DrugScore function return NAN and 0

[saisaitian]

Hi,

Thanks for all the work on this tool. I am having trouble getting results for DrugScore function. I tried a few other tissue types including breast, kidney, lymphatic tissue and they seemed also return 0 and NAN in DrugScore function.

My code is below
#Drug repurposing using drugs/compounds
Gene.list<-readRDS("TNBC_genelist_limma.rds")
my_gene_info<-read.table(file="DrugReference/skin_gene_info.txt",sep="\t",header = T,quote = "")
my_drug_info<-read.table(file="DrugReference/skin_drug_info.txt",sep="\t",header = T,quote = "")
cmap.ref.profiles<-GetDrugRef(drug.response.path = 'DrugReference/skin_rankMatrix.txt',
probe.to.genes = my_gene_info, drug.info = my_drug_info)
Drug.ident.res = GetDrug(gene.data = Gene.list, drug.ref.profiles = cmap.ref.profiles, repurposing.unit = "drug", connectivity = "negative", drug.type="all")
saveRDS(Drug.ident.res,file="TNBC_drugs_limma_all.rds")

#Drug/compound score
library(cmapR)
SC.integrated<-readRDS("TNBC_SCdata.rds")
Gene.list<-readRDS("TNBC_genelist_limma.rds")
Drug.ident.res<-readRDS("TNBC_drugs_limma_all.rds")
GSE92742.gctx.path="./Lincs/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx"
GSE70138.gctx.path="./Lincs/GSE70138_Broad_LINCS_Level5_COMPZ_n118050x12328_2017-03-06.gctx"
Tissue="breast"

Drug.score<-DrugScore(SC.integrated=SC.integrated,
Gene.data=Gene.list,
Cell.type=NULL,
Drug.data=Drug.ident.res,
FDA.drug.only=F,
Case=Case,
Tissue="breast",
GSE92742.gctx=GSE92742.gctx.path,
GSE70138.gctx=GSE70138.gctx.path)

[hebinghb]

I noticed you used the parameter Case, which defined the samples used for calculating drug score, but didn't see where you define it in your code. I'm not sure if it is the issue. But if you used the wrong Case names that do not exist, it may result in NAN or 0 output of drug score. Here is the Case defined in the demo code:
#Case sample names
Case=c("PDX-110","PDX-332")

[NarainritKaruna]

Hi,

Tissue="muscle"
Cell.type = c("B-cells","Cardiomyocytes","Endocardial cells", "Endothelial cells", "Fibroblasts",
"Granulocytes", "Macrophages", "Pericytes", "Schwann cells", "Smooth muscle cells", "T-cells")
#DbCM
Case <- c("HFD_1", "HFD_2", "HFD_3")
Drug.score.bcell<-DrugScore(SC.integrated=SC.data,
Gene.data=Gene.list,
Cell.type= Cell.type,
Drug.data=Drug.ident.res,
FDA.drug.only=T,
Case=Case,
Tissue=Tissue,
GSE92742.gctx=GSE92742.gctx.path,
GSE70138.gctx=GSE70138.gctx.path)

I got the same problem for running all cell types together.

Then, try to look into each cell type; the score is shown. I think it might the process of calculation when putting all cell types together.

Drug.score.mac<-DrugScore(SC.integrated=SC.data,
Gene.data=Gene.list,
Cell.type="Macrophages",
Drug.data=Drug.ident.res,
FDA.drug.only=T,
Case=Case,
Tissue=Tissue,
GSE92742.gctx=GSE92742.gctx.path,
GSE70138.gctx=GSE70138.gctx.path)

[NarainritKaruna]

I figure out how the function calculated NaN (usually from 0/0)

I found that when

• D.D.genes = NULL because names(drug.responses.mean) = NULL then intersect(names(D.gene.expression),names(drug.responses.mean)) will return NULL

Then, It will be 0/0 by Ratio.treated <- length(D.genes.treated)/length(D.D.genes)

for (Drug in C.Drugs) {
D.genes.treated <- NULL
drug.treatments <- subset(data_infor,pert_iname == Drug)$sig_id
if(length(drug.treatments)>1){
drug.responses <- data[,drug.treatments]
drug.responses.mean <- apply(drug.responses,1,mean)
}else{
drug.responses <- data[,drug.treatments]
drug.responses.mean <- drug.responses
}
D.D.genes <- intersect(names(D.gene.expression),names(drug.responses.mean))
D.genes.treated <- D.gene.expression[D.D.genes]drug.responses.mean[D.D.genes]
D.genes.treated <- D.genes.treated[which(D.genes.treated>0)]
Mean.treated <- mean(D.genes.treated)
Ratio.treated <- length(D.genes.treated)/length(D.D.genes)
Coverage.treated <- Drug.coverage[Drug]/100
Treated.score <- (Ratio.treatedCoverage.treated)
Single.treated.score.list <- c(Single.treated.score.list,Treated.score)

Best,

[hebinghb]

Thanks for raising this issue. If drug.responses.mean got NULL, it came out because there is no treatment response data for the selected drugs in the selected L1000 tissue. If the both D.gene.expression and drug.responses.mean have value, but D.D.genes got NULL, it means there is no overlap between the disease-related differential genes and drug response genes.