An introduction to miQC
Compiled: May 20, 2021
Contents
1 Installation
To install the package, please use the following.
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("miQC")2 Introduction
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.
You’ll need the following packages installed to work this tutorial:
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(scRNAseq)
library(scater)
library(flexmix)
library(splines)
library(BiocParallel)
library(biomaRt)
library(miQC)
})2.1 Example data
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(10): tissue group # ... level1class level2class
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(2): ERCC repeat2.2 Scater preprocessing
In 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,
BPPARAM = MulticoreParam())
head(colData(sce))## DataFrame with 6 rows and 22 columns
## tissue group # total mRNA mol well sex
## <character> <numeric> <numeric> <numeric> <numeric>
## 1772071015_C02 sscortex 1 1221 3 3
## 1772071017_G12 sscortex 1 1231 95 1
## 1772071017_A05 sscortex 1 1652 27 1
## 1772071014_B06 sscortex 1 1696 37 3
## 1772067065_H06 sscortex 1 1219 43 3
## 1772071017_E02 sscortex 1 1378 5 1
## age diameter cell_id level1class level2class
## <numeric> <numeric> <character> <character> <character>
## 1772071015_C02 2 1 1772071015_C02 interneurons Int10
## 1772071017_G12 1 353 1772071017_G12 interneurons Int10
## 1772071017_A05 1 13 1772071017_A05 interneurons Int6
## 1772071014_B06 2 19 1772071014_B06 interneurons Int10
## 1772067065_H06 6 12 1772067065_H06 interneurons Int9
## 1772071017_E02 1 21 1772071017_E02 interneurons Int9
## sum detected subsets_mito_sum subsets_mito_detected
## <numeric> <numeric> <numeric> <numeric>
## 1772071015_C02 22354 4871 774 23
## 1772071017_G12 22869 4712 1121 27
## 1772071017_A05 32594 6055 952 27
## 1772071014_B06 33525 5852 611 28
## 1772067065_H06 21694 4724 164 23
## 1772071017_E02 25919 5427 1122 19
## subsets_mito_percent altexps_ERCC_sum altexps_ERCC_detected
## <numeric> <numeric> <numeric>
## 1772071015_C02 3.462468 6706 43
## 1772071017_G12 4.901832 6435 46
## 1772071017_A05 2.920783 6335 47
## 1772071014_B06 1.822521 7032 43
## 1772067065_H06 0.755969 5931 39
## 1772071017_E02 4.328871 6671 43
## altexps_ERCC_percent altexps_repeat_sum altexps_repeat_detected
## <numeric> <numeric> <numeric>
## 1772071015_C02 18.0070 8181 419
## 1772071017_G12 15.6349 11854 480
## 1772071017_A05 11.1238 18021 582
## 1772071014_B06 12.8999 13955 512
## 1772067065_H06 17.1908 6876 363
## 1772071017_E02 13.3543 17364 618
## altexps_repeat_percent total
## <numeric> <numeric>
## 1772071015_C02 21.9677 37241
## 1772071017_G12 28.8012 41158
## 1772071017_A05 31.6435 56950
## 1772071014_B06 25.5999 54512
## 1772067065_H06 19.9299 34501
## 1772071017_E02 34.7600 499543 miQC
Using the QC metrics generated via scater, we can use the plotMetrics function to visually inspect the quality of our dataset.
plotMetrics(sce)
We can see that most cells have a fairly low proportion of mitochondrial reads, given that the graph is much denser at the bottom. We likely have many cells that are intact and biologically meaningful. There are also a few cells that have almost half of their reads mapping to mitochondrial genes, which are likely broken or otherwise compromised and we will want to exclude from our downstream analysis. However, it’s not clear what boundaries to draw to separate the two groups of cells. With that in mind, we’ll generate a linear mixture model using the mixtureModel function.
model <- mixtureModel(sce)This function is a wrapper for flexmix, which fits a mixture model on our data and returns the parameters of the two lines that best fit the data, as well as the posterior probability of each cell being derived from each distribution.
