In gofflab/glmpd: Identification of context-specific drivers of a pattern projection via GLM fitting.

knitr::opts_chunk$set(echo = TRUE)

Test the getProjectionDrivers.R functions using data from the ALS analysis

#source('getProjectionDrivers.R')
#source('coeff_table_glmpd.R')
library(glmpd)

# Directories
#dataDir <- 'data/'

# load the counts mat, sample annotations, and projected p weights
#countsMat <- readRDS(paste(dataDir,'humanCountsMat.rds',sep=''))
countsMatFile <- system.file("extdata", "humanCountsMat.rds", package = "glmpd")
countsMat <- readRDS(countsMatFile)
#annotDF <- readRDS(paste(dataDir,'humanAnnotations.rds',sep=''))
annotDFFile <- system.file("extdata","humanAnnotations.rds", package = "glmpd")
annotDF <- readRDS(annotDFFile)
#POIs <- readRDS(paste(dataDir,'sexPatts_0_9thresh.rds',sep='')) # patterns of interest
POIsFile <- system.file("extdata","sexPatts_0_9thresh.rds", package = "glmpd")
POIs <- readRDS(POIsFile)

# limit to genes expressed in at least one sample
totCounts <- apply(countsMat,1,sum)
countsMat_exp <- countsMat[totCounts>0,]

# order factors to obtain desired baseline categories in annotations map
annotDF$group <- as.factor(as.character(annotDF$group))
annotDF$group <- factor(annotDF$group,levels=levels(annotDF$group)[c(2,1,3)])

# start by trying a couple patterns and a couple genes
POIs_test <- POIs[7:8,] # 7 is female, 8 is male
countsMat_exp_test <- countsMat_exp[c("XIST","IL22","TAGLN"),]

# designate model parameters
model_formula_str <- "~  patternWeights*sex + tissueType + patternWeights:tissueType + group + patternWeights:group"

fits <- getProjectionDrivers(countsMat_exp_test,annotDF,POIs_test,model_formula_str)
coeffTables <- coeff_table_glmpd(fits)

DT::datatable(coeffTables)

Session Information

sessionInfo()

gofflab/glmpd documentation built on April 11, 2021, 6:38 a.m.