https://github.com/j-andrews7/onelineromics

One liner analyses and visualizations for bulk RNA-seq and ChIP-seq data
https://github.com/j-andrews7/onelineromics

chip-seq genomics genomics-visualization oneliner-commands oneliners pipelines r rna-seq visualizations

Last synced: 11 months ago
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One liner analyses and visualizations for bulk RNA-seq and ChIP-seq data

• Host: GitHub
• URL: https://github.com/j-andrews7/onelineromics
• Owner: j-andrews7
• License: gpl-3.0
• Created: 2019-09-26T21:20:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-09-02T14:18:11.000Z (over 4 years ago)
• Last Synced: 2025-04-28T12:02:10.558Z (about 1 year ago)
• Topics: chip-seq, genomics, genomics-visualization, oneliner-commands, oneliners, pipelines, r, rna-seq, visualizations
• Language: R
• Homepage: https://j-andrews7.github.io/OneLinerOmics
• Size: 732 KB
• Stars: 7
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# OneLinerOmics

[![Docs](https://img.shields.io/badge/docs-site-brightgreen?style=plastic)](https://j-andrews7.github.io/OneLinerOmics)

This R package contains several functions that compress major RNA-seq and ChIP-seq analysis steps into one-liners (okay, maybe two, I'm not perfect).

## Objective

This package serves as a complement to [Andrews, J. et al., eBioMedicine 2021](https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(21)00352-2/fulltext) and as a record of how the data was analyzed.

## What It Can Do

Ultimately, this package tries to make generic RNA-seq and ChIP-seq analyses as high-level and straightforward as possible. 

It does so by requiring only the path to a samplesheet containing sample metadata and file locations and the metadata variable of interest. 

It will then perform RNA or ChIP-seq analyses with `DESeq2` or `DiffBind`, respectively, for all possible comparisons for the variable of interest and save the results for each analysis to disk.

You can feed it multiple p-value/FDR/fold change thresholds, and it will generate fairly nice plots for differentially expressed genes or differentially bound regions including PCA/MA plots, heatmaps, volcano plots, GO/pathway enrichment analyses, boxplots, etc., saving all of them as PDFs.

## Installation

```r

require("devtools")

devtools::install_github("j-andrews7/OneLinerOmics")

```

## Usage

Please see the [docs](https://j-andrews7.github.io/OneLinerOmics) for a full reference and vignettes with examples (including steps like gene count quantification, peak calling, etc). 

90% of the functionality can be viewed with `?RunDESeq2` and `?RunDiffBind` after installation.

The [salmon](https://combine-lab.github.io/salmon/getting_started/), [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), and [DiffBind](https://bioconductor.org/packages/release/bioc/html/DiffBind.html) manuals and vignettes may also be helpful for understanding what's going on under the hood and interpreting the output.

## Issues

Please direct all complaints to [management](https://github.com/j-andrews7/OneLinerOmics/issues).