https://github.com/databio/deseqpackager
Generates a countTable from a PEP for DESeq analysis
https://github.com/databio/deseqpackager
Last synced: 11 months ago
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Generates a countTable from a PEP for DESeq analysis
- Host: GitHub
- URL: https://github.com/databio/deseqpackager
- Owner: databio
- Created: 2017-11-22T21:28:43.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-02-15T20:40:52.000Z (over 7 years ago)
- Last Synced: 2025-03-05T04:44:27.946Z (over 1 year ago)
- Language: R
- Homepage:
- Size: 12.2 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DESeqPackager
From Aaron Gu
```R
DESeqPackager(pepr, data_source, gene_names, gene_counts)
```
DESeqPackager takes in an RNA-seq project in [PEP format](https://pepkit.github.io/docs/pepr/) and combines the data for each sample into a DESeq countDataSet structure. It's best used with output from the [rnapipe](https://github.com/databio/rnapipe) pipeline.
### Required Packages
- pepr
- data.table
## Quick Start
The quick start uses sample gene expression data available for download at `expr_tsv.tar.gz` from http://big.databio.org/example_data/deseq_packager/
In a new R file, load in the function
```R
source("DESeqPackager.R")
```
Construct a pepr project using the yaml located in this repository
```R
p = pepr::Project(file="project_config.yaml")
```
Run DESeqPackager
```R
countDataSet <- DESeqPackager(p, "data_source", "ensembl_gene_id", "FPKM")
```
### Running DESeqPackager with BioConductor
[BiocProject](http://code.databio.org/BiocProject/index.html) is another way to read in biological data in the PEP format. It can load both project metadata and sample data in a single line of code.
```R
bpArgs = BiocProject(file="project_config.yaml", funcArgs=list(data_source="data_source", gene_names="ensembl_gene_id", gene_counts="FPKM"))
getData(bpArgs)
```
Another option is to put the `funcArgs` in the yaml file.
```R
bpArgs = BiocProject(file="project_config.yaml")
getData(bpArgs)
```