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[8e0848]: / Fig7_multivariable_regression_eNet_survival.R

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GIT_HOME="/research/users/ppolonen/git_home/ImmunogenomicLandscape-BloodCancers/"

source(file.path(GIT_HOME, "common_scripts/visualisation/plotting_functions.R"))

source(file.path(GIT_HOME, "common_scripts/statistics/functions_statistics.R"))

source(file.path(GIT_HOME, "common_scripts/statistics/statistics_wrappers.R"))

source(file.path(GIT_HOME, "common_scripts/featurematrix/compute.pairwise.R"))

source(file.path(GIT_HOME, "common_scripts/featurematrix/functions_generate_fm.R"))



library(RColorBrewer)

library(survival)

library(data.table)

library(ggplot2)

library(ComplexHeatmap)



validate.model=function(dataset,coef, ind=1, months=F, summary="3quantiles", filt=NULL){

  load(dataset)

  

  if(!is.null(filt)){

    logicalv=list(filt&logicalv[[ind]])

    ind=1

  }

  

  time=TIME[logicalv[[ind]]]

  time[time==0]=0.1

  status=STATUS[logicalv[[ind]]]

  

  genelist=rownames(coef)

  

  test.data=cbind(gexp[logicalv[[ind]], colnames(gexp)%in%genelist,drop=F], immunoscore[logicalv[[ind]], colnames(immunoscore)%in%genelist,drop=F], samp[logicalv[[ind]], colnames(samp)%in%genelist,drop=F])

  

  if(!(all(genelist%in%colnames(test.data)))){

    warning(paste("Features not found from test data:", paste(genelist[!genelist%in%colnames(test.data)], collapse = ",")))

    coef=coef[rownames(coef)%in%colnames(test.data),,drop=F]

    genelist=rownames(coef)

  }

  

  m=test.data[,match(genelist, colnames(test.data))]

  riskPI=as.numeric(coef) %*% data.matrix(t(m))

  

  print(summary(coxph(Surv(time, status) ~ PI.test, data.frame("PI.test"=as.numeric(riskPI)))))

  

  # plot validation data:

  print(fun.kapplanMeier(time, status, CONTINUOUS = as.numeric(riskPI), conf.int = F, MONTHS=months, PVAL=1,LWD = 0.5, CONTINUOUS_SUMMARY = summary, INDIVIDUAL_GROUPS=F, NAME = ""))

  

  return(riskPI)

}



validate.model.cox=function(dataset,coef, ind=1, months=F, summary="3quantiles", filt=NUL, NAME=""){

  load(dataset)

  

  if(!is.null(filt)){

    logicalv=list(filt&logicalv[[ind]])

    ind=1

  }

  

  time=TIME[logicalv[[ind]]]

  time[time==0]=0.1

  status=STATUS[logicalv[[ind]]]

  

  genelist=rownames(coef)

  

  test.data=cbind(gexp[logicalv[[ind]], colnames(gexp)%in%genelist,drop=F], immunoscore[logicalv[[ind]], colnames(immunoscore)%in%genelist,drop=F], samp[logicalv[[ind]], colnames(samp)%in%genelist,drop=F])

  

  if(!(all(genelist%in%colnames(test.data)))){

    warning(paste("Features not found from test data:", paste(genelist[!genelist%in%colnames(test.data)], collapse = ",")))

    coef=coef[rownames(coef)%in%colnames(test.data),,drop=F]

    genelist=rownames(coef)

  }

  

  m=test.data[,match(genelist, colnames(test.data))]

  riskPI=as.numeric(coef) %*% data.matrix(t(m))

  

  fit=coxph(Surv(time, status) ~ PI.test, data.frame("PI.test"=as.numeric(riskPI)))



  a=summary(fit)

  

  pval=a$coefficients[,5]

  coef=a$conf.int[,c(1,3,4)]

  univ=data.frame(rownames(a$coefficients),t(coef), pval, a$concordance[1],NAME, stringsAsFactors = F)

  rownames(univ)=NULL

  colnames(univ)=c("Feature", "exp(coef)", "lower .95", "upper .95", "P", "concordance", "Name")

  return(univ)

