A Tidy Framework to Hack Gene Expression Signatures.
hacksig
The goal of hacksig is to provide a simple and tidy interface to compute single sample scores for gene signatures and methods applied in cancer transcriptomics.
Scores can be obtained either for custom lists of genes or for a manually curated collection of gene signatures, including:
- CINSARC;
- ESTIMATE;
- Immunophenoscore;
- Cytolitic activity;
- and more (use
get_sig_info()for a complete list of gene signatures implemented)
One can choose to apply either the original publication method or one of three single sample scoring alternatives, namely: combined z-score, single sample GSEA and singscore.
Installation
You can install the released version of hacksig from CRAN with:
install.packages("hacksig")
Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("Acare/hacksig")
Usage
You can learn more about usage of the package in vignette("hacksig").
library(hacksig)
library(dplyr)
library(future)
Get available signatures
get_sig_info()
#> # A tibble: 23 × 4
#> signature_id signature_keywords publication_doi description
#> <chr> <chr> <chr> <chr>
#> 1 ayers2017_immexp ayers2017_immexp|immune expand… 10.1172/JCI911… Immune exp…
#> 2 bai2019_immune bai2019_immune|head and neck|h… 10.1155/2019/3… Immune/inf…
#> 3 cinsarc cinsarc|metastasis|sarcoma|sts 10.1038/nm.2174 Biomarker …
#> 4 dececco2014_int172 dececco2014_int172|head and ne… 10.1093/annonc… Signature …
#> 5 eschrich2009_rsi eschrich2009_rsi|radioresistan… 10.1016/j.ijro… Genes aime…
#> # … with 18 more rows
Check your signatures
check_sig(test_expr, signatures = "estimate")
#> # A tibble: 2 × 5
#> signature_id n_genes n_present frac_present missing_genes
#> <chr> <int> <int> <dbl> <named list>
#> 1 estimate_stromal 141 91 0.645 <chr [50]>
#> 2 estimate_immune 141 74 0.525 <chr [67]>
Compute single sample scores
hack_sig(test_expr, signatures = c("ifng", "cinsarc"), method = "zscore")
#> # A tibble: 20 × 3
#> sample_id cinsarc muro2016_ifng
#> <chr> <dbl> <dbl>
#> 1 sample1 -0.482 -0.511
#> 2 sample2 -2.61 0.400
#> 3 sample3 1.44 0.347
#> 4 sample4 -0.538 0.0849
#> 5 sample5 -0.537 0.390
#> # … with 15 more rows
Stratify your samples
test_expr %>%
hack_sig("estimate", method = "singscore", direction = "up") %>%
hack_class(cutoff = "median")
#> # A tibble: 20 × 3
#> sample_id estimate_immune estimate_stromal
#> <chr> <chr> <chr>
#> 1 sample1 low low
#> 2 sample2 high low
#> 3 sample3 low low
#> 4 sample4 low high
#> 5 sample5 low high
#> # … with 15 more rows
Speed-up computation time
plan(multisession)
hack_sig(test_expr, method = "ssgsea")
#> Warning: ℹ No genes are present in 'expr_data' for the following signatures:
#> x rooney2015_cyt
#> # A tibble: 20 × 23
#> sample_id ayers2017_immexp bai2019_immune cinsarc dececco2014_int172
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 sample1 -3914. 2316. -13.5 1288.
#> 2 sample2 -3348. 1350. -1070. 1322.
#> 3 sample3 1697. 1829. 1805. 685.
#> 4 sample4 366. 5611. 326. 1684.
#> 5 sample5 969. 1224. 290. 718.
#> # … with 15 more rows, and 18 more variables: eschrich2009_rsi <dbl>,
#> # estimate_immune <dbl>, estimate_stromal <dbl>, eustace2013_hypoxia <dbl>,
#> # fang2021_irgs <dbl>, hu2021_derbp <dbl>, ips_cp <dbl>, ips_ec <dbl>,
#> # ips_mhc <dbl>, ips_sc <dbl>, li2021_irgs <dbl>, liu2020_immune <dbl>,
#> # liu2021_mgs <dbl>, lohavanichbutr2013_hpvneg <dbl>, muro2016_ifng <dbl>,
#> # qiang2021_irgs <dbl>, she2020_irgs <dbl>, wu2020_metabolic <dbl>
Contributing
If you have any suggestions about adding new features to hacksig, please open an issue request on GitHub. Gene-level information about gene signatures are stored in data-raw/hacksig_signatures.csv and can be used as a template for requests.