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Melody Jin
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Melody Jin
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Oct 31, 2021
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,45 @@ | ||
| computeAUC<- function(result, markers){ | ||
| auc.values<- as.data.frame(matrix(0, nrow=2, ncol=ncol(result$obs.pval)+1)) | ||
| colnames(auc.values)<- c("test", colnames(result$obs.pval)) | ||
| auc.values$test<- c("limma","permutation") | ||
| for (ct in 1:ncol(result$obs.pval)){ | ||
| curr.ct.name<- colnames(result$obs.pval)[ct] | ||
| if (curr.ct.name %in% names(markers)){ | ||
| resp=list() | ||
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| resp.g <- as.vector(ifelse(test=(row.names(result$perm.pval.adj) %in% markers[[curr.ct.name]]), | ||
| yes=1, no=0)) | ||
| resp[[curr.ct.name]]<-resp.g | ||
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| pred.perm<-result$perm.pval.adj[,curr.ct.name] | ||
| pred.limma<-result$obs.pval.adj[,curr.ct.name] | ||
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| roc_perm <-roc( response=resp[[curr.ct.name]], predictor= pred.perm,plot=FALSE, | ||
| legacy.axes=TRUE, percent=FALSE) | ||
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| roc_limma <-roc( response =resp[[curr.ct.name]], predictor=pred.limma, plot=FALSE, | ||
| legacy.axes=TRUE, percent=FALSE) | ||
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| rocp_df <- as.data.frame(cbind("sensitivity" = roc_perm$sensitivities, | ||
| "FPP" = 1-roc_perm$specificities, | ||
| "AUC" = roc_perm$auc, | ||
| "test.name" =rep("permutation", length(roc_perm$sensitivities)))) | ||
| rocl_df <- as.data.frame(cbind("sensitivity" = roc_limma$sensitivities, | ||
| "FPP" = 1- roc_limma$specificities, | ||
| "AUC" = roc_limma$auc, | ||
| "test.name" =rep("limma", length(roc_limma$sensitivities)))) | ||
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| roc_df<- rbind(rocp_df, rocl_df) | ||
| roc_df$sensitivity<- as.numeric(roc_df$sensitivity) | ||
| roc_df$FPP<- as.numeric(roc_df$FPP) | ||
| roc_df$AUC<- as.numeric(roc_df$AUC) | ||
| auc.values[auc.values$test=="limma",curr.ct.name]<- roc_limma$auc | ||
| auc.values[auc.values$test=="permutation",curr.ct.name]<- roc_perm$auc | ||
| }else{ | ||
| auc.values[auc.values$test=="limma",curr.ct.name]<- 0 | ||
| auc.values[auc.values$test=="permutation",curr.ct.name]<- 0 | ||
| } | ||
| } | ||
| return (auc.values) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,29 @@ | ||
| #' Title | ||
| #' | ||
| #' @param x | ||
| #' @param log | ||
| #' @param prior.count | ||
| #' @param lib.size | ||
| #' | ||
| #' @return | ||
| #' @export | ||
| #' | ||
| #' @examples | ||
| lognormCounts <- function(x, log = TRUE, prior.count = 0.5) | ||
| # Function to log normalise a counts matrix to median library size | ||
| # Input is counts matrix | ||
| # Genes as rows, cells as columns | ||
| # Belinda Phipson | ||
| # 26 February 2020 | ||
| { | ||
| x <- as.matrix(x) | ||
| lib.size <- colSums(x) | ||
| M <- median(lib.size) | ||
| if(log){ | ||
| prior.count.scaled <- lib.size/mean(lib.size)*prior.count | ||
| lib.size <- lib.size + 2*prior.count.scaled | ||
| log2(t((t(x)+prior.count.scaled)/lib.size*M)) | ||
| } | ||
| else t(t(x)/lib.size*M) | ||
| } | ||
|
|
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