Increasing evidence suggests that related cancers share alterations of common regulatory programs. Trans-associations of cancer risk variants mediated via molecular phenotypes, such as gene expression and protein levels, can help uncover these downstream mechanisms. In this paper we introduce TASTE (Trans Association using Shared factorization and TEsting), a summary statistic-based framework to identify protein sets that are trans- regulated by genetic variants associated with sets of biologically related cancers. TASTE consists of three steps: (1) TASTE-D, a low-rank matrix factorization to estimate shared and group-specific trans-association patterns across cancers; (2) TASTE-S, a sparse singular value decomposition to identify proteins driving shared effects; (3) TASTE-T, a competitive testing strategy for evaluating significance of trans-associations captured by the identified protein-set. TASTE is a R package for extracting protein sets that are trans-regulated by genetic variants associated with sets of biologically related cancers. The Illustration of the TASTE analysis pipeline is given below.
To install the package use the following code.
install.packages("devtools")
devtools::install_github("diptavo /TASTE")
#Load package
library(TASTE)TASTE comes with an illustrative data set that can be loaded directly after installing and loading the package. The dataset contains summary statistics for three blood-related cancers, corresponding to 2,815, 2,829, and 2,858 proteins, respectively. There are 2,698 proteins that are common across all three cancer groups. TASTE identifies the protein sets that are transregulated by genetic variants associated with the given set of blood cancer.
dat <- readRDS(system.file("extdata", "data_illustration.rds", package = "TASTE"))A detailed description of the method, input format, and example analysis is provided in the package tutorial: Vignette/TASTE