Quality control
Katharina Hembach
5/26/2020
Last updated: 2020-05-26
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$sum) Min. 1st Qu. Median Mean 3rd Qu. Max.
500 2377 4624 6152 7688 78841 # number of detected genes per cell
summary(sce$detected) Min. 1st Qu. Median Mean 3rd Qu. Max.
32 1393 2147 2362 3100 8593 # percentage of counts that come from mitochondrial genes:
summary(sce$subsets_Mt_percent) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.694 4.850 5.914 6.584 98.483 Diagnostic plots
The number of counts per cell:
plotColData(sce, x = "sample_id", y = "sum")
The number of genes:
plotColData(sce, x = "sample_id", y = "detected")
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))
| Version | Author | Date |
|---|---|---|
| d56ccca | khembach | 2020-05-26 |
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)")
| Version | Author | Date |
|---|---|---|
| d56ccca | khembach | 2020-05-26 |
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)
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 htmltools_0.4.0
[17] httpuv_1.5.2 Matrix_1.2-18
[19] pkgconfig_2.0.3 zlibbioc_1.34.0
[21] purrr_0.3.4 whisker_0.4
[23] later_1.0.0 BiocParallel_1.22.0
[25] git2r_0.27.1 tibble_3.0.1
[27] farver_2.0.3 ellipsis_0.3.1
[29] withr_2.2.0 magrittr_1.5
[31] crayon_1.3.4 evaluate_0.14
[33] fs_1.4.1 MASS_7.3-51.6
[35] beeswarm_0.2.3 tools_4.0.0
[37] lifecycle_0.2.0 stringr_1.4.0
[39] Rhdf5lib_1.10.0 munsell_0.5.0
[41] irlba_2.3.3 isoband_0.2.1
[43] compiler_4.0.0 rsvd_1.0.3
[45] rlang_0.4.6 grid_4.0.0
[47] RCurl_1.98-1.2 BiocNeighbors_1.6.0
[49] bitops_1.0-6 labeling_0.3
[51] rmarkdown_2.1 gtable_0.3.0
[53] codetools_0.2-16 R6_2.4.1
[55] gridExtra_2.3 knitr_1.28
[57] dplyr_0.8.5 rprojroot_1.3-2
[59] stringi_1.4.6 ggbeeswarm_0.6.0
[61] Rcpp_1.0.4.6 vctrs_0.3.0
[63] tidyselect_1.1.0 xfun_0.14