}





cols=data.frame("name"=c("Subtype","ImmunoScores","Inhibitory ligand", "Stimulatory ligand", "Stromal/cancer gene (Rho > 0)", "Stromal/cancer gene (Rho < 0)","CTL/NK gene","Clinical", "CGA", "MDS-signature gene"),

                "color"=c("#acb839","#5e2883","#1f78b4","#b2df8a","#377eb8","grey50","#e41a1c","brown", "#d7a85b", "indianred"), stringsAsFactors = F)





Plot.model=function(dataset, coef, type.feat, ind=1, NAME=NULL){

  load(dataset)

  

  time=TIME[logicalv[[ind]]]

  time[time==0]=0.1

  status=STATUS[logicalv[[ind]]]

  

  genelist=rownames(coef)

  

  gexp=gexp[logicalv[[ind]], colnames(gexp)%in%genelist,drop=F]

  immunoscore=immunoscore[logicalv[[ind]], colnames(immunoscore)%in%genelist,drop=F]

  samp=samp[logicalv[[ind]], colnames(samp)%in%genelist,drop=F]

  

  test.data=cbind(gexp, immunoscore, samp)

  

  if(!(all(genelist%in%colnames(test.data)))){

    warning(paste("Features not found from test data:", paste(genelist[!genelist%in%colnames(test.data)], collapse = ",")))

    coef=coef[rownames(coef)%in%colnames(test.data),,drop=F]

    genelist=rownames(coef)

  }

  

  m=test.data[,match(genelist, colnames(test.data))]

  riskPI=as.numeric(coef) %*% data.matrix(t(m))

  

  gene.annot=data.frame(type.feat[match(rownames(coef), type.feat[,1]),], "HR"=as.numeric(exp(coef)), stringsAsFactors = F)

  gene.annot=gene.annot[order(gene.annot$Type, -gene.annot$HR),]

  fm.m=t(m)

  fm.m=fm.m[match(gene.annot$Feature, rownames(fm.m)),]

  

  rownames(fm.m)[rownames(fm.m)%in%colnames(gexp)]=paste0("N:GEXP:", rownames(fm.m)[rownames(fm.m)%in%colnames(gexp)])

  rownames(fm.m)[rownames(fm.m)%in%colnames(immunoscore)]=paste0("N:GSVA:", rownames(fm.m)[rownames(fm.m)%in%colnames(immunoscore)])

  rownames(fm.m)[rownames(fm.m)%in%gene.annot$Feature[gene.annot$Type%in%c("Subtype")]]=paste0("B:SAMP:", rownames(fm.m)[rownames(fm.m)%in%gene.annot$Feature[gene.annot$Type%in%c("Subtype")]])

  rownames(fm.m)[rownames(fm.m)%in%gene.annot$Feature[gene.annot$Type%in%c("Clinical")]]=paste0("N:CLIN:", rownames(fm.m)[rownames(fm.m)%in%gene.annot$Feature[gene.annot$Type%in%c("Clinical")]])

  

  rownames(gene.annot)=rownames(fm.m)

  HR=prettyNum(signif(gene.annot[,3], 3))

  names(HR)=rownames(gene.annot)

  

  annotdf=data.frame("OS"=status, "RiskPI"=t(riskPI), stringsAsFactors = F)

  rownames(annotdf)=colnames(fm.m)

  

  plot.complexHM.fm(feats = rownames(gene.annot), text_annot = HR, feats.barplot = c("RiskPI"), split.columns = F, annotdf = annotdf, fm.f = fm.m, order_columns = colnames(fm.m)[order(as.numeric(riskPI))], use_raster = F, order_rows = F, NAME = NAME)



}



fun_forestplot=function(data, NAME="data", BOX=0.1,cex=2, colorv="black"){

  library(forestplot)

  

  txt.df=data.frame("Feature"=data$Feature, "Cohort"=gsub("_", " ", data$Name), "P"=signif(data$P, 2), stringsAsFactors = F)

  txt.df$Feature[duplicated(txt.df$Feature)]=""

  txt.df=rbind(c("Feature", "Cohort", "P"), txt.df)

  

  coef.df=data.frame("HR"=data$`exp(coef)`, "lower .95"=data$`lower .95`, "upper .95"=data$`upper .95`)

  coef.df=rbind(c(NA, NA, NA), coef.df)

