The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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:
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.
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")You can learn more about usage of the package in
vignette("hacksig").
library(hacksig)
library(dplyr)
library(future)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 rowscheck_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]>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 rowstest_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 rowsplan(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>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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.