We can look at the parameters and posterior values directly with the functions
parameters(model)## Comp.1 Comp.2
## coef.(Intercept) 26.894723233 9.5037727791
## coef.detected -0.003755651 -0.0008344166
## sigma 6.489703279 3.3056046875head(posterior(model))## [,1] [,2]
## [1,] 0.10549462 0.8945054
## [2,] 0.09961169 0.9003883
## [3,] 0.12855102 0.8714490
## [4,] 0.14699953 0.8530005
## [5,] 0.14391468 0.8560853
## [6,] 0.11501939 0.8849806Or we can visualize the model results using the plotModel function:
plotModel(sce, model)
As expected, the cells at the very top of the graph are almost certainly compromised, most likely to have been derived from the distribution with fewer unique genes and higher baseline mitochondrial expression.
We can use these posterior probabilities to choose which cells to keep, and visualize the consequences of this filtering with the plotFiltering function.
plotFiltering(sce, model)
To actually perform the filtering and remove the indicated cells from our SingleCellExperiment object, we can run the filterCells parameter.
sce <- filterCells(sce, model)## Removing 282 out of 3005 cells.sce## class: SingleCellExperiment
## dim: 20006 2723
## metadata(0):
## assays(1): counts
## rownames(20006): Tspan12 Tshz1 ... mt-Rnr1 mt-Nd4l
## rowData names(1): featureType
## colnames(2723): 1772071015_C02 1772071017_G12 ... 1772066097_D04
## 1772066098_A12
## colData names(23): tissue group # ... total prob_compromised
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(2): ERCC repeat3.1 Extras
In most cases, a linear mixture model will be satisfactory as well as simplest, but miQC also supports some non-linear mixture models: currently polynomials and b-splines. A user should only need to change the model_type parameter when making the model, and all visualization and filtering functions will work the same as with a linear model.
sce <- ZeiselBrainData()
sce <- addPerCellQC(sce, subsets = feature_ctrls,
BPPARAM = MulticoreParam())
model2 <- mixtureModel(sce, model_type = "spline")
plotModel(sce, model2)
plotFiltering(sce, model2)
Also, miQC defaults to removing any cell with 75% or greater posterior probability of being compromised, but if we want to be more or less stringent, we can alter the posterior_cutoff parameter, like so:
plotFiltering(sce, model2, posterior_cutoff = 0.9)
Note that when performing miQC multiple times on different samples for the same experiment, it’s recommended to select the same posterior_cutoff for all, to give consistency in addition to the flexibility of sample-specific models.
3.2 When not to use miQC
The miQC model is based on the assumption that there are a non-trivial number of compromised cells in the dataset, which is not true in all datasets. We recommend running plotMetrics on a dataset before running miQC to see if the two-distribution model is appropriate. Look for the distinctive triangular shape where cells have a wide variety of mitochondrial percentages at lower gene counts and taper off to lower mitochondrial percentage at higher gene counts, as can be seen in the Zeisel data.
For example of a dataset where there’s not a significant number of compromised cells, so the two-distribution assumption is not met, look at another dataset from the scRNAseq package, mouse data from Lun et al (Lun et al. 2017). Note that this dataset uses ensembl IDs, so we will use biomaRt to determine which genes are mitochondrial and calculate mitochondrial percentage.
sce <- LunSpikeInData()
mouse_mart <- useEnsembl("ENSEMBL_MART_ENSEMBL", "mmusculus_gene_ensembl")
id_map <- getBM(values = rownames(sce),
filters = "ensembl_gene_id",
attributes = c("ensembl_gene_id", "chromosome_name"),
mart = mouse_mart)
mt_genes <- subset(id_map,id_map$chromosome_name=="MT")$ensembl_gene_id
feature_ctrls <- list(mito = mt_genes)
sce <- addPerCellQC(sce, subsets = feature_ctrls,
BPPARAM = BiocParallel::MulticoreParam())
plotMetrics(sce)
The mixtureModel function will throw a warning if only one distribution is found, in which case no cells would be filtered. In these cases, we recommend using other filtering methods, such as a cutoff on mitochondrial percentage or identifying outliers using median absolute deviation (MAD).
