https://github.com/kpj/rwrap
Seamlessly integrate R packages into Python.
https://github.com/kpj/rwrap
Last synced: 25 days ago
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Seamlessly integrate R packages into Python.
- Host: GitHub
- URL: https://github.com/kpj/rwrap
- Owner: kpj
- License: mit
- Created: 2019-09-17T12:34:54.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-12T06:09:47.000Z (about 2 years ago)
- Last Synced: 2025-04-28T14:21:53.349Z (25 days ago)
- Language: Python
- Homepage:
- Size: 82 KB
- Stars: 21
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# rwrap
[](https://pypi.python.org/pypi/rwrap)
[](https://github.com/kpj/rwrap/actions/workflows/main.yml)
A thin wrapper around [rpy2](https://rpy2.github.io/doc/latest/html/index.html) with strong opinions on how data types should be converted. This enables easy usage of R packages from Python with no boilerplate code.
> Warning: still work-in-progress, issues and PRs welcome
## Installation
```bash
pip install rwrap
```
## Usage
### Genomic Annotations
Accessing Bioconductor's [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html) package can be as simple as follows:
```python
from rwrap import biomaRt
biomaRt
##
snp_list = ["rs7329174", "rs4948523", "rs479445"]
ensembl = biomaRt.useMart("ENSEMBL_MART_SNP", dataset="hsapiens_snp")
df = biomaRt.getBM(
attributes=["refsnp_id", "chr_name", "chrom_start", "consequence_type_tv"],
filters="snp_filter", values=snp_list, mart=ensembl
)
print(df) # pandas.DataFrame
## refsnp_id chr_name chrom_start consequence_type_tv
## 1 rs479445 1 60875960 intron_variant
## 2 rs479445 1 60875960 NMD_transcript_variant
## 3 rs4948523 10 58579338 intron_variant
## 4 rs7329174 13 40983974 intron_variant
```
### Differential Gene Expression analysis workflow
Differentially expressed genes between conditions can be determined using [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) and annotated with [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html):
```python
import pandas as pd
from rwrap import DESeq2, biomaRt, base, stats
DESeq2
##
biomaRt
##
# retrieve count data (https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP009615)
df_counts = pd.read_csv(
"http://duffel.rail.bio/recount/v2/SRP009615/counts_gene.tsv.gz", sep="\t"
).set_index("gene_id")
df_design = pd.DataFrame(
{"condition": ["1", "2", "1", "2", "3", "4", "3", "4", "5", "6", "5", "6"]},
index=df_counts.columns
)
# run differential gene expression analysis
dds = DESeq2.DESeqDataSetFromMatrix(
countData=df_counts, colData=df_design, design=stats.as_formula("~ condition")
)
dds = DESeq2.DESeq(dds)
res = DESeq2.results(dds, contrast=("condition", "1", "2"))
df_res = base.as_data_frame(res)
# annotate result
ensembl = biomaRt.useEnsembl(biomart="genes", dataset="hsapiens_gene_ensembl")
df_anno = biomaRt.getBM(
attributes=["ensembl_gene_id_version", "gene_biotype"],
filters="ensembl_gene_id_version",
values=df_res.index,
mart=ensembl,
).set_index("ensembl_gene_id_version")
df_res = df_res.merge(df_anno, left_index=True, right_index=True).sort_values("padj")
print(df_res.head()) # pd.DataFrame
## baseMean log2FoldChange lfcSE stat pvalue padj gene_biotype
## ENSG00000222806.1 158.010377 22.137400 2.745822 8.062214 7.492501e-16 2.853744e-11 rRNA_pseudogene
## ENSG00000255099.1 65.879611 21.835651 2.915452 7.489627 6.906949e-14 1.315359e-09 processed_pseudogene
## ENSG00000261065.1 92.351998 22.273400 3.144991 7.082182 1.419019e-12 1.351190e-08 lncRNA
## ENSG00000249923.1 154.037908 18.364027 2.636083 6.966407 3.251381e-12 2.476772e-08 lncRNA
## ENSG00000267658.1 64.371181 -19.545702 3.041247 -6.426871 1.302573e-10 8.268736e-07 lncRNA
```
### Geneset Enrichment Analysis
Geneset enrichment analyses can be conducted using [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html):
```python
from rwrap import clusterProfiler, base
clusterProfiler
##
genelist = [8318, 991, 9133, 890, 983, 4085, 7272, 1111, 891, 4174, 9232]
res = clusterProfiler.enrichKEGG(gene=genelist, organism="hsa", pvalueCutoff=0.05)
df = base.as_data_frame(res)
print(df.head()) # pd.DataFrame
## ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
## hsa04110 hsa04110 Cell cycle 11/11 126/8115 8.124144e-21 1.462346e-19 6.841384e-20 8318/991/9133/890/983/4085/7272/1111/891/4174/... 11
## hsa04114 hsa04114 Oocyte meiosis 6/11 131/8115 6.823856e-09 6.141470e-08 2.873202e-08 991/9133/983/4085/891/9232 6
## hsa04914 hsa04914 Progesterone-mediated oocyte maturation 5/11 102/8115 1.237164e-07 7.266746e-07 3.399647e-07 9133/890/983/4085/891 5
## hsa05166 hsa05166 Human T-cell leukemia virus 1 infection 6/11 222/8115 1.614832e-07 7.266746e-07 3.399647e-07 991/9133/890/4085/1111/9232 6
## hsa04218 hsa04218 Cellular senescence 5/11 156/8115 1.036418e-06 3.731103e-06 1.745545e-06 9133/890/983/1111/891 5
```
### More examples
Check the `tests/scripts` directory for more examples showing how to rewrite R scripts in Python.
## Tests
A comprehensive test suite aims at providing stability and avoiding regressions.
The examples in `tests/` are validated using `pytest`.
Run tests as follows:
```bash
$ pytest tests/
```