whitening (version 1.1.1)

lusc: TCGA LUSC Data

Description

A preprocessed sample of gene expression and methylation data as well as selected clinical covariates for 130 patients with lung squamous cell carcinoma (LUSC) as available from The Cancer Genome Atlas (TCGA) database (Kandoth et al. 2013).

Usage

data(lusc)

Format

lusc$rnaseq2 is a 130 x 206 matrix containing the calibrated gene expression levels of 206 genes for 130 patients.

lusc$methyl is a 130 x 234 matrix containing the methylation levels of 234 probes for 130 patients.

sex is a vector recording the sex (male vs. female) of the 130 patients.

packs is the number of cigarette packs per year smoked by each patient.

survivalTime is number of days to last follow-up or the days to death.

censoringStatus is the vital status (0=alive, 1=dead).

Details

This data set is used to illustrate CCA-based data integration in Jendoubi and Strimmer (2019) and also described in Wan et al. (2016).

References

Jendoubi, T., Strimmer, K.: A whitening approach to probabilistic canonical correlation analysis for omics data integration. BMC Bioinformatics 20:15 <DOI:10.1186/s12859-018-2572-9>

Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., andJ. F. McMichael, Q.Z., Wyczalkowski, M.A., Leiserson, M.D.M., Miller, C.A., Welch, J.S., Walter, M.J., Wendl, M.C., Ley, T.J., Wilson, R.K., Raphael, B.J., Ding, L.: Mutational landscape and significance across 12 major cancer types. Nature 502, 333--339 (2013). <DOI:10.1038/nature12634>

Wan, Y.-W., Allen, G.I., Liu, Z.: TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 32, 952--954 (2016). <DOI:10.1093/bioinformatics/btv677>

Examples

# NOT RUN {
# load whitening library
library("whitening")

# load TGCA LUSC data set
data(lusc)

names(lusc)
#"rnaseq2"         "methyl"          "sex"             "packs"          
#"survivalTime"    "censoringStatus" 

dim(lusc$rnaseq2) # 130 206 gene expression
dim(lusc$methyl)  # 130 234 methylation level

# }
# NOT RUN {
library("survival")
s = Surv(lusc$survivalTime, lusc$censoringStatus)
plot(survfit(s ~ lusc$sex), xlab = "Years", ylab = "Probability of survival", lty=c(2,1), lwd=2)
legend("topright", legend = c("male", "female"), lty =c(1,2), lwd=2)
# }
# NOT RUN {
# }