sortGeneTrajectories {LineagePulse} | R Documentation |
Sorts inferred gene trajectories by peak time in continuous covariate. Optional: Can create a heatmap of the gene trajectories sorted according to peak time. The heatmap is based on z-scores.
sortGeneTrajectories(vecIDs, lsMuModel, dirHeatmap = NULL)
vecIDs |
(vector of strings) Names of genes to cluster. |
lsMuModel |
(list) Object containing description of gene-wise mean parameter models. |
dirHeatmap |
(str directory) [Default NULL] Directory to which heatmap is saved to. Return heatmap object if NULL. |
list (length 3) If dirHeatmap is not NULL, only vecSortedGenes is returned and the two heatmaps are printed to pdfs in the directory dirHeatmap. vecSortedGenes: (string vector number of IDs) hmGeneSorted: genes sorted by peak time in continuous covariate hmGeneClusters: genes sorted by clustering
David Sebastian Fischer
Called by user.
lsSimulatedData <- simulateContinuousDataSet( scaNCells = 100, scaNConst = 10, scaNLin = 10, scaNImp = 10, scaMumax = 100, scaSDMuAmplitude = 3, vecNormConstExternal=NULL, vecDispExternal=rep(20, 30), vecGeneWiseDropoutRates = rep(0.1, 30)) matDropoutPredictors <- as.matrix(data.frame( log_means = log(rowMeans(lsSimulatedData$counts)+1) )) objLP <- runLineagePulse( counts = lsSimulatedData$counts, dfAnnotation = lsSimulatedData$annot, strMuModel = "splines", scaDFSplinesMu = 6, strDropModel="logistic", matPiConstPredictors = matDropoutPredictors) lsHeatmaps <- sortGeneTrajectories( vecIDs = objLP$dfResults[which(objLP$dfResults$padj < 0.01),]$gene, lsMuModel = lsMuModelH1(objLP), dirHeatmap = NULL) #print(lsHeatmaps$hmGeneSorted)