https://github.com/blahah/dexperimentr

Differential expression experiment workflows
https://github.com/blahah/dexperimentr

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Differential expression experiment workflows

• Host: GitHub
• URL: https://github.com/blahah/dexperimentr
• Owner: blahah
• License: mit
• Created: 2014-01-20T12:39:29.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2019-07-10T10:50:37.000Z (over 5 years ago)
• Last Synced: 2024-04-16T01:01:02.304Z (7 months ago)
• Language: R
• Size: 58.6 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
dexperimentr

============

dexperimentr is a collection of tools for automating best-practise RNAseq differential expression workflows.

**Warning!** This is pre-release software. Please don't cry if it doesn't work. Check back soon for the first release.

## Usage

**!! WARNING !!** these instructions will eventually be true... but they're not yet. At the moment, this is more of a design-by-help document.

To run the entire workflow, you need the following:

1. your count data as a dataframe, samples as named columns and genes/transcripts as named rows

2. the path to your annotation file, which is a TSV with the first column being gene/transcript identifiers, and subsequent columns containing information about those genes that you want to include in the output

3. Your long-form GO term file, which is a TSV with columns: go.id, id, term, ontology, definition (see below)

Then you just run the pipeline:

```R

library(dexperimentr)

run_DE_workflow(counts, annotation, gene_ontology)

```

An HTML report, `report.html`, will be generated with a log of the process and diagnostic plots and guidance to help you interpret your data.

In addition, the following directories will be created and populated:

- `count_qc/`: Basic quality control information about your count data

- `de_data/`: Expression data, differential expression probabilities and expression patterns

- `de_qc/`: Quality control information about the differential expression experiment

- `functional_analysis/`: Gene Ontology functional category enrichment analysis data and plots