stratify_sig() is supposed to be used in combination after hack_sig() in
order to classify your samples in one of two or more signature classes.
Usage
stratify_sig(sig_data, cutoff = "original", probs = seq(0, 1, 0.25))Arguments
- sig_data
A tibble result of a call to
hack_sig().- cutoff
A character specifying which function to use to categorize samples by signature scores. Can be one of:
"original"(default), apply the original publication method; if categorization is not expected, the median score is used as a threshold;"mean"/"median", samples will be classified as"low"or"high"with respect to the mean/median signature score, respectively;"quantile", samples will be classified into signature score quantiles;
- probs
A numeric vector of probabilities with values in
[0, 1]to use in combination withcutoff = "quantile". By default, it corresponds to quartiles (c(0, 0.25, 0.5, 0.75, 1)).
Value
A tibble with the same dimension as sig_data, having a column sample_id
with sample identifiers and one column for each input signature giving
sample classes.
Examples
scores <- hack_sig(test_expr, "immune")
#> Warning: ℹ No genes are present in 'expr_data' for the following signatures:
#> ✖ rooney2015_cyt
#> ℹ To obtain CINSARC, ESTIMATE and Immunophenoscore with the original procedures, see:
#> ?hack_cinsarc
#> ?hack_estimate
#> ?hack_immunophenoscore
stratify_sig(scores)
#> # A tibble: 20 × 19
#> sample_id ayers2017_immexp bai2019_immune fan2021_ferroptosis fang2021_irgs
#> <chr> <chr> <chr> <chr> <chr>
#> 1 sample1 low high high low
#> 2 sample10 low high high low
#> 3 sample11 high high low high
#> 4 sample12 high high high high
#> 5 sample13 low high low low
#> 6 sample14 high low low low
#> 7 sample15 low low low high
#> 8 sample16 low high high low
#> 9 sample17 low high low low
#> 10 sample18 low low low high
#> 11 sample19 high low high high
#> 12 sample2 low low high low
#> 13 sample20 high high low high
#> 14 sample3 high low high high
#> 15 sample4 high low high low
#> 16 sample5 high low low high
#> 17 sample6 high high high high
#> 18 sample7 high low low low
#> 19 sample8 low high low high
#> 20 sample9 low low high low
#> # ℹ 14 more variables: he2021_ferroptosis_a <chr>, he2021_ferroptosis_b <chr>,
#> # huang2022_ferroptosis <chr>, li2021_ferroptosis_c <chr>,
#> # li2021_ferroptosis_d <chr>, li2021_irgs <chr>, liu2020_immune <chr>,
#> # liu2021_mgs <chr>, lu2020_npc <chr>, lu2021_ferroptosis <chr>,
#> # qiang2021_irgs <chr>, she2020_irgs <chr>, xu2021_ferroptosis <chr>,
#> # zou2020_npc <chr>