Filter cells and features

Source: R/preprocessing_filtering_reduction.R

Function to filter out cells & features from SingleCellExperiment based on total count per cell, number of cells 'ON' in features and top covered cells that might be doublets.

filter_scExp(
  scExp,
  min_cov_cell = 1600,
  quant_removal = 95,
  min_count_per_feature = 10,
  verbose = TRUE
)

Arguments

scExp

A SingleCellExperiment object.
min_cov_cell

Minimum counts for each cell. (1600)
quant_removal

Centile of cell counts above which cells are removed. (95)
min_count_per_feature

Minimum number of reads per feature (10).
verbose

(TRUE)

Value

Returns a filtered SingleCellExperiment object.

Examples


scExp = create_scExp(create_scDataset_raw()$mat,create_scDataset_raw()$annot)

#> ChromSCape::create_scExp - the matrix has 300 cells and 600 features.
scExp. = filter_scExp(scExp)

#> ChromSCape::filter_scExp - 285 cells pass the  threshold of 1600 minimum reads and are lower than the 95th centile of library size ~= 14692 reads.
#> ChromSCape::filter_scExp - 600 features pass the threshold of 10 count per feature.

# No feature filtering (all features are valuable)
scExp. = filter_scExp(scExp,min_count_per_feature=30)

#> ChromSCape::filter_scExp - 285 cells pass the  threshold of 1600 minimum reads and are lower than the 95th centile of library size ~= 14692 reads.
#> ChromSCape::filter_scExp - 600 features pass the threshold of 30 count per feature.

# No cell filtering (all features are valuable)
scExp. = filter_scExp(scExp,min_cov_cell=0,quant_removal=100)

#> ChromSCape::filter_scExp - 300 cells pass the  threshold of 0 minimum reads and are lower than the 100th centile of library size ~= 16237 reads.
#> ChromSCape::filter_scExp - 600 features pass the threshold of 10 count per feature.