testDropout: Test for existance of drop-out with log-likelihood ratio test

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

Description

Performs one test for entire data set.

Usage

testDropout(objLP)

Arguments

objLP

(LineagePulseObject) LineagePulseObject with fitted null and alternative models.

Value

(data frame) Summary of hypothesis test

• Gene: Gene ID.

• p: P-value for existance of drop-out in data set. If high, the data can be explained with models based on NB noise and zero-inflation is not necessary: The null hypothesis of no zero-inflation cannot be rejected.

• loglik_zinb: Loglikelihood of full model with ZINB noise (all genes).

• loglik_nb: Loglikelihood of reduced model with NB noise (all genes).

• df_full: Degrees of freedom of full model with ZINB noise (all genes).

• df_red: Degrees of freedom of reduced model with NB noise (all genes).

Author(s)

David Sebastian Fischer

See Also

Called by user.

Examples

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$annot,
    strMuModel = "splines", scaDFSplinesMu = 6,
    strDropModel="logistic", 
    matPiConstPredictors = matDropoutPredictors)
testDropout(objLP)$p

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