You can install the development version of gnomeR
from
GitHub with:
# install.packages("devtools")
devtools::install_github("MSKCC-Epi-Bio/gnomeR")
Along with its companion package for cbioPortal data download:
devtools::install_github("karissawhiting/cbioportalR")
the gnomeR
package provides a consistent framework for genetic data
processing, visualization and analysis. This is primarily targeted to
IMPACT datasets but can also be applied to any genomic data provided by
cBioPortal. With {gnomeR} and {cbioportalR} you can:
{gnomeR} works with any genomic data that follows cBioPortal guidelines for mutation, CNA, or fusion data file formats.
If you wish to pull the data directly from cBioPortal, see how to get set up with credentials with the {cbioportalR} package.
The below examples uses the data sets mutatations
, sv
, cna
which
were pulled from cBioPortal and are included in the package as example
data sets. We will sample 100 samples for examples:
set.seed(123)
mut <- gnomeR::mutations
cna <- gnomeR::cna
sv <- gnomeR::sv
un <- unique(mut$sampleId)
sample_patients <- sample(un, size = 50, replace = FALSE)
The main data processing function is create_gene_binary()
which takes
mutation, CNA and fusion files as input, and outputs a binary matrix of
N rows (number of samples) by M genes included in the data set. We can
specify which patients are included which will force all patients in
resulting dataframe, even if they have no alterations.
gen_dat <- create_gene_binary(samples = sample_patients,
mutation = mut,
fusion = sv,
cna = cna)
head(gen_dat[, 1:6])
#> # A tibble: 6 × 6
#> sample_id ALK APC AR ARAF ATM
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P-0004508-T01-IM5 1 0 0 0 0
#> 2 P-0005806-T01-IM5 0 1 0 0 0
#> 3 P-0007006-T01-IM5 0 1 0 0 0
#> 4 P-0008682-T01-IM5 0 1 0 0 0
#> 5 P-0001297-T01-IM3 0 0 1 0 0
#> 6 P-0007538-T01-IM5 0 0 0 1 0
By default, mutations, CNA and fusions will be returned in separate columns. You can combine these at the gene level using the following:
by_gene <- gen_dat %>%
summarize_by_gene()
head(by_gene[,1:6])
#> sample_id ALK ARAF BLM CD79B CSF1R
#> 1 P-0004508-T01-IM5 1 0 0 0 0
#> 2 P-0005806-T01-IM5 0 0 0 0 0
#> 3 P-0007006-T01-IM5 0 0 0 0 0
#> 4 P-0008682-T01-IM5 0 0 0 0 0
#> 5 P-0001297-T01-IM3 0 0 0 0 0
#> 6 P-0007538-T01-IM5 0 1 0 0 1
You can visualize your processed and raw alteration data sets using {gnomeR}’s many data visualization functions.
Quickly visualize mutation characteristics with ggvarclass()
,
ggvartype()
, ggsnvclass()
, ggsamplevar()
, ggtopgenes()
,
gggenecor()
, and ggcomut()
.
ggvarclass(mutation = mut)
You can tabulate summarize your genomic data frame using the
tbl_genomic()
function, a wrapper for gtsummary::tbl_summary()
.
gen_dat <- gen_dat %>%
dplyr::mutate(trt_status = sample(x = c("pre-trt", "post-trt"),
size = nrow(gen_dat), replace = TRUE))
gene_tbl_trt <- gen_dat %>%
subset_by_frequency(t = .1, other_vars = trt_status) %>%
tbl_genomic(by = trt_status) %>%
gtsummary::add_p()
Additionally, you can analyze custom pathways, or a set of default gene
pathways using add_pathways()
:
path_by_trt <- gen_dat %>%
add_pathways() %>%
select(sample_id, trt_status, contains("pathway_")) %>%
tbl_genomic(by = trt_status) %>%
gtsummary::add_p()
Please note that the gnomeR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Thank you to all contributors!
@akriti21, @alrein-05, @arorarshi, @AxelitoMartin, @carokos, @ChristineZ-msk, @edrill, @hfuchs5, @jalavery, @jflynn264, @karissawhiting, @michaelcurry1123, @mljaniczek, and @slb2240