  

  xticks=seq(ifelse(min(data$`lower .95`)<0.5, 0, 0.5), min(c(5, max(data$`upper .95`))), by = 0.5)

  

  attr(xticks, "labels") = xticks%in%seq(-4, 4, by=1)

  

  forestplot(txt.df,coef.df, new_page = T, zero = c(0.98, 1.02), 

             clip =c(-1, 2), is.summary = c(T, rep(F, length(data$Name))),

             xticks=xticks,

             boxsize=BOX,

             xlab="HR",

             col=fpColors(box=colorv),

             txt_gp = fpTxtGp(label = list(gpar(fontfamily = "Helvetica", cex=cex*1.25),

                                           gpar(fontfamily = "Helvetica", col = "black", cex=cex)),

                              summary = gpar(fontfamily = "Helvetica", cex=cex*1.5),

                              ticks = gpar(fontfamily = "Helvetica", cex=cex),

                              xlab  = gpar(fontfamily = "Helvetica", cex = cex*1.5)))

  

}



# survival analysis for all scores:

# many coefficients in survival analysis, using regularization and elastic net to select features for cox model.



setwd("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/Published_data_figures")



#**************************************** training data DLBCL **************************************** 

load("Hemap_DLBCL_survival_data.Rdata")



# significant genes:

files=list.files(pattern = "tableS7")



# significant genes:

univariate.results=lapply(files[grepl("signif", files)], fread, data.table=F)

names(univariate.results)=files[grepl("signif", files)]



DLBCL=do.call(rbind, univariate.results[grep("DLBCL", names(univariate.results))])

DLBCL$Name=gsub("_RCHOP", "", DLBCL$Name)

DLBCL$Feature=gsub("-", ".", DLBCL$Feature)



# genes with FDR<0.2, beta to same direction and observed in 2 cohorts:

genes=table(DLBCL$Feature, DLBCL$`exp(coef)`>1)>1

genelist=rownames(genes)[rowSums(genes)==1]



# combine data:

time=TIME[logicalv[[5]]]

status=STATUS[logicalv[[5]]]



regression.data=cbind(gexp[logicalv[[5]], colnames(gexp)%in%genelist,], immunoscore[logicalv[[5]], colnames(immunoscore)%in%genelist,], samp[logicalv[[5]], colnames(samp)%in%genelist,])

all(genelist%in%colnames(regression.data))



# results_dlbcl=fun.cox.elasticnet(DATA_ORG = regression.data, time, status, summary.km = "3quantile", cores = 10, REPEATS = 100, percentage = 0, nfold = 10, min.elnet = 0, max.elnet = 0.1)

# save(results_dlbcl, file="Hemap_DLBCL_cox_datasets_filt_adj20_revision.Rdata")

load("Hemap_DLBCL_cox_datasets_filt_adj20_revision.Rdata")



# revision

# [1] 0.1

# [1] 0.0745611

# [1] 11.60345



annot=get(load("GSE98588_annot.Rdata"))



pdf("FigS7D_DLBCL_model_cox.pdf", height = 2.5, width = 2)

riskInd_Chapuy=validate.model("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients)

riskInd_Hemap=validate.model("Hemap_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, 5)

dev.off()



DLBCL_risk1=validate.model.cox("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, filt=annot$IPI%in%c(0, 1, 2), NAME = "DLBCL IPI 0-2")

DLBCL_risk2=validate.model.cox("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, filt=annot$IPI%in%c(3), NAME = "DLBCL IPI 3")

DLBCL_risk3=validate.model.cox("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, filt=annot$IPI%in%c(4,5), NAME = "DLBCL IPI 4-5")

DLBCL_risk4=validate.model.cox("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, filt=annot$COO_byGEP=="ABC", NAME = "DLBCL ABC")

DLBCL_risk5=validate.model.cox("GSE98588_DLBCL_survival_data.Rdata", results_dlbcl$coefficients, filt=annot$COO_byGEP=="GCB", NAME = "DLBCL GCB")



type.feat=unique(DLBCL[,c("Feature", "Type")])

type.feat=type.feat[order(type.feat[,2]),]



