Quality control
Katharina Hembach
5/26/2020
Last updated: 2020-06-08
Checks: 7 0
Knit directory: neural_scRNAseq/
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| Rmd | 24db792 | khembach | 2020-05-26 | Preprocessing and quality control plots |
Load packages
library(scater)
library(scales)
library(viridis)Load sce
sce <- readRDS(file.path("output", "sce_01_preprocessing.rds"))Quality control
We compute cell-level QC.
(mito <- grep("MT-", rownames(sce), value = TRUE)) [1] "ENSG00000210049.MT-TF" "ENSG00000211459.MT-RNR1"
[3] "ENSG00000210077.MT-TV" "ENSG00000210082.MT-RNR2"
[5] "ENSG00000209082.MT-TL1" "ENSG00000198888.MT-ND1"
[7] "ENSG00000210100.MT-TI" "ENSG00000210107.MT-TQ"
[9] "ENSG00000210112.MT-TM" "ENSG00000198763.MT-ND2"
[11] "ENSG00000210117.MT-TW" "ENSG00000210127.MT-TA"
[13] "ENSG00000210135.MT-TN" "ENSG00000210140.MT-TC"
[15] "ENSG00000210144.MT-TY" "ENSG00000198804.MT-CO1"
[17] "ENSG00000210151.MT-TS1" "ENSG00000210154.MT-TD"
[19] "ENSG00000198712.MT-CO2" "ENSG00000210156.MT-TK"
[21] "ENSG00000228253.MT-ATP8" "ENSG00000198899.MT-ATP6"
[23] "ENSG00000198938.MT-CO3" "ENSG00000210164.MT-TG"
[25] "ENSG00000198840.MT-ND3" "ENSG00000210174.MT-TR"
[27] "ENSG00000212907.MT-ND4L" "ENSG00000198886.MT-ND4"
[29] "ENSG00000210176.MT-TH" "ENSG00000210184.MT-TS2"
[31] "ENSG00000210191.MT-TL2" "ENSG00000198786.MT-ND5"
[33] "ENSG00000198695.MT-ND6" "ENSG00000210194.MT-TE"
[35] "ENSG00000198727.MT-CYB" "ENSG00000210195.MT-TT"
[37] "ENSG00000210196.MT-TP" sce <- addPerCellQC(sce, subsets = list(Mt = mito))
# we compute the fraction of mitochondrial genes and the logit of it
sce$subsets_Mt_fraction <- (sce$subsets_Mt_percent + 0.001) /100
sce$subsets_Mt_fraction_logit <- qlogis(sce$subsets_Mt_fraction + 0.001)
# library size
summary(sce$sum) Min. 1st Qu. Median Mean 3rd Qu. Max.
500 4071 5871 7816 9338 89589 # number of detected genes per cell
summary(sce$detected) Min. 1st Qu. Median Mean 3rd Qu. Max.
40 1956 2573 2834 3527 9218 # percentage of counts that come from mitochondrial genes:
summary(sce$subsets_Mt_percent) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.616 4.623 5.480 5.900 98.483 Diagnostic plots
The number of counts per cell:
plotColData(sce, x = "sample_id", y = "sum") + scale_y_log10()
The number of genes:
plotColData(sce, x = "sample_id", y = "detected") + scale_y_log10() 
The percentage of mitochondrial genes:
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent")
We plot the total number of counts against the number of detected genes and color by the fraction of mitochondrial genes:
cd <- data.frame(colData(sce))
ggplot(cd, aes(x = sum, y = detected, color = subsets_Mt_fraction)) +
geom_point(alpha = 0.7) +
geom_density_2d(color = "grey", bins = 6) +
scale_x_log10() +
scale_y_log10() +
facet_wrap(~sample_id) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab("sum of counts") +
ylab("number of detected genes") +
labs(color = "mitochondrial fraction") +
scale_color_viridis(trans = "logit", breaks = c(0.01, 0.1, 0.25, 0.5, 0.75))
We plot the total number of counts against the mitochondrial content. Well-behaved cells should have many expressed genes and a low fraction of mitochondrial genes. High mitochondrial content indicates empty or damaged cells.
ggplot(cd, aes(x = sum, y = subsets_Mt_fraction)) +
geom_point(color = "darkgrey", alpha = 0.3) +
geom_density_2d(color = "lightblue") +
scale_x_log10() +
scale_y_continuous(trans = 'logit',
breaks = c(0.01, 0.05, 0.1, 0.2, 0.5, 0.75)) +
facet_wrap(~sample_id) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab("sum of counts") +
ylab("logit(mitochondrial fraction)")
We plot the top 20 genes with highest expression. Mitochondrial genes, actin, ribosomal proteins or MALAT1 are examples of genes that are expected to have very high expression.
plotHighestExprs(sce, n = 20)Warning in sweep(sub_mat, 2, colSums2(exprs_mat), "/", check.margin = FALSE): 'check.margin' is ignored when 'x' is a DelayedArray object or
derivative
Save data to RDS
saveRDS(sce, file.path("output", "sce_02_quality_control.rds"))
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/lib/libRlapack.so
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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] HDF5Array_1.16.0 rhdf5_2.32.0
[3] viridis_0.5.1 viridisLite_0.3.0
[5] scales_1.1.1 scater_1.16.0
[7] ggplot2_3.3.0 SingleCellExperiment_1.10.1
[9] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[11] matrixStats_0.56.0 Biobase_2.48.0
[13] GenomicRanges_1.40.0 GenomeInfoDb_1.24.0
[15] IRanges_2.22.2 S4Vectors_0.26.1
[17] BiocGenerics_0.34.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] BiocSingular_1.4.0 DelayedMatrixStats_1.10.0
[3] assertthat_0.2.1 GenomeInfoDbData_1.2.3
[5] vipor_0.4.5 yaml_2.2.1
[7] pillar_1.4.4 backports_1.1.7
[9] lattice_0.20-41 glue_1.4.1
[11] beachmat_2.4.0 digest_0.6.25
[13] promises_1.1.0 XVector_0.28.0
[15] colorspace_1.4-1 cowplot_1.0.0
[17] htmltools_0.4.0 httpuv_1.5.2
[19] Matrix_1.2-18 pkgconfig_2.0.3
[21] zlibbioc_1.34.0 purrr_0.3.4
[23] whisker_0.4 later_1.0.0
[25] BiocParallel_1.22.0 git2r_0.27.1
[27] tibble_3.0.1 farver_2.0.3
[29] ellipsis_0.3.1 withr_2.2.0
[31] magrittr_1.5 crayon_1.3.4
[33] evaluate_0.14 fs_1.4.1
[35] MASS_7.3-51.6 beeswarm_0.2.3
[37] tools_4.0.0 lifecycle_0.2.0
[39] stringr_1.4.0 Rhdf5lib_1.10.0
[41] munsell_0.5.0 irlba_2.3.3
[43] isoband_0.2.1 compiler_4.0.0
[45] rsvd_1.0.3 rlang_0.4.6
[47] grid_4.0.0 RCurl_1.98-1.2
[49] BiocNeighbors_1.6.0 bitops_1.0-6
[51] labeling_0.3 rmarkdown_2.1
[53] gtable_0.3.0 codetools_0.2-16
[55] R6_2.4.1 gridExtra_2.3
[57] knitr_1.28 dplyr_0.8.5
[59] rprojroot_1.3-2 stringi_1.4.6
[61] ggbeeswarm_0.6.0 Rcpp_1.0.4.6
[63] vctrs_0.3.0 tidyselect_1.1.0
[65] xfun_0.14