SCTK Cell QC Report
sample47053
April 05, 2024
Parse_sn
1 Introduction
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.
2 Summary Statistics
2.1 SCTK-QC
| All Samples | |
|---|---|
| Number of Cells | 3499 |
| Mean counts | 11333 |
| Median counts | 6038 |
| Mean features detected | 2981.8 |
| Median features detected | 2407 |
| scDblFinder - Number of doublets | 290 |
| scDblFinder - Percentage of doublets | 8.29 |
| DecontX - Mean contamination | 0.097 |
| DecontX - Median contamination | 0.0662 |
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.
3 General quality control metrics
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.
3.1 Total Counts
3.2 Total Features
3.3 Percentage of Library Size Occupied by Top 50 Expressed Features
3.4 Total Mitochondrial Counts
3.5 Total Mitochondrial Features
3.6 Percentage of Mitochondrial Counts
3.7 Parameters
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.
4 Doublet Detection
4.1 Doublet Detection Summary
4.1.1 scDblFinder
4.2 ScDblFinder
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.
4.2.1 sample_47053
4.2.1.1 ScDblFinder Doublet Assignment
4.2.1.2 ScDblFinder Doublet Score
4.2.1.3 Density Score
4.2.1.4 Violin Score
4.2.1.5 Parameters
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.
5 Ambient RNA Detection
5.1 Ambient RNA Detection Summary
5.1.1 decontX
5.2 DecontX
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.
5.2.1 sample_47053
5.2.1.1 DecontX Contamination Score
5.2.1.2 DecontX Clusters
5.2.1.3 Density Score
5.2.1.4 Violin Score
5.2.1.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.18.1 |
6 Session Information
Session Information
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /common/software/install/migrated/openblas/0.3.5_gcc8.2.0_multiarch/lib/libopenblasp-r0.3.5.so; LAPACK version 3.8.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: US/Central
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] cowplot_1.1.3 dplyr_1.1.4
## [3] ggplot2_3.5.0 singleCellTK_2.12.2
## [5] DelayedArray_0.28.0 SparseArray_1.2.4
## [7] S4Arrays_1.2.1 abind_1.4-5
## [9] Matrix_1.6-5 SingleCellExperiment_1.24.0
## [11] SummarizedExperiment_1.32.0 Biobase_2.62.0
## [13] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8
## [15] IRanges_2.36.0 S4Vectors_0.40.2
## [17] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
## [19] matrixStats_1.2.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 gridExtra_2.3
## [3] rlang_1.1.3 magrittr_2.0.3
## [5] scater_1.30.1 compiler_4.3.0
## [7] DelayedMatrixStats_1.24.0 systemfonts_1.0.5
## [9] png_0.1-8 vctrs_0.6.5
## [11] reshape2_1.4.4 stringr_1.5.1
## [13] pkgconfig_2.0.3 crayon_1.5.2
## [15] fastmap_1.1.1 XVector_0.42.0
## [17] labeling_0.4.3 scuttle_1.12.0
## [19] utf8_1.2.4 rmarkdown_2.26
## [21] ggbeeswarm_0.7.2 xfun_0.43
## [23] zlibbioc_1.48.2 cachem_1.0.8
## [25] beachmat_2.18.1 jsonlite_1.8.8
## [27] highr_0.10 rhdf5filters_1.14.1
## [29] Rhdf5lib_1.24.2 BiocParallel_1.36.0
## [31] irlba_2.3.5.1 parallel_4.3.0
## [33] R6_2.5.1 bslib_0.7.0
## [35] stringi_1.8.3 limma_3.58.1
## [37] reticulate_1.35.0 jquerylib_0.1.4
## [39] Rcpp_1.0.12 knitr_1.45
## [41] R.utils_2.12.3 FNN_1.1.4
## [43] eds_1.4.0 tidyselect_1.2.1
## [45] rstudioapi_0.15.0 yaml_2.3.8
## [47] viridis_0.6.5 codetools_0.2-19
## [49] lattice_0.21-8 tibble_3.2.1
## [51] plyr_1.8.9 withr_3.0.0
## [53] evaluate_0.23 GSVAdata_1.38.0
## [55] xml2_1.3.5 pillar_1.9.0
## [57] generics_0.1.3 RCurl_1.98-1.14
## [59] sparseMatrixStats_1.14.0 munsell_0.5.1
## [61] scales_1.3.0 glue_1.7.0
## [63] tools_4.3.0 BiocNeighbors_1.20.2
## [65] ScaledMatrix_1.10.0 locfit_1.5-9.9
## [67] rhdf5_2.46.1 grid_4.3.0
## [69] DropletUtils_1.22.0 edgeR_4.0.16
## [71] colorspace_2.1-0 GenomeInfoDbData_1.2.11
## [73] beeswarm_0.4.0 BiocSingular_1.18.0
## [75] HDF5Array_1.30.1 vipor_0.4.7
## [77] cli_3.6.2 rsvd_1.0.5
## [79] kableExtra_1.4.0 fansi_1.0.6
## [81] viridisLite_0.4.2 svglite_2.1.3
## [83] uwot_0.1.16 gtable_0.3.4
## [85] R.methodsS3_1.8.2 sass_0.4.9
## [87] digest_0.6.35 ggrepel_0.9.5
## [89] dqrng_0.3.2 farver_2.1.1
## [91] htmltools_0.5.8.1 R.oo_1.26.0
## [93] lifecycle_1.0.4 statmod_1.5.0