Plot.model(dataset = "GSE98588_DLBCL_survival_data.Rdata", coef = results_dlbcl$coefficients, type.feat = type.feat, ind = 1,NAME = "Fig7F_Chapuy_model")

# Plot.model(dataset = "Reddy_DLBCL_survival_data.Rdata", coef = results_dlbcl$coefficients, type.feat = type.feat, ind = 1,NAME = "Reddy_model")



#**************************************** training data MM **************************************** 

load("Hemap_MM_survival_data.Rdata")



# significant genes:

files=list.files(pattern = "tableS7")



# significant genes:

univariate.results=lapply(files[grepl("signif", files)], fread, data.table=F)

names(univariate.results)=files[grepl("signif", files)]



MM=do.call(rbind, univariate.results[grep("MM", names(univariate.results))])

MM$Feature=gsub("-", ".", MM$Feature)



# genes with FDR<0.2, beta to same direction and observed in 2 cohorts:

# use GSE19784 Cancer_Myeloma as training and GSE16716,GSE24080 Cancer_Myeloma as test set

genes=table(MM$Feature, MM$`exp(coef)`>1)>1

genelist=rownames(genes)[rowSums(genes)==1]

genelist=genelist[!genelist%in%"WHSC1_FGFR3_Ig"]



# combine data:

ind=1

time=TIME[logicalv[[ind]]]

time[time==0]=0.1

status=STATUS[logicalv[[ind]]]



regression.data=cbind(gexp[logicalv[[ind]], colnames(gexp)%in%genelist,drop=F], immunoscore[logicalv[[ind]], colnames(immunoscore)%in%genelist,drop=F], samp[logicalv[[ind]], colnames(samp)%in%genelist,drop=F])

all(genelist%in%colnames(regression.data))



# results_MM=fun.cox.elasticnet(DATA_ORG = regression.data, time, status, summary.km = "3quantile", cores = 10, REPEATS = 100, percentage = 0, nfold = 10)

# save(results_MM, file="Hemap_MM_cox_datasets_filt_adj20_revision.Rdata")

# [1] 0.05

# [1] 0.1089963

# [1] 14.10799



load("Hemap_MM_cox_datasets_filt_adj20_revision.Rdata")



pdf("FigS7C_MM_model_cox.pdf", height = 2.5, width = 2)

riskInd_CoMMpass=validate.model(dataset = "Hemap_MM_survival_data.Rdata", coef = results_MM$coefficients, ind=1, summary="75th_25th_percentile")

riskInd_CoMMpass=validate.model("CoMMpass_survival_data.Rdata", results_MM$coefficients, summary="75th_25th_percentile", months = T)

dev.off()



type.feat=unique(MM[,c("Feature", "Type")])

type.feat=type.feat[order(type.feat[,2]),]



Plot.model(dataset = "CoMMpass_survival_data.Rdata", coef = results_MM$coefficients, type.feat = type.feat, ind = 1,NAME = "Fig7E_CoMMpass_model")



load("CoMMpass_MM_subtypes.Rdata")



subtype=coordinates.subtype[match(colnames(riskInd_CoMMpass)[order(-riskInd_CoMMpass)],coordinates.subtype$ID),]

subtype$subtype[subtype$cluster=="CGA_Prolif"]="CGA_Prolif"



col=data.frame("subtype"=c("CCND1_Ig", "WHSC1_FGFR3_Ig", "Hyperdiploid_gain11q", "Hyperdiploid_gain1q", "MAF_Ig", "TRAF3_Aberrated", "CGA_Prolif"),

"color"=c("#e41a1b", "#357eb8", "#5eb45b", "#9b53a4", "#ff7d00", "#f8f875", "darkred"), stringsAsFactors = F)



load("CoMMpass_survival_data.Rdata")

MM_risk1=validate.model.cox("CoMMpass_survival_data.Rdata", results_MM$coefficients, filt=samp$ISS1==1, NAME = "CoMMpass ISS 1")

MM_risk2=validate.model.cox("CoMMpass_survival_data.Rdata", results_MM$coefficients, filt=samp$ISS2==1, NAME = "CoMMpass ISS 2")

