getNormData {LineagePulse} | R Documentation |
The data normalisation is based on the model normalisation used by and inferred by LineagePulse, e.g. for data visualisation.
getNormData(matCounts, lsMuModel, vecGeneIDs, boolDepth = TRUE, boolBatch = TRUE)
matCounts |
(numeric matrix genes x cells) Count data. |
lsMuModel |
(list) Mean parameter model parameters. |
vecGeneIDs |
(vector of strings) Gene IDs for which mean model fits are to be extracted. |
boolDepth |
(bool) [Default TRUE] Whether to normalize for sequencing depth. |
boolBatch |
(bool) [Default TRUE] Whether to normalize for batch. |
(numeric matrix genes x cells) Input data normalized by library size factors (optional) and by inferred batch factors (optional).
David Sebastian Fischer
Called by fitZINB
. Can be called by user.
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 batch correction on alternative model: # Use H1 model fits. matNormData <- getNormData( matCounts = lsSimulatedData$counts, lsMuModel = lsMuModelH1(objLP), vecGeneIDs = rownames(lsSimulatedData$counts)[1], boolDepth = TRUE, boolBatch = TRUE)