Quality filtering
Katharina Hembach
10/7/2020
Last updated: 2021-04-12
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2ba83d0 | khembach | 2021-04-12 | print number of cells, UMIs and detected genes per cell and sample |
| html | bcec025 | khembach | 2020-10-09 | Build site. |
| Rmd | a653577 | khembach | 2020-10-09 | manual cutoffs for cell filtering |
| html | 5a50966 | khembach | 2020-10-07 | Build site. |
| Rmd | e5acfd9 | khembach | 2020-10-07 | Cell filtering of TDP experiment |
Load packages
library(scater)
library(LSD)
library(dplyr)
library(edgeR)
library(ggrepel)Load data
sce <- readRDS(file.path("output", "sce_TDP_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$sample_id)
# Overlap of outlier cells from two metrics
sapply(drop_cols, function(i)
sapply(drop_cols, function(j)
sum(sce[[i]] & sce[[j]]))) sum_drop detected_drop subsets_Mt_percent_drop
sum_drop 3644 3644 221
detected_drop 3644 7701 686
subsets_Mt_percent_drop 221 686 4229colData(sce)$discard <- rowSums(data.frame(colData(sce)[,drop_cols])) > 0
table(colData(sce)$discard)
FALSE TRUE
61769 11244 ## Plot the metrics and highlight the discarded cells
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
We decided to additionally filter the cells in the TDP experiment samples. We use the same cutoffs as for the 96 days old neural cultures from the first experiment. We also remove the cell population with low number of UMIs and detected genes from the old neural cultures (223 days).
## filter the cells with less than 5000 UMIs in the TDP experiment samples
tdp_samples <- c("TDP2wON", "TDP4wOFF", "TDP4wONa", "TDP4wONb")
colData(sce)$manual_discard_sum <- colData(sce)$sum < 5000 &
colData(sce)$sample_id %in% tdp_samples
## filter the cells with less than 2500 detected genes
colData(sce)$manual_discard_detected <- colData(sce)$detected < 2500 &
colData(sce)$sample_id %in% tdp_samples
## day 223
colData(sce)$manual_discard_sum <- colData(sce)$manual_discard_sum |
colData(sce)$sum < 2000 &
colData(sce)$sample_id %in% c("NC223a", "NC223b")
colData(sce)$manual_discard_detected <- colData(sce)$manual_discard_detected |
colData(sce)$detected < 1500 &
colData(sce)$sample_id %in% c("NC223a", "NC223b")
## highlight all manually discarded cells
colData(sce)$manual_discard <- colData(sce)$manual_discard_sum |
colData(sce)$manual_discard_detected
plotColData(sce, x = "sample_id", y = "sum", colour_by = "manual_discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
plotColData(sce, x = "sample_id", y = "detected", colour_by = "manual_discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
## highlight all discarded cells
colData(sce)$discard <- colData(sce)$manual_discard |
colData(sce)$discard
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
| Version | Author | Date |
|---|---|---|
| bcec025 | khembach | 2020-10-09 |
Plot the library size against the number of detected genes before and after filtering.
cd <- colData(sce)
layout(matrix(1:12, nrow = 3, byrow = TRUE))
for (i in levels(sce$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$discard],
log = "xy", main = paste0(i, "-filtered"), xlab = "total counts",
ylab = "detected genes")
}
Removal of outlier cells
We remove the outlier cells and filter the genes:
## summary of the kept cells
nr <- table(cd$sample_id)
nr_fil <- table(cd$sample_id[!cd$discard])
print(rbind(
unfiltered = nr, filtered = nr_fil,
"%" = round(nr_fil / nr * 100, digits = 0))) NC223a NC223b TDP2wON TDP4wOFF TDP4wONa TDP4wONb
unfiltered 12647 14221 11030 8758 14112 12245
filtered 5350 7363 7406 6077 9665 7722
% 42 52 67 69 68 63## discard the outlier cells
dim(sce)[1] 19741 73013sce <- sce[,!cd$discard]
dim(sce)[1] 19741 43583## we filter genes and require > 1 count in at least 20 cells
sce_filtered <- sce[rowSums(counts(sce) > 1) >= 20, ]
dim(sce_filtered)[1] 13968 43583## number of cells per sample
sce_filtered$sample_id %>% table.
