Keep high-quality cells based on QC columns

Usage

keep_quality_cells(
  data,
  empty_droplet_col = "empty_droplet",
  alive_col = "alive",
  doublet_col = "scDblFinder.class",
  nfeature_col = "feature_count",
  min_features = 5000L
)

Source

Mangiola et al.,2023

Arguments

data: A data frame or tibble containing single-cell metadata.
empty_droplet_col: A string specifying the column name that indicates empty droplets (default: "empty_droplet"). Expected logical vector
alive_col: A string specifying the column name that indicates whether cells are alive (default: "alive"). Expected logical vector
doublet_col: A string specifying the column name that indicates doublets (default: "scDblFinder.class"). Expected character vector: "doublet" and/or "singlet" and/or "unknown".
nfeature_col: Column containing the number of detected features per dataset
min_features: Minimum number of detected features required.

Value

A filtered data frame containing only cells that pass all QC checks.

Examples

get_metadata(cloud_metadata = SAMPLE_DATABASE_URL, cache_directory = tempdir()) |>
  head(2) |>
  keep_quality_cells()
#> # A query:  ?? x 31
#> # Database: DuckDB 1.5.5 [unknown@Linux 6.17.0-1022-azure:R 4.6.1/:memory:]
#>   cell_id dataset_id     sample_id feature_count age_days nFeature_expressed_i…¹
#>     <dbl> <chr>          <chr>             <int>    <int>                  <int>
#> 1      14 842c6f5d-4a94… 1119f482…         33145    14600                   1547
#> 2      15 842c6f5d-4a94… 1119f482…         33145    14600                   1701
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 25 more variables: nCount_RNA <dbl>, empty_droplet <lgl>,
#> #   cell_type_unified_ensemble <chr>, is_immune <lgl>,
#> #   subsets_Mito_percent <int>, subsets_Ribo_percent <int>,
#> #   high_mitochondrion <lgl>, high_ribosome <lgl>, alive <lgl>,
#> #   scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>,
#> #   file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>, …