The FacileData
package was written to facilitate easier analysis of large, multi-assay high-throughput genomics datasets. To this end, the FacileData
package provides two things:
FacileDataSet
provides efficient storage and retrieval of arbitrarily large high-throughput genomics datasets. For example, a single FacileDataSet
can be used to store all of the RNA-seq, microarray, RPPA, etc. data from the The Cancer Genome Atlas. This singular FacileDataSet
allows analysts easy access to arbitrary subsets of these data without having to load all of it into memory.The FacileData suite of packages is only available from github from now. You will want to install three FacileData*
packages to appreciate the its utility:
# install.packages("devtools")
devtools::install_github("Genentech/FacileData")
As a teaser, we provide code snippets that show how to plot HER2 copy number vs expression across the TCGA "BLCA" and "BRCA" indications using the= FacileDataSet
. We'll then compare that to how the same code might be written using more traditional bioconductor containers.
library(ggplot2)
library(FacileData)
library(FacileTCGADataSet)
tcga <- FacileTCGADataSet()
features <- filter_features(tcga, name == "ERBB2")
fdat <- tcga %>%
filter_samples(indication %in% c("BLCA", "BRCA")) %>%
with_assay_data(features, assay_name = "rnaseq", normalized = TRUE) %>%
with_assay_data(features, assay_name = "cnv_score") %>%
with_sample_covariates(c("indication", "sex"))
ggplot(fdat, aes(cnv_score_ERBB2, ERBB2, color=sex)) +
geom_point() +
facet_wrap(~ indication)