plotModel: plotModel

In greenelab/miQC: Flexible, probabilistic metrics for quality control of scRNA-seq data

plotModel

Description

Function to plot quality characteristics of cells in dataset, parameters of compromised and intact distributions, and posterior probability of each cell belonging to the compromised distribution.

Usage

plotModel(
sce,
model = NULL,
detected = "detected",
subsets_mito_percent = "subsets_mito_percent"
)

Arguments

sce	(SingleCellExperiment) Input data object.
model	(flexmix) Output of mixtureModel function, which should be explicitly called first to ensure stability of model parameters. Default = NULL.
detected	(character) Column name in sce giving the number of unique genes detected per cell. This name is inherited by default from scater's addPerCellQC() function.
subsets_mito_percent	(character) Column name in sce giving the percent of reads mapping to mitochondrial genes. This name is inherited from scater's addPerCellQC() function, provided the subset "mito" with names of all mitochondrial genes is passed in. See examples for details.

Value

Returns a ggplot object. Additional plot elements can be added as ggplot elements (e.g. title, customized formatting, etc).

Examples

library(scRNAseq)
library(scater)
sce <- ZeiselBrainData()
mt_genes <- grepl("^mt-",  rownames(sce))
feature_ctrls <- list(mito = rownames(sce)[mt_genes])
sce <- addPerCellQC(sce, subsets = feature_ctrls)
model <- mixtureModel(sce)
plotModel(sce, model)

greenelab/miQC documentation built on Jan. 6, 2023, 12:16 a.m.