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loadOrganoidData.R
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require(reticulate)
library(readxl)
library(stringr)
library(dplyr)
library(tidyr)
library(nationalparkcolors)
library(pheatmap)
##get metadata file
#condaenv="C:\\Users\\gosl241\\OneDrive - PNNL\\Documents\\GitHub\\amlresistancenetworks\\renv\\python\\r-reticulate\\"
pal = nationalparkcolors::park_palette('Acadia')
#reticulate::use_condaenv(condaenv)
syn=reticulate::import('synapseclient')
sync=syn$login()
return(sync)
##get annotations
tabres = sync$tableQuery('SELECT zScore,totalCounts,specimenID,Symbol,individualID,experimentalCondition FROM syn22878645')
tabres2 = sync$tableQuery('SELECT zScore,totalCounts,specimenID,Symbol,individualID,experimentalCondition FROM syn21222341')
rnaseq=rbind(tabres$asDataFrame(), tabres2$asDataFrame())%>%
distinct()%>%
mutate(specimenID=str_replace_all(specimenID,fixed('NF0007-2-M+C'),'NF0007-2- M+C'))%>%
mutate(specimenID=str_replace_all(specimenID,fixed('NF0007-2-M+C+F'),'NF0007-2- M+C+F'))%>%
mutate(specimenID=str_replace_all(specimenID,"S $","S"))
badSpecs=c("NF0009-1 M","NF0009-1 M+C","NF0002-8-19- M+C+F","NF0002-8-19- D+C","NF0002-8-19- D+C+F","NF0002-8-19- S+C")
rnaseq<-rnaseq%>%subset(!specimenID%in%badSpecs)
annotes<-rnaseq%>%
dplyr::select(specimenID,individualID,experimentalCondition)%>%
separate(specimenID,into=c('altID','extra'),sep=' ',remove=F)%>%
mutate(altID=stringr::str_replace(altID,'-$',''))%>%
dplyr::select(-extra)%>%
distinct()
rownames(annotes)<-c()
annotes<-annotes%>%tibble::column_to_rownames('specimenID')
##update annote
annotes$Media<-rep("None",nrow(annotes))
annotes$Media[grep("DMEM",annotes$experimentalCondition)]<-'DMEM'
annotes$Media[grep("StemPro",annotes$experimentalCondition)]<-'StemPro'
annotes$Media[grep('Mammo',annotes$experimentalCondition)]<-'Mammo'
pats=annotes$altID[grep('patient',annotes$individualID)]
specs<-c(rownames(annotes)[grep('patient',rownames(annotes))],
'NF0002-8-19 M','NF0009-1- M+C+F','NF0012-3-6 M')
for(x in c('kines','Forskoline')){
annotes[[x]]<-FALSE
annotes[[x]][grep(x,annotes$experimentalCondition)]<-TRUE
# annotes[[x]]=as.character(annotes)
}
annotes<-annotes%>%dplyr::rename(Cytokines='kines')#%>%dplyr::select(-experimentalCondition)
nannotes<-apply(annotes,2,as.character)
rownames(nannotes)<-rownames(annotes)
orgs<-subset(annotes,experimentalCondition!='NaN')$altID
biga<-annotes%>%
subset(altID%in%orgs)%>%
tibble::rownames_to_column('specimenID')%>%
dplyr::select(-c(individualID,experimentalCondition))%>%
mutate(val=1) %>%
pivot_wider(names_from = Media,values_from = val) %>%
mutate(across(-c(Cytokines,Forskoline,altID,specimenID), ~replace_na(.x, 0))) %>%
mutate(across(-c(Cytokines,Forskoline,altID,specimenID), ~ifelse(.x==1, TRUE,FALSE)))
mat<-rnaseq%>%
dplyr::select(specimenID,zScore,Symbol)%>%
tidyr::pivot_wider(values_from=zScore,names_from=specimenID,
values_fn=list(zScore=mean),values_fill=list(zScore=0))%>%
tibble::column_to_rownames('Symbol')%>%as.matrix()
vars<-apply(mat,1,var,na.rm=T)%>%sort(decreasing=T)
#' getDifferencesInCOndition
#' Filters for a particular condition and data type
#' Computes limma differences at a p-value <0.05
#' @param mat
#' @param biga
#' @param cond
#' @param dataType
#' @param doPlot
#' @return number of genes
getDifferencesInCondition<-function(mat,biga,cond='None',dataType='geneExpression',doPlot=TRUE){
print(cond)
p1<-biga%>%dplyr::rename(val=cond)%>%
subset(val==TRUE)
p2<-biga%>%subset(None==TRUE)
res=limmaTwoFactorDEAnalysis(mat,p1$specimenID,p2$specimenID)%>%
dplyr::arrange(P.Value)
mres<-subset(res,adj.P.Val<0.05)
numGenes= nrow(mres)
if(numGenes>1 && doPlot){
pheatmap(mat[mres$featureID[1:min(50,numGenes)],biga$specimenID],cellheight=10, cellwidth=10,
annotation_col = as.data.frame(nannotes,stringsAsFactors = FALSE)%>%dplyr::select(-experimentalCondition),
filename=paste0(cond,'top50',dataType,'.pdf'))
}
##we should probably do some sort of GSEA enrichment poltting as well.