MM_risk3=validate.model.cox("CoMMpass_survival_data.Rdata", results_MM$coefficients, filt=samp$ISS3==1, NAME = "CoMMpass ISS 3")



#**************************************** training data AML **************************************** 

load("Hemap_AML_survival_data.Rdata")



# significant genes:

files=list.files(pattern = "tableS7")



# significant genes:

univariate.results=lapply(files[grepl("signif", files)], fread, data.table=F)

names(univariate.results)=files[grepl("signif", files)]



AML=do.call(rbind, univariate.results[grep("AML", names(univariate.results))])

AML$Feature=gsub("-", ".", AML$Feature)



# genes with FDR<0.2, beta to same direction and observed in 2 cohorts:

genes=table(AML$Feature, AML$`exp(coef)`>1)>1

genelist=rownames(genes)[rowSums(genes)==1]

genelist=gsub("-", ".", genelist)



# combine data:

time=TIME[logicalv[[1]]]

time[time==0]=0.1

status=STATUS[logicalv[[1]]]



regression.data=cbind(gexp[logicalv[[1]], colnames(gexp)%in%genelist,drop=F], immunoscore[logicalv[[1]], colnames(immunoscore)%in%genelist,drop=F], samp[logicalv[[1]], colnames(samp)%in%genelist,drop=F])

all(genelist%in%colnames(regression.data))



# results_AML=fun.cox.elasticnet(DATA_ORG = regression.data, time, status, summary.km = "3quantile", cores = 10, REPEATS = 100, percentage = 0, nfold = 5)



# model revision

# [1] 0.06

# [1] 0.1982066

# [1] 11.88285



# save(results_AML, file="Hemap_AML_cox_datasets_filt_adj20_revision.Rdata")

load("Hemap_AML_cox_datasets_filt_adj20_revision.Rdata")

exp(results_AML$coefficients)



pdf("FigS7E_Risk_AML_validation.pdf", height = 2.5, width = 2)

riskInd_Hemap_AML=validate.model("Hemap_AML_survival_data.Rdata", results_AML$coefficients, summary = "75th_25th_percentile")

riskInd_BeatAML=validate.model("BeatAML_survival_data.Rdata", results_AML$coefficients, months=T, summary = "75th_25th_percentile")

riskInd_TCGA_AML=validate.model("TCGA_AML_survival_data.Rdata", results_AML$coefficients, summary = "75th_25th_percentile")

dev.off()



type.feat=unique(AML[,c("Feature", "Type")])

type.feat=type.feat[order(type.feat[,2]),]



# Plot.model(dataset = "Hemap_AML_survival_data.Rdata", coef = results_AML$coefficients, type.feat = type.feat, ind = 1,NAME = "Hemap_AML_model")

Plot.model(dataset = "BeatAML_survival_data.Rdata", coef = results_AML$coefficients, type.feat = type.feat, ind = 1,NAME = "FigS7G_BeatAML_model")

# Plot.model(dataset = "TCGA_AML_survival_data.Rdata", coef = results_AML$coefficients, type.feat = type.feat, ind = 1,NAME = "TCGA_AML_model")



load("BeatAML_survival_data.Rdata")

annot=get(load("BeatAML_fm_annot.Rdata"))



AML_risk1=validate.model.cox("BeatAML_survival_data.Rdata", results_AML$coefficients, filt=grepl("Adverse", annot$ELN2017), NAME = "BeatAML Adverse")

AML_risk2=validate.model.cox("BeatAML_survival_data.Rdata", results_AML$coefficients, filt=annot$ELN2017=="Intermediate", NAME = "BeatAML Intermediate")

AML_risk3=validate.model.cox("BeatAML_survival_data.Rdata", results_AML$coefficients, filt=grepl("Favorable", annot$ELN2017), NAME = "BeatAML Favorable")



dat=rbind(DLBCL_risk1, DLBCL_risk2, DLBCL_risk3, DLBCL_risk4, DLBCL_risk5, MM_risk1, MM_risk2, MM_risk3, AML_risk1, AML_risk2, AML_risk3)



pdf("FigS7F_Subset_data_model.pdf", width = 4, height = 3)

fun_forestplot(dat, "AML, MM, DLBCL", BOX=0.5, cex=0.5, colorv ="black")

dev.off()