plotFiltering: plotFiltering

View source: R/plotFiltering.R

plotFilteringR 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,
keep_all_below_boundary = TRUE,
enforce_left_cutoff = TRUE,
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

keep_all_below_boundary

(boolean) Ensures that no cells below the intact cell distribution are removed. This should almost always be set to true. Default = TRUE

enforce_left_cutoff

(boolean) Prevents a U-shape in the filtering plot. Identifies the cell with the lowest mitochondrial fraction that is set to be discarded, it ensures that no cells with lower library complexity (further left) and higher mitochondrial percentage (further up) than it are kept. Default = TRUE

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)
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)
plotFiltering(sce, model)

greenelab/miQC documentation built on June 2, 2022, 4:06 a.m.