4 Session Information
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] splines parallel stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ensembldb_2.16.0 AnnotationFilter_1.16.0 GenomicFeatures_1.44.0 AnnotationDbi_1.54.0
## [5] miQC_1.0.0 biomaRt_2.48.0 BiocParallel_1.26.0 flexmix_2.3-17
## [9] lattice_0.20-44 scater_1.20.0 ggplot2_3.3.3 scuttle_1.2.0
## [13] scRNAseq_2.5.10 SingleCellExperiment_1.14.0 SummarizedExperiment_1.22.0 Biobase_2.52.0
## [17] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0 IRanges_2.26.0 S4Vectors_0.30.0
## [21] BiocGenerics_0.38.0 MatrixGenerics_1.4.0 matrixStats_0.58.0 BiocStyle_2.20.0
##
## loaded via a namespace (and not attached):
## [1] ggbeeswarm_0.6.0 colorspace_2.0-1 rjson_0.2.20
## [4] modeltools_0.2-23 ellipsis_0.3.2 XVector_0.32.0
## [7] BiocNeighbors_1.10.0 farver_2.1.0 bit64_4.0.5
## [10] interactiveDisplayBase_1.30.0 fansi_0.4.2 xml2_1.3.2
## [13] sparseMatrixStats_1.4.0 cachem_1.0.5 knitr_1.33
## [16] jsonlite_1.7.2 Rsamtools_2.8.0 dbplyr_2.1.1
## [19] png_0.1-7 shiny_1.6.0 BiocManager_1.30.15
## [22] compiler_4.1.0 httr_1.4.2 assertthat_0.2.1
## [25] Matrix_1.3-3 fastmap_1.1.0 lazyeval_0.2.2
## [28] BiocSingular_1.8.0 later_1.2.0 htmltools_0.5.1.1
## [31] prettyunits_1.1.1 tools_4.1.0 rsvd_1.0.5
## [34] gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.6
## [37] dplyr_1.0.6 rappdirs_0.3.3 Rcpp_1.0.6
## [40] jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.60.0
## [43] ExperimentHub_2.0.0 rtracklayer_1.52.0 DelayedMatrixStats_1.14.0
## [46] xfun_0.23 stringr_1.4.0 beachmat_2.8.0
## [49] irlba_2.3.3 mime_0.10 lifecycle_1.0.0
## [52] restfulr_0.0.13 XML_3.99-0.6 AnnotationHub_3.0.0
## [55] zlibbioc_1.38.0 scales_1.1.1 hms_1.1.0
## [58] promises_1.2.0.1 ProtGenerics_1.24.0 yaml_2.2.1
## [61] curl_4.3.1 gridExtra_2.3 memoise_2.0.0
## [64] sass_0.4.0 stringi_1.6.2 RSQLite_2.2.7
## [67] highr_0.9 BiocVersion_3.13.1 BiocIO_1.2.0
## [70] ScaledMatrix_1.0.0 filelock_1.0.2 rlang_0.4.11
## [73] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
## [76] purrr_0.3.4 labeling_0.4.2 GenomicAlignments_1.28.0
## [79] bit_4.0.4 tidyselect_1.1.1 magrittr_2.0.1
## [82] bookdown_0.22 R6_2.5.0 magick_2.7.2
## [85] generics_0.1.0 DelayedArray_0.18.0 DBI_1.1.1
## [88] pillar_1.6.1 withr_2.4.2 nnet_7.3-16
## [91] KEGGREST_1.32.0 RCurl_1.98-1.3 tibble_3.1.2
## [94] crayon_1.4.1 utf8_1.2.1 BiocFileCache_2.0.0
## [97] rmarkdown_2.8 viridis_0.6.1 progress_1.2.2
## [100] grid_4.1.0 blob_1.2.1 digest_0.6.27
## [103] xtable_1.8-4 httpuv_1.6.1 munsell_0.5.0
## [106] viridisLite_0.4.0 beeswarm_0.3.1 vipor_0.4.5
## [109] bslib_0.2.5.1References
Lun, Aaron T. L., Fernando J. Calero-Nieto, Liora Haim-Vilmovsky, Berthold Göttgens, and John C. Marioni. 2017. “Assessing the Reliability of Spike-in Normalization for Analyses of Single-Cell RNA Sequencing Data.” Genome Research 27 (11): 1795–1806. https://doi.org/10.1101/gr.222877.117.
Zeisel, Amit, Ana B. Muñoz-Manchado, Simone Codeluppi, Peter Lönnerberg, Gioele La Manno, Anna Juréus, Sueli Marques, et al. 2015. “Brain Structure. Cell Types in the Mouse Cortex and Hippocampus Revealed by Single-Cell RNA-Seq.” Science (New York, N.Y.) 347 (6226). https://doi.org/10.1126/science.aaa1934.