You can install the pre-2022-03-23 version with:
remotes::install_github('MSKCC-Epi-Bio/[email protected]')
You can install the development version of gnomeR
from
GitHub with:
# install.packages("devtools")
devtools::install_github("AxelitoMartin/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.
- Dowloading and gathering data from
CbioPortal through
an integrated API using simply the sample IDs of the samples of
interests or the name of the study to retrieve all samples in that
study. A separate package
cbioportalr
was developed independently. - Processing genomic data retrieved for mutations (MAF file), fusions (MAF file) and copy-number alterations (and when available segmentation files) into an analysis ready format.
- Visualization of the processed data provided through MAF file summaries, OncoPrints and heatmaps.
- Analyzing the processed data for association with binary, continuous and survival outcome. Including further visualization to improve understanding of the results.
In order to download the data from CbioPortal, one must first require a token from the website CbioPortal wich will prompt a login page with your MSKCC credentials. Then navigate to “Web API” in the top bar menu, following this simply download a token and copy it after running the following command in R:
usethis::edit_r_environ()
And pasting the token you were given in the .Renviron file that was created and saving after pasting your token.
CBIOPORTAL_TOKEN = 'YOUR_TOKEN'
You can test your connection using:
cbioportalr::get_cbioportal_token()
Now that the Cbioportal API is set up in your environment, you must first specify the database of interest (IMPACT or TCGA are the two available options). Following this one can either specify the samples or study of interest:
library(gnomeR)
library(cbioportalr)
ids <- as.character(unique(mut$Tumor_Sample_Barcode)[1:100])
df <- get_genetics(sample_ids = ids,database = "msk_impact",
mutations = TRUE, fusions = TRUE, cna = TRUE)
The binmat()
function is the feature of the data processing of
gnomeR
. It takes genomic inputs from various sources of CbioPortal
(mutation files, fusion files and copy number raw counts) to give out a
clean binary matrix of n samples by all the events that were found in
the files.
df.clean <- binmat(maf = df$mut, cna = df$cna)
We further included example datasets from the raw dowloaded files on
CbioPortal (mut
, fusion
, cna
) which we will use for the following
examples.
set.seed(123)
patients <- as.character(unique(mut$Tumor_Sample_Barcode))[sample(1:length(unique(mut$Tumor_Sample_Barcode)), 100, replace=FALSE)]
gen_dat <- binmat(patients = patients, maf = mut, fusion = fusion, cna = cna)
kable(gen_dat[1:10,1:10],row.names = TRUE)
TP53 | IGF1R | KEAP1 | KDM5C | KRAS | TERT | MAP2K1 | NCOR1 | DDR2 | FIP1L1 | |
---|---|---|---|---|---|---|---|---|---|---|
P-0010604-T01-IM5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0002651-T01-IM3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0000270-T01-IM3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0002915-T01-IM3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0011099-T01-IM5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0000080-T01-IM3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0001741-T01-IM3 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
P-0003964-T01-IM3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
P-0003842-T01-IM5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P-0002597-T02-IM5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Before we move on to more complex visualizations, we integrate the
maf_viz()
function to give an overview of the distribution of the
different mutations across the cohort of interest:
sum.plots <- maf_viz(maf = mut %>% filter(Tumor_Sample_Barcode %in% patients))
sum.plots$topgenes
sum.plots$genecomut
OncoPrints are a convenient way to display the overall genomic profiles of samples in the cohort of interest. This is best used for a subset of genes that are under consideration.
genes <- c("TP53","PIK3CA","KRAS","TERT","EGFR","FAT","ALK","CDKN2A","CDKN2B")
plot_oncoprint(gen_dat = gen_dat %>% select(starts_with(genes)))
FACETs is an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that includes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output.
p.heat <- facets_heatmap(seg = seg, patients = patients, min_purity = 0)
p.heat$p
In this section we will quickly overview the possible analysis in gnomeR.
The gen_summary()
function let’s the user perform a large scale
association between the genomic features present in the binmat()
function output and an outcome of choice:
- binary (unpaired test using Fisher’s exact test and paired test using McNemmar’s exact test)
- continuous (using simple linear regression)
outcome <- factor(rbinom(n = length(patients),size = 1,prob = 1/2),levels = c("0","1"))
# out <- gen_summary(gen_dat = gen_dat,outcome = outcome,filter = 0.05)
# kable(out$fits[1:10,],row.names = TRUE)
# out$forest.plot
Similarly we include simple tools to perform univariate Cox’s
proportional regression adjusted for false discovery rate in the
gen_uni_cox()
function.
time <- rexp(length(patients))
status <- outcome
surv_dat <- as.data.frame(cbind(time,status))
out <- gen_uni_cox(X = gen_dat, surv_dat = surv_dat, surv_formula = Surv(time,status)~.,filter = 0.05)
kable(out$tab[1:10,],row.names = TRUE)
Feature | Coefficient | HR | Pvalue | FDR | MutationFrequency | |
---|---|---|---|---|---|---|
MLL | MLL | -1.48 | 0.23 | 0.0155 | 0.496 | 0.09 |
STK11 | STK11 | -1.46 | 0.23 | 0.0519 | 0.831 | 0.07 |
KEAP1 | KEAP1 | -1.02 | 0.36 | 0.1670 | 0.922 | 0.05 |
NOTCH1 | NOTCH1 | 0.61 | 1.84 | 0.2070 | 0.922 | 0.08 |
DOT1L | DOT1L | 0.66 | 1.93 | 0.2110 | 0.922 | 0.05 |
TSC1 | TSC1 | 0.75 | 2.12 | 0.2130 | 0.922 | 0.05 |
CDH1 | CDH1 | 0.60 | 1.83 | 0.2520 | 0.922 | 0.06 |
EPHA5 | EPHA5 | -1.08 | 0.34 | 0.2870 | 0.922 | 0.05 |
PIK3CA | PIK3CA | 0.41 | 1.51 | 0.2950 | 0.922 | 0.12 |
PTPRD | PTPRD | 0.49 | 1.63 | 0.3570 | 0.922 | 0.08 |
out$KM[[1]]
The primary goal of gnomeR
not being in depth analysis of genomic data
but rather reliable, modulable and reproducible framework for processing
various types of genomic data. For users interested in large scale
genomic analytical methods we compiled various packages developed by
Department of Epidemiology and
Biostatistics,
Memorial Sloan-Kettering Cancer Center under an umbrella R package,
gnomeVerse.