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sparrow

R build status pkgdown Project Status: Active Lifecycle: stable codecov

sparrow was built to facilitate the use of gene sets in the analysis of high throughput genomics data (primarily RNA-seq). Analysts can orchestrate any number of GSEA methods across a specific contrast using the unified interface provided by the seas. A shiny application is provided via the sparrow.shiny package that enables the interactive exploration of of GSEA results.

  • The seas function is a wrapper that orchestrates the execution of any number of user-specified gene set enrichment analyses (GSEA) over a particular experimental contrast of interest. This will create a SparrowResult object which stores the results of each GSEA method internally, allowing for easy query and retrieval.

  • A sister sparrow.shiny package provides an explore function, which is invoked on SparrowResult objects returned from a call to seas(). The shiny application facilitates interactive exploration of these GSEA results. This application can also be deployed to a shiny server and can be initialized by uploading a serialized SparrowResult *.rds file.

Full details that outline the use of this software package is provided in the package’s vignette, however a brief description is outlined below.

Usage

A subset of the RNA-seq data tumor/normal samples in the BRCA indication from the TCGA are provided in this package. We will use that data to perform a “camera” and “fry” gene set enrichment analysis of tumor vs normal samples using the MSigDB hallmark gene set collection with sparrow::seas().

library(sparrow)
library(dplyr)
bsc <- getMSigCollection('H', species = 'human', id.type = "entrez")
vm <- exampleExpressionSet(dataset = 'tumor-vs-normal', do.voom = TRUE)
mg <- seas(vm, bsc, c("cameraPR", "fry"), design = vm$design, contrast = "tumor")

We can view the top “camera” results with the smallest pvalues like so:

results(mg, "cameraPR") %>%
  arrange(pval) %>%
  select(name, padj) %>%
  head
#>                        name         padj
#> 1      HALLMARK_E2F_TARGETS 4.303158e-21
#> 2   HALLMARK_G2M_CHECKPOINT 5.412503e-16
#> 3   HALLMARK_MYC_TARGETS_V1 8.642770e-10
#> 4 HALLMARK_MTORC1_SIGNALING 3.170023e-07
#> 5       HALLMARK_MYOGENESIS 3.402614e-06
#> 6   HALLMARK_UV_RESPONSE_DN 6.624519e-06

The shift in expression of the genes within the top gene set can be visualized with the iplot function below. This plot produces interactive graphics, but rasterized versions are saved for use with this README file:

iplot(mg, 'HALLMARK_MYC_TARGETS_V1', type = "density")

iplot(mg, 'HALLMARK_MYC_TARGETS_V1', type = "gsea")

When these plots are rendered in your workspace or an Rmarkdown document, the user can hover of the genes (dots) to see their name and differential expression statistics.

For an immersive, interactive way to explore the GSEA results, use the sparrow.shiny::explore(mg) method!

Installation

This is the development version of the R/bioconductor package {sparrow}. It may contain unstable or untested new features. If you are looking for the release version of this package please go to its official Bioconductor landing page and follow the instructions there to install it.

You can install this development version using the {BiocManager} CRAN package:

BiocManager::install("sparrow", version = "devel")

Alternatively, you can install it from GitHub using the {remotes} package.

remotes::install_github("lianos/sparrow")

To install the shiny bits for this package, you can install the {sparrow.shiny} in a similar way as described above.

Historical Note

This package used to be called multiGSEA), but it’s name was changed to avoid conflict with another package by the same name that was submitted to Bioconductor version 3.12.