Preprocessing and QC
Katharina Hembach
5/25/2020
Last updated: 2020-06-17
Checks: 7 0
Knit directory: neural_scRNAseq/
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| Rmd | 7379a9b | khembach | 2020-06-17 | add histo and PCA for scDblFinder results |
| html | 7e96c71 | khembach | 2020-06-17 | Build site. |
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Load packages
library(DropletUtils)
library(scDblFinder)
library(BiocParallel)
library(ggplot2)
library(scater)Importing CellRanger output and metadata
fs <- dir(path = "data/filtered_feature_matrices",
pattern = "^[1-6]N*", recursive = FALSE, full.names = TRUE)
names(fs) <- basename(fs)
## we want to analyse the count matrix
fs <- sapply(fs, function(x) file.path(x, "filtered_feature_bc_matrix.h5"))
sce <- read10xCounts(samples = fs)
# rename colnames and dimnames
rowData(sce)$Type <- NULL
names(rowData(sce)) <- c("ensembl_id", "symbol")
names(colData(sce)) <- c("sample_id", "barcode")
sce$sample_id <- factor(sce$sample_id)
dimnames(sce) <- list(with(rowData(sce), paste(ensembl_id, symbol, sep = ".")),
with(colData(sce), paste(barcode, sample_id, sep = ".")))
# load metadata
meta <- read.csv(file.path("data", "metadata.csv"))
m <- match(sce$sample_id, meta$sample)
sce$group_id <- meta$group[m]Remove undetected genes and doublets
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
dim(sce)[1] 19375 52830# doublet detection with 'scDblFinder'
# the expected proportion of doublets is 1% per 1000 cells
sce <- scDblFinder(sce, samples="sample_id", BPPARAM=MulticoreParam(6))
table(colData(sce)[,c("scDblFinder.class", "sample_id")]) sample_id
scDblFinder.class 1NSC 2NSC 3NC52 4NC52 5NC96 6NC96
doublet 838 813 904 826 422 404
singlet 8908 8860 9096 8889 6578 6292# histogram of the doublet scores
dat <- as.data.frame(colData(sce)[c("scDblFinder.score",
"scDblFinder.class", "sample_id")])
dat$scDblFinder.class <- factor(dat$scDblFinder.class,
levels = c("singlet", "doublet"))
p <- ggplot(dat, aes(scDblFinder.score)) +
geom_histogram(bins = 100) +
facet_grid(vars(sample_id), vars(scDblFinder.class)) +
scale_y_log10()
print(p)Warning: Transformation introduced infinite values in continuous y-axisWarning: Removed 625 rows containing missing values (geom_bar).
| Version | Author | Date |
|---|---|---|
| 7e96c71 | khembach | 2020-06-17 |
## PCA plot colored by doublet score
for (i in levels(sce$sample_id)) {
print(i)
subs <- sce[,sce$sample_id == i]
subs <- logNormCounts(subs)
subs <- runPCA(subs)
print(plotPCA(subs, colour_by = "scDblFinder.score"))
print(plotPCA(subs, colour_by = "scDblFinder.class"))
}[1] "1NSC"
[1] "2NSC"
[1] "3NC52"
[1] "4NC52"
[1] "5NC96"
[1] "6NC96"
# we remove the cells that were classified as doublets
sce <- sce[,sce$scDblFinder.class == "singlet"]Save data to RDS
saveRDS(sce, file.path("output", "sce_01_preprocessing.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] scater_1.16.0 ggplot2_3.3.0
[3] BiocParallel_1.22.0 scDblFinder_1.1.15
[5] DropletUtils_1.8.0 SingleCellExperiment_1.10.1
[7] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[9] matrixStats_0.56.0 Biobase_2.48.0
[11] GenomicRanges_1.40.0 GenomeInfoDb_1.24.0
[13] IRanges_2.22.2 S4Vectors_0.26.1
[15] BiocGenerics_0.34.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] viridis_0.5.1 edgeR_3.30.0
[3] BiocSingular_1.4.0 viridisLite_0.3.0
[5] DelayedMatrixStats_1.10.0 R.utils_2.9.2
[7] assertthat_0.2.1 statmod_1.4.34
[9] dqrng_0.2.1 vipor_0.4.5
[11] GenomeInfoDbData_1.2.3 yaml_2.2.1
[13] pillar_1.4.4 backports_1.1.7
[15] lattice_0.20-41 glue_1.4.1
[17] limma_3.44.1 digest_0.6.25
[19] promises_1.1.0 XVector_0.28.0
[21] randomForest_4.6-14 colorspace_1.4-1
[23] cowplot_1.0.0 htmltools_0.4.0
[25] httpuv_1.5.2 Matrix_1.2-18
[27] R.oo_1.23.0 pkgconfig_2.0.3
[29] zlibbioc_1.34.0 purrr_0.3.4
[31] scales_1.1.1 HDF5Array_1.16.0
[33] whisker_0.4 later_1.0.0
[35] git2r_0.27.1 tibble_3.0.1
[37] farver_2.0.3 ellipsis_0.3.1
[39] withr_2.2.0 magrittr_1.5
[41] crayon_1.3.4 evaluate_0.14
[43] R.methodsS3_1.8.0 fs_1.4.1
[45] beeswarm_0.2.3 tools_4.0.0
[47] data.table_1.12.8 lifecycle_0.2.0
[49] stringr_1.4.0 Rhdf5lib_1.10.0
[51] munsell_0.5.0 locfit_1.5-9.4
[53] irlba_2.3.3 compiler_4.0.0
[55] rsvd_1.0.3 rlang_0.4.6
[57] rhdf5_2.32.0 grid_4.0.0
[59] RCurl_1.98-1.2 BiocNeighbors_1.6.0
[61] igraph_1.2.5 labeling_0.3
[63] bitops_1.0-6 rmarkdown_2.1
[65] codetools_0.2-16 gtable_0.3.0
[67] R6_2.4.1 gridExtra_2.3
[69] knitr_1.28 dplyr_0.8.5
[71] rprojroot_1.3-2 stringi_1.4.6
[73] ggbeeswarm_0.6.0 Rcpp_1.0.4.6
[75] scran_1.16.0 vctrs_0.3.0
[77] tidyselect_1.1.0 xfun_0.14