Quality filtering

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Last updated: 2020-10-09

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Knit directory: neural_scRNAseq/

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/TDP-03-filtering.Rmd) and HTML (docs/TDP-03-filtering.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
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                    4229

colData(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()

plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") + 
  scale_y_log10()

plotColData(sce, x = "sample_id", y = "subsets_Mt_percent", 
            colour_by = "discard")

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()

plotColData(sce, x = "sample_id", y = "detected", colour_by = "manual_discard") + 
  scale_y_log10()

## 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()

plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") + 
  scale_y_log10()

plotColData(sce, x = "sample_id", y = "subsets_Mt_percent", 
            colour_by = "discard")

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")    
}

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
5a50966	khembach	2020-10-07

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 73013

sce <- 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

Save 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"))

Session information

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_1.16.1            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_2.2.0              
[33] magrittr_1.5              crayon_1.3.4             
[35] evaluate_0.14             fs_1.4.2                 
[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] locfit_1.5-9.4            irlba_2.3.3              
[45] compiler_4.0.0            rsvd_1.0.3               
[47] rlang_0.4.7               grid_4.0.0               
[49] RCurl_1.98-1.2            BiocNeighbors_1.6.0      
[51] labeling_0.3              bitops_1.0-6             
[53] rmarkdown_2.3             gtable_0.3.0             
[55] codetools_0.2-16          R6_2.4.1                 
[57] gridExtra_2.3             knitr_1.29               
[59] rprojroot_1.3-2           stringi_1.4.6            
[61] ggbeeswarm_0.6.0          Rcpp_1.0.5               
[63] vctrs_0.3.4               tidyselect_1.1.0         
[65] xfun_0.15