To install the package, please use the following.
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("miQC")This vignette provides a basic example of how to run miQC, which allows users to perform cell-wise filtering of single-cell RNA-seq data for quality control. Single-cell RNA-seq data is very sensitive to tissue quality and choice of experimental workflow; it’s critical to ensure compromised cells and failed cell libraries are removed. A high proportion of reads mapping to mitochondrial DNA is one sign of a damaged cell, so most analyses will remove cells with mtRNA over a certain threshold, but those thresholds can be arbitrary and/or detrimentally stringent, especially for archived tumor tissues. miQC jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to identify the low-quality cells in a given dataset.
For more information about the statistical background of miQC and for biological use cases, consult the miQC paper (Hippen et al. 2021).
You’ll need the following packages installed to run this tutorial:
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(scRNAseq)
library(scater)
library(flexmix)
library(splines)
library(miQC)
})To demonstrate how to run miQC on a single-cell RNA-seq dataset, we’ll use data from mouse brain cells, originating from an experiment by Zeisel et al (Zeisel et al. 2015), and available through the Bioconductor package scRNAseq.
sce <- ZeiselBrainData()
sce## class: SingleCellExperiment
## dim: 20006 3005
## metadata(0):
## assays(1): counts
## rownames(20006): Tspan12 Tshz1 ... mt-Rnr1 mt-Nd4l
## rowData names(1): featureType
## colnames(3005): 1772071015_C02 1772071017_G12 ... 1772066098_A12
## 1772058148_F03
## colData names(9): tissue group # ... level1class level2class
## reducedDimNames(0):
## mainExpName: gene
## altExpNames(2): repeat ERCCIn order to calculate the percent of reads in a cell that map to mitochondrial genes, we first need to establish which genes are mitochondrial. For genes listed as HGNC symbols, this is as simple as searching for genes starting with mt-. For other IDs, we recommend using a biomaRt query to map to chromosomal location and identify all mitochondrial genes.
mt_genes <- grepl("^mt-", rownames(sce))
feature_ctrls <- list(mito = rownames(sce)[mt_genes])
feature_ctrls## $mito
## [1] "mt-Tl1" "mt-Tn" "mt-Tr" "mt-Tq" "mt-Ty" "mt-Tk" "mt-Ta"
## [8] "mt-Tf" "mt-Tp" "mt-Tc" "mt-Td" "mt-Tl2" "mt-Te" "mt-Tv"
## [15] "mt-Tg" "mt-Tt" "mt-Tw" "mt-Tm" "mt-Ti" "mt-Nd3" "mt-Nd6"
## [22] "mt-Nd4" "mt-Atp6" "mt-Nd2" "mt-Nd5" "mt-Nd1" "mt-Co3" "mt-Cytb"
## [29] "mt-Atp8" "mt-Co2" "mt-Co1" "mt-Rnr2" "mt-Rnr1" "mt-Nd4l"miQC is designed to be run with the Bioconductor package scater, which has a built-in function addPerCellQC to calculate basic QC metrics like number of unique genes detected per cell and total number of reads. When we pass in our list of mitochondrial genes, it will also calculate percent mitochondrial reads.
sce <- addPerCellQC(sce, subsets = feature_ctrls)
head(colData(sce))## DataFrame with 6 rows and 21 columns
## tissue group # total mRNA mol well sex
## <character> <numeric> <numeric> <numeric> <numeric>
## 1772071015_C02 sscortex 1 21580 11 1
## 1772071017_G12 sscortex 1 21748 95 -1
## 1772071017_A05 sscortex 1 31642 33 -1
## 1772071014_B06 sscortex 1 32916 42 1
## 1772067065_H06 sscortex 1 21531 48 1
## 1772071017_E02 sscortex 1 24799 13 -1
## age diameter level1class level2class sum detected
## <numeric> <numeric> <character> <character> <numeric> <integer>
## 1772071015_C02 21 0.00 interneurons Int10 22354 4871
## 1772071017_G12 20 9.56 interneurons Int10 22869 4712
## 1772071017_A05 20 11.10 interneurons Int6 32594 6055
## 1772071014_B06 21 11.70 interneurons Int10 33525 5852
## 1772067065_H06 25 11.00 interneurons Int9 21694 4724
## 1772071017_E02 20 11.90 interneurons Int9 25919 5427
## subsets_mito_sum subsets_mito_detected subsets_mito_percent
## <numeric> <integer> <numeric>
## 1772071015_C02 774 23 3.462468
## 1772071017_G12 1121 27 4.901832
## 1772071017_A05 952 27 2.920783
## 1772071014_B06 611 28 1.822521
## 1772067065_H06 164 23 0.755969
## 1772071017_E02 1122 19 4.328871
## altexps_repeat_sum altexps_repeat_detected
## <numeric> <numeric>
## 1772071015_C02 8181 419
## 1772071017_G12 11854 480
## 1772071017_A05 18021 582
## 1772071014_B06 13955 512
## 1772067065_H06 6876 363
## 1772071017_E02 17364 618
## altexps_repeat_percent altexps_ERCC_sum altexps_ERCC_detected
## <numeric> <numeric> <numeric>
## 1772071015_C02 21.9677 6706 43
## 1772071017_G12 28.8012 6435 46
## 1772071017_A05 31.6435 6335 47
## 1772071014_B06 25.5999 7032 43
## 1772067065_H06 19.9299 5931 39
## 1772071017_E02 34.7600 6671 43
## altexps_ERCC_percent total
## <numeric> <numeric>
## 1772071015_C02 18.0070 37241
## 1772071017_G12 15.6349 41158
## 1772071017_A05 11.1238 56950
## 1772071014_B06 12.8999 54512
## 1772067065_H06 17.1908 34501
## 1772071017_E02 13.3543 49954Using the QC metrics generated via scater, we can use the plotMetrics function to visually inspect the quality of our dataset.
plotMetrics(sce)