Comprehensive quality control (QC) of single-cell RNA-seq data was performed with the singleCellTK package. This report contains information about each QC tool and visualization of the QC metrics for each sample. For more information on running this pipeline and performing quality control, see the documentation. If you use the singleCellTK package for quality control, please include a reference in your publication.
Total | |
---|---|
Number of Cells | 2700 |
Mean counts | 2366.9 |
Median counts | 2197 |
Mean features detected | 846.99 |
Median features detected | 817 |
scDblFinder - Number of doublets | 87 |
scDblFinder - Percentage of doublets | 3.22 |
DecontX - Mean contamination | 0.0808 |
DecontX - Median contamination | 0.055 |
The summary statistics table summarizes QC metrics of the cell matrix. This table summarizes the mean and median of UMI counts and median of genes detected per cell, as well as the number and percentages of doublets and estimated ambient RNA scores per dataset.
SingleCellTK utilizes the scater package to compute cell-level QC metrics. The wrapper function runPerCellQC
can be used to separately compute QC metrics on its own. The wrapper function plotRunPerCellQCResults
can be used to plot the general QC outputs. The QC outputs are sum
, detected
, and percent_top_X
. sum
contains the total number of counts for each cell. detected
contains the total number of features for each cell. percent_top_X
contains the percentage of the total counts that is made up by the expression of the top X genes for each cell. The subsets_
columns contain information for the specific gene list that was used. For instance, if a gene list containing mitochondrial genes named mito
was used, subsets_mito_sum
would contains the total number of mitochondrial counts for each cell.
useAssay | counts |
collectionName | mito |
geneSetList | NULL |
geneSetListLocation | rownames |
percent_top | 50 100 200 500 |
use_altexps | FALSE |
flatten | TRUE |
detectionLimit | 0 |
packageVersion | 1.20.1 |
In this function, the inSCE
parameter is the input SingleCellExperiment object, while the useAssay
parameter is the assay object that in the SingleCellExperiment object the user wishes to use.
scDblFinder is a doublet detection algorithm in the scran
package. scDblFinder aims to detect doublets by creating a simulated doublet from existing cells and projecting it to the same PCA space as the cells. The wrapper function runScDblFinder
can be used to separately run the scDblFinder algorithm on its own. The wrapper function plotScDblFinderResults
can be used to plot the QC outputs from the scDblFinder algorithm. The output of scDblFinder is a scDblFinder_doublet_score
and scDblFinder_doublet_call
. The doublet score of a droplet will be higher if the it is deemed likely to be a doublet.
useAssay | counts |
nNeighbors | 50 |
simDoublets | 10000 |
seed | 12345 |
packageVersion | 1.6.0 |
The nNeighbors
parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets
is used to determine the number of simulated doublets used for doublet detection.
In droplet-based single cell technologies, ambient RNA that may have been released from apoptotic or damaged cells may get incorporated into another droplet, and can lead to contamination. decontX, available from the celda, is a Bayesian method for the identification of the contamination level at a cellular level. The wrapper function runDecontX
can be used to separately run the DecontX algorithm on its own. The wrapper function plotDecontXResults
can be used to plot the QC outputs from the DecontX algorithm. The outputs of runDecontX
are decontX_contamination
and decontX_clusters
. decontX_contamination
is a numeric vector which characterizes the level of contamination in each cell. Clustering is performed as part of the runDecontX
algorithm. decontX_clusters
is the resulting cluster assignment, which can also be labeled on the plot.
useAssay | counts |
z | NULL |
maxIter | 500 |
delta | 10 10 |
estimateDelta | TRUE |
convergence | 0.001 |
iterLogLik | 10 |
varGenes | 5000 |
dbscanEps | 1 |
seed | 12345 |
logfile | NULL |
verbose | TRUE |
packageVersion | 1.8.1 |
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] dplyr_1.0.7 ggplot2_3.3.5 TENxPBMCData_1.10.0 HDF5Array_1.20.0
## [5] rhdf5_2.36.0 singleCellTK_2.4.0 DelayedArray_0.18.0 Matrix_1.4-0
## [9] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0 Biobase_2.52.0 GenomicRanges_1.44.0
## [13] GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0 BiocGenerics_0.38.0
## [17] MatrixGenerics_1.4.0 matrixStats_0.59.0
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