scSubtype related code，Is it possible to call directly？ about brca_cell_atlas

[zhangjl-work]

Thank you very much for your reply, I will communicate with the person you recommended！ Thanks again！

[dlroden]

Hi, thanks for your interest in our work. Could you please provide more detail to allow us to understand your question more clearly? At a minimum, a reproducible example of the input data, the specific code that you are running and the output would be needed to help further. Thanks

[zhangjl-work]

hi,thank you for your reply！ I use your trained file NatGen_Supplementary_table_S4.csv，as input file。 and use my own rds file。the following is my code. library(Seurat) # read in scsubtype gene signatures sigdat <- read.csv("NatGen_Supplementary_table_S4.csv") temp_allgenes <- c(as.vector(sigdat[,"Basal_SC"]), as.vector(sigdat[,"Her2E_SC"]), as.vector(sigdat[,"LumA_SC"]), as.vector(sigdat[,"LumB_SC"])) temp_allgenes <- unique(temp_allgenes[!temp_allgenes == ""]) #读取数据 allfile <- list.files("/PROJ2/development/zhangjunling/project/breast_cancer/scSubtype/data/", pattern = 'rds') rds_list <- list() for (i in allfile) { print (i) file_path <- paste0("./data/",i) Mydata <- readRDS(file_path) #Read in the single cell RDS object as 'Mydata' #Mydata <- readRDS("./B_epi_filter_T.rds") Mydata <- ScaleData(Mydata, features=temp_allgenes) ***@***.******@***.***) #calculate mean scsubtype scores outdat <- matrix(0, nrow=ncol(sigdat), ncol=ncol(tocalc), dimnames=list(colnames(sigdat), colnames(tocalc))) for(i in 1:ncol(sigdat)){ row <- as.character(sigdat[,i]) row<-unique(row[row != ""]) genes<-which(rownames(tocalc) %in% row) temp<-apply(tocalc[genes,],2,function(x){mean(as.numeric(x),na.rm=TRUE)}) outdat[i,]<-as.numeric(temp) } final<-outdat[which(rowSums(outdat,na.rm=TRUE)!=0),] final<-as.data.frame(final) is.num <- sapply(final, is.numeric);final[is.num] <- lapply(final[is.num], round, 4) finalm<-as.matrix(final) ##Scaling scores function before calling the highest Call center_sweep <- function(x, row.w = rep(1, nrow(x))/nrow(x)) { get_average <- function(v) sum(v * row.w)/sum(row.w) average <- apply(x, 2, get_average) sweep(x, 2, average) } ##Obtaining the highest call finalmt<-as.data.frame(t(finalm)) #print (head(finalmt)) finalm.sweep.t<-center_sweep(finalmt) Finalnames<-colnames(finalm.sweep.t)[max.col(finalm.sweep.t,ties.method="first")] finalm.sweep.t$SCSubtypeCall <- Finalnames result <- as.data.frame(table(Finalnames)) print (result) } Below is my output： [1] "G_epi_filter_LN1.rds" Centering and scaling data matrix Finalnames Freq 1 Basal_SC 1184 2 Her2E_SC 1116 3 LumA_SC 1464 4 LumB_SC 1559 [1] "G_epi_filter_LN2.rds" Centering and scaling data matrix Finalnames Freq 1 Basal_SC 906 2 Her2E_SC 848 3 LumA_SC 1073 4 LumB_SC 1111 [1] "G_epi_filter_T.rds" Centering and scaling data matrix Finalnames Freq 1 Basal_SC 754 2 Her2E_SC 766 3 LumA_SC 923 4 LumB_SC 936 Seems to split my data equally across the four subtypes，why is that？

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2022年6月27日 下午6:54，Daniel Roden ***@***.***> 写道： Hi, thanks for your interest in our work. Could you please provide more detail to allow us to understand your question more clearly? At a minimum, a reproducible example of the input data, the specific code that you are running and the output would be needed to help further. Thanks — Reply to this email directly, view it on GitHub <#16 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AP747ND4PRHSX4YJIGDINZDVRGB7HANCNFSM5X6TTC4Q>. You are receiving this because you authored the thread.

[RegnerM2015]

Hi @zhangjl-work,

You actually have to integrate your data with their training data first. Then you apply the scSubtyping script to the rescaled integrated expression data (your data + their training data).

Applying the scSubtyping script directly to your data alone will result in a random or even distribution of subtype calls.

[zhangjl-work]

Thank you very much for your reply, but there is one more thing I don't understand. Question 1: Do I have to have my own training set? If I don't have my own training set data, can I use the training set data from your article? Question 2: What is the file format of the training set? The format of the TNBCmerged_Training_SwarbrickInferCNV.txt file, can you take a few lines to see it? Question 3: What does the SinglecellMolecularSubtypesignaturesonlytumor_SEPTEMBER2019.tx file in Calculatingscoresandplotting.R do? Can you take a few lines and take a look? Looking forward to your reply, thanks!

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2022年6月28日 上午1:08，Matt Regner ***@***.***> 写道： Hi @zhangjl-work <https://github.com/zhangjl-work>, You actually have to integrate your data with their training data first. Then you apply the scSubtyping script to the rescaled integrated expression data (your data + their training data). Applying the scSubtyping script directly to your data alone will result in a random or even distribution of subtype calls. — Reply to this email directly, view it on GitHub <#16 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AP747NCKZO4XTUB4CTWGPATVRHNXXANCNFSM5X6TTC4Q>. You are receiving this because you were mentioned.

[RegnerM2015]

Hi @zhangjl-work,

Question 1: Yes, use the training set data from the article.

Question 2: The format of the training set should be four Seurat objects (saved as RDS files). There should be one rds file for each subtype (LumA, LumB, Her2, and Basal). I am not familiar with the file TNBCmerged_Training_SwarbrickInferCNV.txt, perhaps @dlroden can clarify.

Question 3: I think SinglecellMolecularSubtypesignaturesonlytumor_SEPTEMBER2019.txt holds the gene signatures for each molecular subtype and Calculatingscoresandplotting.R computes the signature enrichment scores for these gene signatures.

I hope @dlroden and @sunnyzwu can clarify these.

[shufanzhang]

Hi @zhangjl-work,
Is your problem solved? I also want to know the format of the input data for scSubtype and where to get the training set file.