plotCellDensity: Plot density of cells in continuous covariate
In LineagePulse: Differential expression analysis and model fitting for single-cell RNA-seq data
Description
Usage
Arguments
Value
Author(s)
Examples
View source: R/srcLineagePulse_plotGene.R
Description
Uses kernel density estimate.
Usage
1
plotCellDensity(objLP)
Arguments
objLP
(LineagePulseObject) LineagePulseObject to base plot on.
Value
gplotKDE (ggplot object)
ggplot2 kernel density estimator plot.
Author(s)
David Sebastian Fischer
Examples
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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))
objLP <- runLineagePulse(
counts = lsSimulatedData$counts,
dfAnnotation = lsSimulatedData$annot,
strMuModel = "impulse")
gplotCellDensity <- plotCellDensity(objLP = objLP)
#print(gplotCellDensity)
LineagePulse documentation built on Nov. 8, 2020, 7:01 p.m.
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Description Usage Arguments Value Author(s) Examples
View source: R/srcLineagePulse_plotGene.R
Description
Uses kernel density estimate.
Usage
1 | plotCellDensity(objLP)
|
Arguments
objLP |
(LineagePulseObject) LineagePulseObject to base plot on. |
Value
gplotKDE (ggplot object) ggplot2 kernel density estimator plot.
Author(s)
David Sebastian Fischer
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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))
objLP <- runLineagePulse(
counts = lsSimulatedData$counts,
dfAnnotation = lsSimulatedData$annot,
strMuModel = "impulse")
gplotCellDensity <- plotCellDensity(objLP = objLP)
#print(gplotCellDensity)
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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