NC223a NC223b TDP2wON TDP4wOFF TDP4wONa TDP4wONb
5350 7363 7406 6077 9665 7722 ## number of UMIs per cells and sample
colData(sce_filtered) %>% as.data.frame %>%
dplyr::group_by(sample_id) %>%
summarize(min = min(sum), median = median(sum),
mean = mean(sum), max = max(sum))# A tibble: 6 x 5
sample_id min median mean max
<fct> <int> <dbl> <dbl> <int>
1 NC223a 2016 13740. 20695. 118668
2 NC223b 2036 9170 14777. 103185
3 TDP2wON 5179 19128. 21543. 112462
4 TDP4wOFF 5070 17780 19964. 83062
5 TDP4wONa 5066 15054 16943. 65985
6 TDP4wONb 5080 20052. 22381. 98147# number of detected genes per cell and sample
colData(sce_filtered) %>% as.data.frame %>%
dplyr::group_by(sample_id) %>%
summarize(min = min(detected), median = median(detected),
mean = mean(detected), max = max(detected))# A tibble: 6 x 5
sample_id min median mean max
<fct> <int> <dbl> <dbl> <int>
1 NC223a 1500 4337 4632. 9786
2 NC223b 1500 3429 3992. 9155
3 TDP2wON 2503 4881 5002. 9785
4 TDP4wOFF 2501 4770 4864. 8963
5 TDP4wONa 2500 4301 4438. 8572
6 TDP4wONb 2507 5108 5190. 9421Save data to RDS
saveRDS(sce_filtered, file.path("output", "sce_TDP_03_filtering.rds"))
saveRDS(sce, file.path("output", "sce_TDP_03_filtering_all_genes.rds"))
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 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_1.16.1 rhdf5_2.32.2
[3] ggrepel_0.8.2 edgeR_3.30.3
[5] limma_3.44.3 dplyr_1.0.2
[7] LSD_4.1-0 scater_1.16.2
[9] ggplot2_3.3.2 SingleCellExperiment_1.10.1
[11] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[13] matrixStats_0.56.0 Biobase_2.48.0
[15] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[17] IRanges_2.22.2 S4Vectors_0.26.1
[19] 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_1.10.1
[5] GenomeInfoDbData_1.2.3 vipor_0.4.5
[7] yaml_2.2.1 pillar_1.4.6
[9] backports_1.1.9 lattice_0.20-41
[11] glue_1.4.2 digest_0.6.25
[13] promises_1.1.1 XVector_0.28.0
[15] colorspace_1.4-1 cowplot_1.0.0
[17] htmltools_0.5.0 httpuv_1.5.4
[19] Matrix_1.2-18 pkgconfig_2.0.3
[21] zlibbioc_1.34.0 purrr_0.3.4
[23] scales_1.1.1 whisker_0.4
[25] later_1.1.0.1 BiocParallel_1.22.0
[27] git2r_0.27.1 tibble_3.0.3
[29] generics_0.0.2 farver_2.0.3
[31] ellipsis_0.3.1 withr_2.4.1
[33] cli_2.4.0 magrittr_1.5
[35] crayon_1.3.4 evaluate_0.14
[37] fansi_0.4.1 fs_1.5.0
[39] beeswarm_0.2.3 tools_4.0.0
[41] lifecycle_1.0.0 stringr_1.4.0
[43] Rhdf5lib_1.10.0 munsell_0.5.0
[45] locfit_1.5-9.4 irlba_2.3.3
[47] compiler_4.0.0 rsvd_1.0.3
[49] rlang_0.4.10 grid_4.0.0
[51] RCurl_1.98-1.3 rstudioapi_0.13
[53] BiocNeighbors_1.6.0 labeling_0.3
[55] bitops_1.0-6 rmarkdown_2.3
[57] gtable_0.3.0 codetools_0.2-16
[59] R6_2.4.1 gridExtra_2.3
[61] knitr_1.29 utf8_1.1.4
[63] rprojroot_1.3-2 stringi_1.4.6
[65] ggbeeswarm_0.6.0 Rcpp_1.0.5
[67] vctrs_0.3.4 tidyselect_1.1.0
[69] xfun_0.15