filter_low_counts

filter_low_counts

Usage

filter_low_counts(counts_dat, min_count = 0)

Arguments

counts_dat

expected gene counts from RSEM as a data frame or tibble

min_count

integer number of minimum counts across all samples (default: 0)

Value

filtered counts dataframe

Examples

filter_low_counts(gene_counts) %>% head()
#> # A tibble: 6 × 6
#>   gene_id            GeneName KO_S3 KO_S4 WT_S1 WT_S2
#>   <chr>              <chr>    <dbl> <dbl> <dbl> <dbl>
#> 1 ENSG00000121410.11 A1BG         0     0     0     0
#> 2 ENSG00000268895.5  A1BG-AS1     0     0     0     0
#> 3 ENSG00000148584.15 A1CF         0     0     0     0
#> 4 ENSG00000175899.14 A2M          0     0     0     0
#> 5 ENSG00000245105.3  A2M-AS1      0     0     0     0
#> 6 ENSG00000166535.20 A2ML1        0     0     0     0
filter_low_counts(gene_counts, min_count = 100)
#> # A tibble: 9 × 6
#>   gene_id            GeneName KO_S3 KO_S4 WT_S1 WT_S2
#>   <chr>              <chr>    <dbl> <dbl> <dbl> <dbl>
#> 1 ENSG00000154734.15 ADAMTS1      0   0      46    80
#> 2 ENSG00000142192.21 APP          2   1      78    88
#> 3 ENSG00000185658.13 BRWD1       25  22.1    74   104
#> 4 ENSG00000142156.14 COL6A1       3   6      71   112
#> 5 ENSG00000142173.15 COL6A2       3   0      77    98
#> 6 ENSG00000157540.21 DYRK1A      12   8      31    49
#> 7 ENSG00000159140.20 SON          3   1      74    97
#> 8 ENSG00000160181.9  TFF2         0   0      71    89
#> 9 ENSG00000182670.13 TTC3        12  11      76    85