Smoking data

Rob Dunne

Saturday, August 8, 2020

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

load the data

library(RFlocalfdr.data)
data(smoking)
?smoking 
y<-smoking$y
smoking_data<-smoking$rma
y.numeric <-ifelse((y=="never-smoked"),0,1)

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()
A small simulated data set. Each band contains blocks of size {1, 2, 4, 8, 16, 32, 64}, and each block consists of correlated (identical variables).

A small simulated data set. Each band contains blocks of size {1, 2, 4, 8, 16, 32, 64}, and each block consists of correlated (identical variables).

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

The option do.plot=2 returns the same plot with the addition of

sessionInfo()
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.