Filtering
Katharina Hembach
June 08, 2020
Last updated: 2020-06-08
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 6d822af | khembach | 2020-05-28 | Build site. |
| Rmd | a7ced59 | khembach | 2020-05-28 | cell and gene filtering |
Load packages
library(scater)
library(LSD)Load data
sce <- readRDS(file.path("output", "sce_02_quality_control.rds"))Identification of outlier cells
Based on the QC metrics, we now identify outlier cells:
cols <- c("sum", "detected", "subsets_Mt_percent")
log <- c(TRUE, TRUE, FALSE)
type <- c("both", "both", "higher")
drop_cols <- paste0(cols, "_drop")
for (i in seq_along(cols))
colData(sce)[[drop_cols[i]]] <- isOutlier(sce[[cols[i]]],
nmads = 3, type = type[i], log = log[i], batch = sce$sample_id)
# Overlap of outlier cells from two metrics
sapply(drop_cols, function(i)
sapply(drop_cols, function(j)
sum(sce[[i]] & sce[[j]]))) sum_drop detected_drop subsets_Mt_percent_drop
sum_drop 2499 2289 614
detected_drop 2289 2740 809
subsets_Mt_percent_drop 614 809 2611colData(sce)$discard <- rowSums(data.frame(colData(sce)[,drop_cols])) > 0
table(colData(sce)$discard)
FALSE TRUE
43846 4735 ## Plot the metrics and highlight the discarded cells
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| 6d822af | khembach | 2020-05-28 |
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| 6d822af | khembach | 2020-05-28 |
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
| Version | Author | Date |
|---|---|---|
| 6d822af | khembach | 2020-05-28 |
Plot the library size against the number of detected genes before and after filtering.
cd <- colData(sce)
layout(matrix(1:12, nrow = 3, byrow = TRUE))
for (i in levels(sce$sample_id)) {
tmp <- cd[cd$sample_id == i,]
heatscatter(tmp$sum, tmp$detected, log = "xy",
main = paste0(i, "-unfiltered"), xlab = "total counts",
ylab = "detected genes")
heatscatter(tmp$sum[!tmp$discard], tmp$detected[!tmp$discard],
log = "xy", main = paste0(i, "-filtered"), xlab = "total counts",
ylab = "detected genes")
}
| Version | Author | Date |
|---|---|---|
| 6d822af | khembach | 2020-05-28 |
We remove the outlier cells and filter the genes:
## summary of the kept cells
nr <- table(cd$sample_id)
nr_fil <- table(cd$sample_id[!cd$discard])
print(rbind(
unfiltered = nr, filtered = nr_fil,
"%" = round(nr_fil / nr * 100, digits = 0))) 1NSC 2NSC 3NC52 4NC52 5NC96 6NC96
unfiltered 8893 8854 9109 8865 6571 6289
filtered 8331 8408 8687 7438 6189 4793
% 94 95 95 84 94 76## discard the outlier cells
dim(sce)[1] 19375 48581sce <- sce[,!cd$discard]
dim(sce)[1] 19375 43846## we filter genes and require > 1 count in at least 20 cells
sce <- sce[rowSums(counts(sce) > 1) >= 20, ]
dim(sce)[1] 13264 43846Save data to RDS
saveRDS(sce, file.path("output", "sce_03_filtering.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] LSD_4.0-0 scater_1.16.0
[5] ggplot2_3.3.0 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 BiocSingular_1.4.0
[3] viridisLite_0.3.0 DelayedMatrixStats_1.10.0
[5] assertthat_0.2.1 GenomeInfoDbData_1.2.3
[7] vipor_0.4.5 yaml_2.2.1
[9] pillar_1.4.4 backports_1.1.7
[11] lattice_0.20-41 glue_1.4.1
[13] digest_0.6.25 promises_1.1.0
[15] XVector_0.28.0 colorspace_1.4-1
[17] cowplot_1.0.0 htmltools_0.4.0
[19] httpuv_1.5.2 Matrix_1.2-18
[21] pkgconfig_2.0.3 zlibbioc_1.34.0
[23] purrr_0.3.4 scales_1.1.1
[25] whisker_0.4 later_1.0.0
[27] BiocParallel_1.22.0 git2r_0.27.1
[29] tibble_3.0.1 farver_2.0.3
[31] ellipsis_0.3.1 withr_2.2.0
[33] magrittr_1.5 crayon_1.3.4
[35] evaluate_0.14 fs_1.4.1
[37] beeswarm_0.2.3 tools_4.0.0
[39] lifecycle_0.2.0 stringr_1.4.0
[41] Rhdf5lib_1.10.0 munsell_0.5.0
[43] irlba_2.3.3 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