getNormData: Return depth and batch corrected data

Description Usage Arguments Value Author(s) See Also Examples

View source: R/srcLineagePulse_getFits.R

Description

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

Usage

1
2
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
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)

LineagePulse documentation built on Nov. 8, 2020, 7:01 p.m.