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

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

Get Data

gilt.data<-readRDS(system.file('gilteritinibData.Rds',package='amlresistancenetworks'))


#rcalculate differences and p-values

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

total.mean.diffs<-amlresistancenetworks::computeFoldChangePvals(early.data,control='None',conditions=c("FL","FGF2"))%>%
  subset(p_adj<0.05)

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"))%>%subset(p_adj<0.05)

Computing networks from diffex proteins

This part runs the network

## Not run: 
library("PCSF")
data("STRING")

ppi <- construct_interactome(STRING)

vals<-total.mean.diffs%>%
    subset(Condition=='FL')%>%
    dplyr::select(condition_to_control,Gene)

terms<-vals$condition_to_control
names(terms)<-vals$Gene


fl.subnet <- PCSF_rand(ppi, terms, n=1000, r=0.3,w = 4, b = 50, mu = 0.0005)

vals<-total.mean.diffs%>%
    subset(Condition=='FGF2')%>%
    dplyr::select(condition_to_control,Gene)

terms<-vals$condition_to_control
names(terms)<-vals$Gene


fgf2.subnet <- PCSF_rand(ppi, terms, n=1000,r=0.3,w = 4, b = 50, mu = 0.0005)


vals<-late.diffs%>%
  #  subset(Condition=='FGF2')%>%
    dplyr::select(condition_to_control,Gene)

terms<-vals$condition_to_control
names(terms)<-vals$Gene

late.subnet <- PCSF_rand(ppi, terms, n=1000,r=0.3,w = 4, b = 50, mu = 0.0005)
#
## End(Not run)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

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