In bioinfocz/scdrake: A pipeline for droplet-based single-cell RNA-seq data secondary analysis implemented in the drake Make-like toolkit for R language

Gene filtering is the same as for the dataset-sensitive filtering, except the cell count and proportion may change. We excluded genes that are not expressed in our system and don't contribute any information to our experiment. Very lowly expressed genes may only contribute noise.

Table of zero-expression genes count:

janitor::tabyl(sce_custom_filter_rowSums == 0) %>%
  dplyr::rename(zero_expression = `sce_custom_filter_rowSums == 0`) %>%
  dplyr::mutate(percent = scales::percent(percent)) %>%
  scdrake::render_bootstrap_table(full_width = FALSE, position = "left")

Removing r sum(drake::readd(sce_qc_gene_filter, path = drake_cache_dir)) genes with UMI per cell less than r cfg$MIN_UMI and expressed in less than r cfg$MIN_RATIO_CELLS * 100 % of all cells.

Info on custom filtered dataset:

cat(drake::readd(sce_custom_filter_genes_info, path = drake_cache_dir)$str)

bioinfocz/scdrake documentation built on Sept. 19, 2024, 4:43 p.m.