plotFiltering {miQC}R Documentation

plotFiltering

Description

Function to plot which cells will be kept and removed given their posterior probability of belonging to the compromised distribution.

Usage

plotFiltering(
  sce,
  model = NULL,
  posterior_cutoff = 0.75,
  palette = c("#999999", "#E69F00"),
  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.

posterior_cutoff

(numeric) The posterior probability of a cell being part of the compromised distribution, a number between 0 and 1. Any cells below the appointed cutoff will be marked to keep. Default = 0.75

palette

(character) Color palette. A vector of length two containing custom colors. Default = c("#999999", "#E69F00").

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)
library(BiocParallel)
sce <- ZeiselBrainData()
mt_genes <- grepl("^mt-",  rownames(sce))
feature_ctrls <- list(mito = rownames(sce)[mt_genes])
sce <- addPerCellQC(sce, subsets = feature_ctrls, BPPARAM = MulticoreParam())
model <- mixtureModel(sce)
plotFiltering(sce, model)

[Package miQC version 1.0.0 Index]