A small example using the smoking data set, containing normalized
transcript measurements for 51 subjects (23 “never-smoked” and 34
“smokers”) and 22283 transcripts of lung tissue. See Spira et al. (2004)
I have removed that data set temporarily until I solve a problem with
upload the package to CRAN
fit a ranger model
library(ranger)
rf1 <-ranger(y=y.numeric ,x=smoking_data,importance="impurity",seed=123, num.trees = 10000,
classification=TRUE)
t2 <-count_variables(rf1)
imp<-log(rf1$variable.importance)
#png("./supp_figures/smoking_log_importances.png")
plot(density(imp),xlab="log importances",main="")
#dev.off()
# starting from a randomForest model
library(RFlocalfdr)
library(randomForest)
sert.seed(123)
rf2 <-randomForest(y=factor(y.numeric) ,x=smoking_data,importance=TRUE, ntree = 10000)
imp.rf2 <- log(rf2$importance[,"MeanDecreaseGini"])
t2.rf2 <-varUsed(rf2)
plot(density(imp.rf2),xlab="log importances",main="")
cutoffs <- c(2,3,4,5)
res.con<- determine_cutoff(imp.rf2,t2.rf2 ,cutoff=cutoffs,plot=c(2,3,4,5))
plot(cutoffs,res.con[,3],pch=15,col="red",cex=1.5,ylab="max(abs(y - t1))")
cutoffs[which.min(res.con[,3])]
#2
Determine a cutoff to get a unimodal density.
See @ref(fig:log_importances) for the log importances. They are
clearly multimodal and we try to determine a cutoff so that we are left
with a unimodal distribution.
cutoffs <- c(2,3,4,5)
#png("./supp_figures/smoking_data_determine_cutoff.png")
res.con<- determine_cutoff(imp,t2,cutoff=cutoffs,plot=c(2,3,4,5))
#dev.off()
#png("./supp_figures/smoking_data_determine_cutoffs_2.png")
plot(cutoffs,res.con[,3],pch=15,col="red",cex=1.5,ylab="max(abs(y - t1))")
#dev.off()
cutoffs[which.min(res.con[,3])]
fit RFlocalfdr
We select a cutoff of 3 and fit the RFlocalfdr model
temp<-imp[t2 > 3]
temp <- temp - min(temp) + .Machine$double.eps
qq <- plotQ(temp,debug.flag = 1)
ppp<-run.it.importances(qq,temp,debug.flag = 0)
#png("./supp_figures/smoking_significant_genes.png")
#aa<-significant.genes(ppp,temp,cutoff=0.05,do.plot=1)
aa<-significant.genes(ppp,temp,cutoff=0.05,debug.flag=0,do.plot=TRUE,use_95_q=TRUE)
#dev.off()
length(aa$probabilities) # 17
aa<-significant.genes(ppp,temp,cutoff=0.05,debug.flag=0,do.plot=TRUE,use_95_q=FALSE)
length(aa$probabilities) # 19
The option do.plot=1 returns a plot containing
the
- histogram of the importances in magenta
- fdr in black – axis scale on right
- alpha value for significance – axis scale on right
The option do.plot=2 returns the same plot with the
addition of
- fitted curve using estimates_C_0.95 in red
- 0.95 quantile of the fitted distribution, also in red
- cutoff for significant genes (in orange)
- abline(v = object$C_0.95, lwd = 2, col = “blue”) what are these
- abline(v = object$cc, lwd = 2, col = “purple”)
devtools::install_github("parsifal9/RFlocalfdr", build_vignettes = TRUE, force = TRUE)
library(RFlocalfdr)
data(smoking)
?smoking
y<-smoking$y
smoking_data<-smoking$rma
y.numeric <-ifelse((y=="never-smoked"),0,1)
library(ranger)
rf1 <-ranger(y=y.numeric ,x=smoking_data,importance="impurity",seed=123, num.trees = 10000,
classification=TRUE)
t2 <-count_variables(rf1)
imp<-log(rf1$variable.importance)
#png("./supp_figures/smoking_log_importances.png")
plot(density(imp),xlab="log importances",main="")
#dev.off()
cutoffs <- c(2,3,4,5)
#png("./supp_figures/smoking_data_determine_cutoff.png")
res.con<- determine_cutoff(imp,t2,cutoff=cutoffs,plot=c(2,3,4,5))
#dev.off()
#png("./supp_figures/smoking_data_determine_cutoffs_2.png")
plot(cutoffs,res.con[,3],pch=15,col="red",cex=1.5,ylab="max(abs(y - t1))")
#dev.off()
cutoffs[which.min(res.con[,3])]
temp<-imp[t2 > 3]
temp <- temp - min(temp) + .Machine$double.eps
qq <- plotQ(temp,debug.flag = 1)
ppp<-run.it.importances(qq,temp,debug.flag = 0)
#png("./supp_figures/smoking_significant_genes.png")
#aa<-significant.genes(ppp,temp,cutoff=0.05,do.plot=1)
aa<-significant.genes(ppp,temp,cutoff=0.05,debug.flag=0,do.plot=TRUE,use_95_q=TRUE)
#dev.off()
length(aa$probabilities) # 17 -- Roc gets 30
aa<-significant.genes(ppp,temp,cutoff=0.05,debug.flag=0,do.plot=TRUE,use_95_q=FALSE)
length(aa$probabilities) # 19-- Roc gets 30
Spira, A. E., J. Beane, V. Pinto-Plata, A. Kadar, G. Liu, V. Shah, B.
Celli, and J. S. Brody. 2004. “Gene Expression Profiling of Human
Lung Tissue from Smokers with Severe Emphysema.” Am J Respir
Cell Mol Biol.
