In PNNL-CompBio/amlresistancenetworks: Calculates networks that lead to AML resistance to drugs

knitr::opts_chunk$set(echo = TRUE)
require(dplyr)
require(amlresistancenetworks)

Get Data

This package has formatted the Gilteritnib-treated AML cells into a tidied data frame so they can be easily processed. Here is a summary of the samples collected so that we can better analyze them.

Samples collected

gilt.data<-readRDS(system.file('gilteritinibData.Rds',package='amlresistancenetworks'))
prot.univ<-unique(gilt.data$Gene)

#view as table
samps<-gilt.data%>%
  dplyr::select(c(Sample,ligand,CellLine,treatment))%>%distinct()

DT::datatable(samps)

Compute differential expression between early and late

Here we compare early to late treatments

late.data<-gilt.data%>%
    subset(treatment%in%(c('None','Late Gilteritinib')))%>%
    subset(ligand=='None')%>%
    dplyr::select(Gene,Sample,CellLine,treatment,value)



late.diffs<-amlresistancenetworks::computeFoldChangePvals(late.data,control='None',conditions=c("Late Gilteritinib"))

molm.late.diffs<-amlresistancenetworks::computeFoldChangePvals(subset(late.data,CellLine=='MOLM14'),control='None',conditions=c("Late Gilteritinib"))
mv411.late.diffs<-amlresistancenetworks::computeFoldChangePvals(subset(late.data,CellLine=='MV411'),control='None',conditions=c("Late Gilteritinib"))


mean.diffs<-rbind(mutate(late.diffs,CellLine='Both'),
                  mutate(molm.late.diffs,CellLine='MOLM14'),
                  mutate(mv411.late.diffs,CellLine='MV411'))
#count the proteins at our significance threshold
prot.counts=mean.diffs%>%
    subset(p_adj<0.05)%>%
    group_by(Condition,CellLine)%>%
    summarize(`ProteinsDiffEx`=n_distinct(Gene))

DT::datatable(prot.counts)

GSEA enrichment of early vs. late

tot.diff<-late.diffs%>%dplyr::select(Gene,value=condition_to_control)

DT::datatable(subset(late.diffs,p_adj<0.05))


combined.diff=computeGSEA(tot.diff,prot.univ)

if(nrow(as.data.frame(combined.diff))>0){
  enrichplot::ridgeplot(combined.diff,showCategory=25)+ggplot2::ggtitle("GO Terms for all early vs late")
    ggplot2::ggsave('bothCells_early_vs_late_GO.png',width=16,height=8)

}

Now separate out by cell types to make sure that the effects are the same.

GSEA enrichment of MV411 cells only

genes.with.values=mv411.late.diffs%>%
    ungroup()%>%
    dplyr::select(Gene,value=condition_to_control)

DT::datatable(subset(mv411.late.diffs,p_adj<0.05))

mv411.late=computeGSEA(genes.with.values,prot.univ)

enrichplot::ridgeplot(mv411.late,showCategory=25)+ggplot2::ggtitle("GO Terms for MV411 Early vs Late")
ggplot2::ggsave('MV411_early_late_GO.png',width=16,height=8)

GSEA enrichment of MOLM cells only

genes.with.values=molm.late.diffs%>%
    ungroup()%>%
    dplyr::select(Gene,value=condition_to_control)

molm.late=computeGSEA(genes.with.values,prot.univ)

enrichplot::ridgeplot(molm.late,showCategory=25)+ggplot2::ggtitle("GO Terms for MOLM Early vs Late")
  ggplot2::ggsave('Molm14_early_late_GO.png',width=16,height=8)

DT::datatable(subset(molm.late.diffs,p_adj<0.05))

PNNL-CompBio/amlresistancenetworks documentation built on Feb. 8, 2025, 11:27 a.m.