#print(head(res))
#nres<-res%>%dplyr::arrange(as.numeric(logFC))
return(numGenes)
}
#' Old plot using clusterProfiler
#' @export
#' @import org.Hs.eg.db
#' @import clusterProfiler
plotOldGSEA<-function(genes.with.values,prefix,gsea_FDR=0.05){
require(org.Hs.eg.db)
# mapping<-as.data.frame(org.Hs.egALIAS2EG)%>%
# dplyr::rename(Gene='alias_symbol')
# genes.with.values<-genes.with.values%>%
# dplyr::left_join(mapping,by='Gene')%>%
# arrange(desc(value))
genes.with.values<-arrange(genes.with.values,desc(value))
# print(head(genes.with.values))
genelist=genes.with.values$value
names(genelist)=genes.with.values$Gene
print(head(genelist))
genelist<-sort(genelist,decreasing=TRUE)
gr<-clusterProfiler::gseGO(unlist(genelist),ont="BP",keyType="SYMBOL",
OrgDb=org.Hs.eg.db,pAdjustMethod = 'BH',pvalueCutoff = gsea_FDR)#,eps=1e-10)
res<-filter(as.data.frame(gr),p.adjust<gsea_FDR)
if(nrow(res)==0)
return(gr)
all_gseaGO<-res %>%
dplyr::rename(pathway = 'Description') %>%
arrange(NES) %>%
dplyr::mutate(status = case_when(NES > 0 ~ "Up",
NES < 0 ~ "Down"),
status = factor(status, levels = c("Up", "Down"))) %>%
group_by(status) %>%
top_n(20, wt = abs(NES)) %>%
ungroup() %>%
ggplot2::ggplot(aes(x=reorder(pathway, NES), y=NES)) +
geom_bar(stat='identity', aes(fill=status)) +
scale_fill_manual(values = c("Up" = "darkred", "Down" = "dodgerblue4")) +
coord_flip() +
theme_minimal() +
theme(plot.title = element_text(face = "bold", hjust = 0.5, size = 18),
axis.title.x = element_text(size=16),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y=element_text(size = 14),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none") +
labs(title = "", y="NES") +#for some reason labs still works with orientation before cord flip so set y
ggtitle(paste('All',prefix))
ggsave(paste0("allRegProts_", prefix,"_gseaGO_plot.pdf"), all_gseaGO, height = 8.5, width = 11, units = "in")
enrichplot::ridgeplot(gr,showCategory = 50,fill='pvalue')+ggplot2::ggtitle(paste0("GO Terms for ",prefix))
ggplot2::ggsave(paste0(prefix,'_GO.pdf'),width=10,height=10)
df<-as.data.frame(gr)%>%mutate(Condition=prefix)
return(df)
}
##which genes are differentially expressed between all 'treated' and 'none'?
#'
#'limmaTwoFactorDEAnalysis
#'uses Osama's code to compute de from limma
#'@author Osama
#'@import limma
#'@param data matrix
#'@param group1 ids
#'@param group2 ids
limmaTwoFactorDEAnalysis <- function(dat, sampleIDs.group1, sampleIDs.group2) {
# Conduct DE expression analysis using limma from the expression matrix dat (group2 vs group1, group1 is reference)
#
# Args:
# dat: Expression data matrix, rows are genes, columns are samples
# sampleIDs.group1: Vector with ids of samples in reference group (eg. normal samples)
# sampleIDs.group2: Vector with ids of samples in interest group (eg. tumor samples)
#
# Returns:
# limma Differential Expression results.
#
#http://www.biostat.jhsph.edu/~kkammers/software/CVproteomics/R_guide.html
#http://genomicsclass.github.io/book/pages/using_limma.html
#https://wiki.bits.vib.be/index.php/Tutorial:_Testing_for_differential_expression_I
library(limma)
fac <- factor(rep(c(2,1), c(length(sampleIDs.group2), length(sampleIDs.group1))))
design <- model.matrix(~fac)
fit <- lmFit(dat[,c(sampleIDs.group2, sampleIDs.group1)], design)
fit <- eBayes(fit)
print(topTable(fit, coef=2))
res <- topTable(fit, coef=2, number=Inf, sort.by="none")
res <- data.frame(featureID=rownames(res), res, stringsAsFactors = F)
return(arrange(res,P.Value))
}
plotCorrelationBetweenSamps<-function(mat,annotes,prefix='geneExpression'){
orgs<-setdiff(annotes$altID,pats)
##now compute the correlation values
dlist<-lapply(orgs,function(pat){
iannote<-subset(annotes,altID==pat)%>%
dplyr::select(experimentalCondition,Media,Cytokines,Forskolin='Forskoline')
norm=iannote%>%subset(experimentalCondition=='None')%>%rownames()
print(norm)
norcors<-sapply(setdiff(rownames(iannote),norm),function(x)
cor(mat[,norm],mat[,x],method='spearman'))
pdat<-iannote%>%subset(experimentalCondition!="None")%>%
dplyr::select(Media,Cytokines,Forskolin)%>%
cbind(Similarity=norcors)
return(pdat)
})
names(dlist)<-orgs
ddf<-do.call(rbind,lapply(names(dlist),function(x) data.frame(Patient=x,dlist[[x]])))%>%
tibble::rownames_to_column('altID')
#write.csv(ddf,paste0(prefix,'orgCorrelations.csv'),row.names=F)
names(dlist)<-orgs
plist<-lapply(orgs,function(pat){
pdat<-dlist[[pat]]
pdat%>%ggplot(aes(y=Similarity,x=Media,shape=Forskolin,color=Cytokines))+
geom_point(aes(size=10))+scale_colour_manual(values=pal)+
ggtitle(pat)
})
library(cowplot)
res=cowplot::plot_grid(plotlist=plist)
ggsave(filename=paste0(prefix,'corPlots.pdf'),res,width=10)
return(ddf)
}