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Plot Trend of Variance Fractions Vs Count Magnitude

Source: R/plots.R
plotTrendVP-methods.Rd

Plot trend of variance fractions for a specified component versus count magnitude for each gene and cell cluster

Usage

plotTrendVP(fit, vp, component, ...)

# S4 method for class 'lucida,data.frame'
plotTrendVP(fit, vp, component, ...)

Arguments

fit

object returned by lucida()

vp

data.frame from fitVarPart()

component

variance component to extract from vp

...

additional arguments

Value

Plot of variance fraction vs count magnitude

Examples

library(SingleCellExperiment)

# Load example data 
data(example_sce, package="muscat")
sce <- example_sce

# Compute library size for each cell
sce$libSize <- colSums(counts(sce))

# Specify regression formula and cell annotation 
form <- ~ group_id + (1|sample_id)
fit <- lucida(sce, form, "cluster_id", verbose=FALSE)
#> B cells 
#> CD14+ Monocytes 
#> CD4 T cells 
#> CD8 T cells 
#> FCGR3A+ Monocytes 

# Model with only intercept and random effect
form <- ~ (1|sample_id)
fit.null <- lucida(sce, form, "cluster_id", verbose=FALSE)
#> B cells 
#> CD14+ Monocytes 
#> CD4 T cells 
#> CD8 T cells 
#> FCGR3A+ Monocytes 

# Variance partitioning analysis
vp <- fitVarPart(fit, fit.null)

plotTrendVP(fit, vp, "CountNoise")
#> Warning: Removed 41 rows containing missing values or values outside the scale range
#> (`geom_smooth()`).