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LigProb_lvl3_breast.Rmd
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---
title: "Probability"
---
```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
library(cmapR)
library(colorspace)
```
```{r load_data_lvl4, message=FALSE, warning=FALSE, include=FALSE}
if (exists("BRmat")) {
} else if (file.exists("~/Dropbox/GDB_archive/CMapCorr_files/200730_lvl3_breastonly.RData")) {
load("~/Dropbox/GDB_archive/CMapCorr_files/200730_lvl3_breastonly.RData")
} else {
source("200730_probs_mixall_breast_lvl3.R")
}
```
[Random forest model source code](https://github.com/BaderLab/Brendan_CCInxPred/blob/master/200730_probs_mixall_breast_lvl3.R)
Playing with test data first (10 iterations of leaving out one ligand-treated sample per training run).
## Unbalanced cell types
```{r testnobal_load,echo=FALSE,message=FALSE,warning=FALSE}
if (file.exists("~/Dropbox/GDB_archive/CMapCorr_files/200730_probs_mixall_breast_lvl3_nobal.RData")) {
load("~/Dropbox/GDB_archive/CMapCorr_files/200730_probs_mixall_breast_lvl3_nobal.RData")
} else {
source("200730_probs_mixall_breast_lvl3.R")
}
```
```{r testnobal_datacheck, echo=F,message=F,warning=F,fig.height=4.5,fig.width=9,fig.show='hold'}
temp_label <- as.factor(rep(names(scores_nobal),ncol(BRmat)))
temp_mean <- as.vector(sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,mean)))
temp_sd <- as.vector(sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,sd)))
Z <- sample(length(temp_label))
par(mfrow=c(1,2),mar=c(3,3,1,1),mgp=2:0)
plot(temp_mean[Z],temp_sd[Z],pch=".",cex=2,xlab="Mean of rep scores",ylab="SD of rep scores",
col=qualitative_hcl(length(levels(temp_label)),palette="dark3",alpha=0.7)[temp_label[Z]])
for (LIG in names(scores_nobal)) {
for (X in seq_along(scores_nobal[[LIG]])) {
scores_nobal[[LIG]][[X]]$label <- BRcdesc[rownames(scores_nobal[[LIG]][[X]]),"pert_iname"] == LIG
}
}
temp <- list(
neg_train = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "train" & !Y$label])))),
neg_test = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "test" & !Y$label])))),
pos_train = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "train" & Y$label])))),
pos_test = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "test" & Y$label]))))
)
boxplot(temp,ylim=0:1,ylab="Scores")
rm(list=c(grep("^temp",ls(),value=T),"LIG","X","Z"))
```
```{r testnobal_samplescores, echo=F,message=F,warning=F}
score_nobal <- sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,max))
rownames(score_nobal) <- rownames(BRcdesc)
class_nobal <- data.frame(
label=colnames(score_nobal)[apply(score_nobal,1,which.max)],
score=apply(score_nobal,1,max),
truth=BRcdesc$pert_iname,
stringsAsFactors=F
)
# class_nobal$truth[!class_nobal$truth %in% lig29] <- NA
sum(class_nobal$label == class_nobal$truth,na.rm=T) / sum(!is.na(class_nobal$truth))
```
```{r echo=F,message=F,warning=F}
```
# Level 4 data
```{r load_data_lvl4, message=FALSE, warning=FALSE, include=FALSE}
if (exists("BRmat")) {
} else if (file.exists("~/Dropbox/GDB_archive/CMapCorr_files/200731_lvl4_breastonly.RData")) {
load("~/Dropbox/GDB_archive/CMapCorr_files/200731_lvl4_breastonly.RData")
} else {
source("200731_probs_mixall_breast_lvl4.R")
}
```
[Random forest model source code](https://github.com/BaderLab/Brendan_CCInxPred/blob/master/200730_probs_mixall_breast_lvl3.R)
Playing with test data first (10 iterations of leaving out one ligand-treated sample per training run).
## Unbalanced cell types
```{r testnobal_load,echo=FALSE,message=FALSE,warning=FALSE}
if (file.exists("~/Dropbox/GDB_archive/CMapCorr_files/200731_probs_mixall_breast_lvl4_nobal.RData")) {
load("~/Dropbox/GDB_archive/CMapCorr_files/200731_probs_mixall_breast_lvl4_nobal.RData")
} else {
source("200731_probs_mixall_breast_lvl4.R")
}
```
```{r testnobal_datacheck, echo=F,message=F,warning=F,fig.height=4.5,fig.width=9,fig.show='hold'}
temp_label <- as.factor(rep(names(scores_nobal),ncol(BRmat)))
temp_mean <- as.vector(sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,mean)))
temp_sd <- as.vector(sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,sd)))
Z <- sample(length(temp_label))
par(mfrow=c(1,2),mar=c(3,3,1,1),mgp=2:0)
plot(temp_mean[Z],temp_sd[Z],pch=".",cex=2,xlab="Mean of rep scores",ylab="SD of rep scores",
col=qualitative_hcl(length(levels(temp_label)),palette="dark3",alpha=0.7)[temp_label[Z]])
for (LIG in names(scores_nobal)) {
for (X in seq_along(scores_nobal[[LIG]])) {
scores_nobal[[LIG]][[X]]$label <- BRcdesc[rownames(scores_nobal[[LIG]][[X]]),"pert_iname"] == LIG
}
}
temp <- list(
neg_train = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "train" & !Y$label])))),
neg_test = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "test" & !Y$label])))),
pos_train = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "train" & Y$label])))),
pos_test = unlist(lapply(scores_nobal,function(X)
unlist(lapply(X,function(Y) Y$score[Y$source == "test" & Y$label]))))
)
boxplot(temp,ylim=0:1,ylab="Scores")
rm(list=c(grep("^temp",ls(),value=T),"LIG","X","Z"))
```
```{r testnobal_samplescores, echo=F,message=F,warning=F}
score_nobal <- sapply(scores_nobal,function(X) apply(sapply(X,"[[","score"),1,max))
rownames(score_nobal) <- rownames(BRcdesc)
class_nobal <- data.frame(
label=colnames(score_nobal)[apply(score_nobal,1,which.max)],
score=apply(score_nobal,1,max),
truth=BRcdesc$pert_iname,
stringsAsFactors=F
)
# class_nobal$truth[!class_nobal$truth %in% lig29] <- NA
sum(class_nobal$label == class_nobal$truth,na.rm=T) / sum(!is.na(class_nobal$truth))
```