Last updated: 2020-10-07

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Knit directory: neural_scRNAseq/

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File Version Author Date Message
Rmd d425729 khembach 2020-10-07 preprocessing of new sequencing data

Load packages

library(DropletUtils)
library(scDblFinder)
library(BiocParallel)
library(ggplot2)
library(scater)

Importing CellRanger output and metadata

fs <- dir(path = "data/Sep2020/filtered_feature_matrices", 
                pattern = "^[NT]*", 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]

Detect doublets

# 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 NC223a NC223b TDP2wON TDP4wOFF TDP4wONa TDP4wONb
          doublet   1877   2415    1415      854     2548     1799
          singlet  12647  14221   11030     8758    14112    12245
# 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-axis
Warning: Removed 617 rows containing missing values (geom_bar).

## 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] "NC223a"

[1] "NC223b"

[1] "TDP2wON"

[1] "TDP4wOFF"

[1] "TDP4wONa"

[1] "TDP4wONb"

Save data to RDS

saveRDS(sce, file.path("output", "sce_TDP_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_1.16.2               ggplot2_3.3.2              
 [3] BiocParallel_1.22.0         scDblFinder_1.2.0          
 [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_1.24.2        
[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_3.30.3             
 [3] BiocSingular_1.4.0        viridisLite_0.3.0        
 [5] DelayedMatrixStats_1.10.1 R.utils_2.9.2            
 [7] statmod_1.4.34            dqrng_0.2.1              
 [9] vipor_0.4.5               GenomeInfoDbData_1.2.3   
[11] yaml_2.2.1                pillar_1.4.6             
[13] backports_1.1.9           lattice_0.20-41          
[15] glue_1.4.2                limma_3.44.3             
[17] digest_0.6.25             promises_1.1.1           
[19] XVector_0.28.0            randomForest_4.6-14      
[21] colorspace_1.4-1          cowplot_1.0.0            
[23] htmltools_0.5.0           httpuv_1.5.4             
[25] Matrix_1.2-18             R.oo_1.23.0              
[27] pkgconfig_2.0.3           zlibbioc_1.34.0          
[29] purrr_0.3.4               scales_1.1.1             
[31] HDF5Array_1.16.1          whisker_0.4              
[33] later_1.1.0.1             git2r_0.27.1             
[35] tibble_3.0.3              farver_2.0.3             
[37] generics_0.0.2            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_1.4.2                 
[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_0.4.7              
[57] rhdf5_2.32.2              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_2.3            
[65] codetools_0.2-16          gtable_0.3.0             
[67] R6_2.4.1                  gridExtra_2.3            
[69] knitr_1.29                dplyr_1.0.2              
[71] rprojroot_1.3-2           ggbeeswarm_0.6.0         
[73] stringi_1.4.6             Rcpp_1.0.5               
[75] scran_1.16.0              vctrs_0.3.4              
[77] tidyselect_1.1.0          xfun_0.15