getNormData: Return depth and batch corrected data

In YosefLab/LineagePulse: Differential expression analysis and model fitting for single-cell RNA-seq data

Description

The data normalisation is based on the model normalisation used by and inferred by LineagePulse, e.g. for data visualisation.

Usage

getNormData(matCounts, lsMuModel, vecGeneIDs, boolDepth = TRUE,
  boolBatch = TRUE)

Arguments

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.

Value

(numeric matrix genes x cells) Input data normalized by library size factors (optional) and by inferred batch factors (optional).

Author(s)

David Sebastian Fischer

See Also

Called by fitZINB. Can be called by user.

Examples

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)

YosefLab/LineagePulse documentation built on May 6, 2019, 2:19 p.m.