it is 'auto', only the significant terms (see below FDR.cutoff) will be displayed
#' @param displayBy which statistics will be used for displaying. It can be "fc" for enrichment fold change (by default), "adjp" or "fdr" for adjusted p value (or FDR), "pvalue" for p value, "zscore" for enrichment z-score
#' @param FDR.cutoff FDR cutoff used to declare the significant terms. By default, it is set to 0.05. This option only works when setting top_num (see above) is 'auto'
#' @param bar.label logical to indicate whether to label each bar with FDR. By default, it sets to true for bar labelling
#' @param bar.label.size an integer specifying the bar labelling text size. By default, it sets to 3
#' @param bar.color either NULL or fill color names ('lightyellow-orange' by default)
#' @param bar.width bar width. By default, 80% of the resolution of the data
#' @param wrap.width a positive integer specifying wrap width of name
#' @param font.family the font family for texts
#' @param signature logical to indicate whether the signature is assigned to the plot caption. By default, it sets TRUE showing which function is used to draw this graph
#' @return an object of class "ggplot"
#' @note none
#' @export
#' @seealso \code{\link{xEnricherGenes}}, \code{\link{xEnricherSNPs}}, \code{\link{xEnrichViewer}}
#' @include xEnrichBarplot.r
#' @examples
#' \dontrun{
#' # Load the XGR package and specify the location of built-in data
#' library(XGR)
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata/"
#'
#' # 1) load eQTL mapping results: cis-eQTLs significantly induced by IFN
#' cis <- xRDataLoader(RData.customised='JKscience_TS2A', RData.location=RData.location)
#' ind <- which(cis$IFN_t > 0 & cis$IFN_fdr < 0.05)
#' df_cis <- cis[ind, c('variant','Symbol','IFN_t','IFN_fdr')]
#' data <- df_cis$variant
#'
#' # 2) Enrichment analysis using Experimental Factor Ontology (EFO)
#' # Considering LD SNPs and respecting ontology tree
#' eTerm <- xEnricherSNPs(data, ontology="EF", include.LD="EUR", LD.r2=0.8, ontology.algorithm="lea", RData.location=RData.location)
#'
#' # 3) Barplot of enrichment results
#' bp <- xEnrichBarplot(eTerm, top_num="auto", displayBy="fc")
#' #pdf(file="enrichment_barplot.pdf", height=6, width=12, compress=TRUE)
#' print(bp)
#' #dev.off()
#' }
#'
#' # 4) use font family (Arial)
#' \dontrun{
#' BiocManager::install("extrafont")
#' library(extrafont)
#' font_import()
#' fonttable()
#' ## creating PDF files with fonts
#' library(extrafont)
#' loadfonts()
#' bp <- xEnrichBarplot(eTerm, top_num="auto", displayBy="fc", font.family="Arial Black")
#' pdf(file="enrichment_barplot_fonts.pdf", height=6, width=12, family="Arial Black")
#' print(bp)
#' dev.off()
#' }
xEnrichBarplot <- function(eTerm, top_num=10, displayBy=c("fc","adjp","fdr","zscore","pvalue"), FDR.cutoff=0.05, bar.label=TRUE, bar.label.size=3, bar.color='lightyellow-orange', bar.width=0.8, wrap.width=NULL, font.family="sans", signature=TRUE)
{
displayBy <- match.arg(displayBy)
if(is.null(eTerm)){
warnings("There is no enrichment in the 'eTerm' object.\n")
return(NULL)
}
## when 'auto', will keep the significant terms
df <- xEnrichViewer(eTerm, top_num="all")
if(top_num=='auto'){
top_num <- sum(df$adjp<FDR.cutoff)
if(top_num <= 1){
top_num <- 10
}
}
df <- xEnrichViewer(eTerm, top_num=top_num, sortBy="adjp")
## text wrap
if(!is.null(wrap.width)){
width <- as.integer(wrap.width)
res_list <- lapply(df$name, function(x){
x <- gsub('_', ' ', x)
y <- strwrap(x, width=width)
if(length(y)>1){
paste0(y[1], '...')
}else{
y
}
})
df$name <- unlist(res_list)
}
name <- height <- direction <- hjust <- NULL