## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----message = FALSE, warning = FALSE----------------------------------------- library(trena) ## ----------------------------------------------------------------------------- load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData")) ## ----echo=FALSE, fig.width = 6------------------------------------------------ hist(mtx.sub, main = "Expression Matrix Data") ## ----message = FALSE---------------------------------------------------------- mtx.asinh <- asinh(mtx.sub) ## ----echo=FALSE, fig.width = 6------------------------------------------------ hist(mtx.asinh, main = "VOOM-Transformed Expression Matrix Data") ## ----------------------------------------------------------------------------- variance.filter <- VarianceFilter(mtx.assay = mtx.asinh, targetGene = "MEF2C", varSize = 0.5) tf.list <- getCandidates(variance.filter) str(tf.list) ## ----------------------------------------------------------------------------- # Sp db.address <- system.file(package="trena", "extdata") genome.db.uri <- paste("sqlite:/",db.address,"mef2c.neighborhood.hg38.gtfAnnotation.db", sep = "/") project.db.uri <- paste("sqlite:/",db.address,"mef2c.neigborhood.hg38.footprints.db", sep = "/") # Create the specs for the MEF2C gene region tss <- 88904257 start <- tss - 1000 end <- tss + 1000 chrom <- "chr5" gene.regions <- data.frame(chrom=chrom, start=start, end=end, stringsAsFactors = FALSE) # Create a filter using the geneCenteredSpec option footprint.filter <- FootprintFilter(genomeDB = genome.db.uri, footprintDB = project.db.uri, regions = gene.regions) # Run the getCandidates method tbl <- getCandidates(footprint.filter)[[1]] str(tbl) ## ----message = FALSE, warning= FALSE------------------------------------------ library(MotifDb) tbl.tfs <- associateTranscriptionFactors(MotifDb, tbl, source="MotifDb", expand.rows=TRUE) str(tbl.tfs) ## ----------------------------------------------------------------------------- lasso.solver <- LassoSolver(mtx.assay = mtx.asinh, targetGene = "MEF2C", candidateRegulators = tbl.tfs$geneSymbol ) ## ----------------------------------------------------------------------------- set.seed(10) tbl <- run(lasso.solver) str(tbl) ## ----------------------------------------------------------------------------- set.seed(10) lasso.solver.keep <- LassoSolver(mtx.assay = mtx.asinh, targetGene = "MEF2C", candidateRegulators = tbl.tfs$geneSymbol, keep.metrics = TRUE ) tbl.keep <- run(lasso.solver.keep) str(tbl.keep) ## ----------------------------------------------------------------------------- lasso.solver.lenient <- LassoSolver(mtx.assay = mtx.asinh, targetGene = "MEF2C", candidateRegulators = tbl.tfs$geneSymbol, lambda = 0.05 ) tbl.lenient <- run(lasso.solver.lenient) str(tbl.lenient) ## ----------------------------------------------------------------------------- ensemble.solver <- EnsembleSolver(mtx.assay = mtx.asinh, targetGene = "MEF2C", candidateRegulators = tbl.tfs$geneSymbol) tbl.out <- run(ensemble.solver) tbl.out ## ----------------------------------------------------------------------------- ensemble.full <- EnsembleSolver(mtx.assay = mtx.asinh, targetGene = "MEF2C", candidateRegulators = tbl.tfs$geneSymbol, solverNames = c("lasso","pearson","randomforest","ridge"), geneCutoff = 1 ) tbl.full <- run(ensemble.full) str(tbl.full)