getPostDrop {LineagePulse} | R Documentation |
Return posteriors of drop-out per gene and cell as matrix for chosen models.
getPostDrop(matCounts, lsMuModel, lsDispModel, lsDropModel, vecGeneIDs)
matCounts |
(count matrix genes x cells) Observed read counts, not observed are NA. |
lsMuModel |
(list) Object containing description of gene-wise mean parameter models. |
lsDispModel |
(list) Object containing description of gene-wise dispersion parameter models. |
lsDropModel |
(list) Object containing description of cell-wise drop-out parameter models. |
vecGeneIDs |
(vector of strings) [Default NULL] Gene IDs for which posteriors of drop-out are to be computed. |
(numeric matrix genes x cells) Posterior probability of observation not being generated by drop-out.
David Sebastian Fischer
lsSimulatedData <- simulateContinuousDataSet( scaNCells = 20, scaNConst = 2, scaNLin = 2, scaNImp = 2, scaMumax = 100, scaSDMuAmplitude = 3, vecNormConstExternal=NULL, vecDispExternal=rep(20, 6), vecGeneWiseDropoutRates = rep(0.1, 6)) objLP <- runLineagePulse( counts = lsSimulatedData$counts, dfAnnotation = lsSimulatedData$annot, strMuModel = "impulse") # Get posterior of drop-out on alternative model: # Use H1 model fits. vecPosteriorDropoutFits <- getPostDrop( matCounts = lsSimulatedData$counts, lsMuModel = lsMuModelH1(objLP), lsDispModel = lsDispModelH1(objLP), lsDropModel = lsDropModel(objLP), vecGeneIDs = rownames(lsSimulatedData$counts)[1])