| Name | IRescue JSON |
| Version |
1.1.2
JSON |
| download |
| home_page | None |
| Summary | Interspersed Repeats singl-cell quantifier |
| upload_time | 2024-09-12 16:20:05 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2022-2024 Benedetto Polimeni Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
bioinformatics
transposable-elements
scrna-seq
single-cell
single-cell-rna-seq
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.org/project/irescue/)
[](#container)
[](https://bioconda.github.io/recipes/irescue/README.html)
[](https://doi.org/10.1101/2022.09.16.508229)
[](https://doi.org/10.5281/zenodo.13479364)
# IRescue - <ins>I</ins>nterspersed <ins>Re</ins>peats <ins>s</ins>ingle-<ins>c</ins>ell q<ins>u</ins>antifi<ins>e</ins>r
<img align="right" height="160" src="docs/logo.png">
IRescue quantifies the expression fo transposable elements (TEs) subfamilies in single cell RNA sequencing (scRNA-seq) data, performing UMI-deduplication with sequencing errors correction and probabilistic assignment of multi-mapping reads by Expectation-Maximization (EM). The output is written on a sparse matrix compatible with Seurat, Scanpy and other toolkits.
## Content
- [Installation](#installation)
- [Using conda](#conda)
- [Using pip](#pip)
- [Container (Docker/Singularity)](#container)
- [Usage](#usage)
- [Quick start](#quick_start)
- [Output files](#output_files)
- [Load IRescue data with Seurat](#seurat)
- [Cite](#cite)
## <a name="installation"></a>Installation
### <a name="conda"></a>Using conda (recommended)
We recommend using conda, as it will install all the required packages along IRescue.
```bash
conda create -n irescue -c conda-forge -c bioconda irescue
```
### <a name="pip"></a>Using pip
If for any reason it's not possible or desiderable to use conda, it can be installed with pip and the following requirements must be installed manually: `python>=3.8`, `samtools>=1.12`, `bedtools>=2.30.0`, and fairly recent versions of the GNU utilities are required, specifically `gawk>=5.0.1`, `coreutils>=8.30` and `gzip>=1.10` (older versions are untested).
```bash
pip install irescue
```
### Build from source
By building the package directly from the source, you can try out the features and bug fixes that will be implemented in the future release. As above, you need to install some requirements manually. Be aware that builds from the development branches may be unstable.
```bash
git clone https://github.com/bodegalab/irescue
cd irescue
pip install .
```
### <a name="container"></a>Container (Docker/Singularity)
Docker and Singularity containers are available for each conda release of IRescue. Choose the `TAG` corresponding to the desired IRescue version [from the Biocontainers repository](https://quay.io/repository/biocontainers/irescue?tab=tags) and pull or execute the container with Docker or Singularity:
```bash
# Get latest biocontainers tag (with curl and python3, otherwise check the above link for the desired version/tag)
TAG=$(curl -s -X GET https://quay.io/api/v1/repository/biocontainers/irescue/tag/ | python3 -c 'import json,sys;obj=json.load(sys.stdin);print(obj["tags"][0]["name"])')
# Run with Docker
docker run quay.io/biocontainers/irescue:$TAG irescue --help
# Run with Singularity
singularity exec https://depot.galaxyproject.org/singularity/irescue:$TAG irescue --help
```
## <a name="usage"></a>Usage
```sh
irescue --help
```
The only required input is a BAM file annotated with cell barcode and UMI sequences as tags (by default, `CB` tag for cell barcode and `UR` tag for UMI; override with `--cb-tag` and `--umi-tag`).
You can obtain it by aligning your reads using [STARsolo](https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md). It is advised to keep secondary alignments in BAM file, that will be used in the EM procedure to assign multi-mapping reads (e.g. `--outFilterMultimapNmax 100 --winAnchorMultimapNmax 100` or more), and remember to output all the needed SAM attributes (e.g. `--outSAMattributes NH HI AS nM NM MD jM jI XS MC ch cN CR CY UR UY GX GN CB UB sM sS sQ`).
RepeatMasker annotation will be automatically downloaded for the chosen genome assembly (e.g. `-g hg38`), or provide your own annotation in bed format (e.g. `-r TE.bed`).
```sh
irescue -b genome_alignments.bam -g hg38
```
If you already obtained gene-level counts (using STARsolo, Cell Ranger, Alevin, Kallisto or other tools), it is advised to provide the whitelisted cell barcodes list as a text file (`-w barcodes.tsv`). This will significantly improve performance by processing viable cells only.
For optimal run time, use at least, e.g.: `-p 8`.
### <a name="output_files"></a>Output files
IRescue generates TE counts in a sparse matrix readable by [Seurat](https://github.com/satijalab/seurat) or [Scanpy](https://github.com/scverse/scanpy) into a `counts/` subdirectory. Optional outputs include a description of equivalence classes with UMI deduplication stats `ec_dump.tsv.gz` and a subdirectory of temporary files `tmp/` for debugging purpose. A detailed logging is enabled by `--verbose` and written to standard error.
```
irescue_out/
├── counts/
│ ├── barcodes.tsv.gz
│ ├── features.tsv.gz
│ └── matrix.mtx.gz
├── ec_dump.tsv.gz
└── tmp/
```
### <a name="seurat"></a>Load IRescue data with Seurat
To integrate TE counts into an existing Seurat object containing gene expression data, they can be added as an additional assay:
```R
# import TE counts from IRescue output directory
te.data <- Seurat::Read10X('./IRescue_out/', gene.column = 1, cell.column = 1)
# create Seurat assay from TE counts
te.assay <- Seurat::CreateAssayObject(te.data)
# subset the assay by the cells already present in the Seurat object (in case it has been filtered)
te.assay <- subset(te.assay, colnames(te.assay)[which(colnames(te.assay) %in% colnames(seurat_object))])
# add the assay in the Seurat object
seurat_object[['TE']] <- irescue.assay
```
The result will be something like this:
```
An object of class Seurat
32276 features across 42513 samples within 2 assays
Active assay: RNA (31078 features, 0 variable features)
1 other assay present: TE
```
From here, TE expression can be normalized. To normalize according to gene counts or TE+gene counts, normalize manually or merge the assays. Reductions can be made using TE, gene or TE+gene expression.
## <a name="cite"></a>Cite
Polimeni B, Marasca F, Ranzani V, Bodega B.
*IRescue: uncertainty-aware quantification of transposable elements expression at single cell level.*
bioRxiv 2022.09.16.508229; doi: https://doi.org/10.1101/2022.09.16.508229
Raw data
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"description": "\n[](https://pypi.org/project/irescue/)\n[](#container)\n[](https://bioconda.github.io/recipes/irescue/README.html)\n[](https://doi.org/10.1101/2022.09.16.508229)\n[](https://doi.org/10.5281/zenodo.13479364)\n\n# IRescue - <ins>I</ins>nterspersed <ins>Re</ins>peats <ins>s</ins>ingle-<ins>c</ins>ell q<ins>u</ins>antifi<ins>e</ins>r\n\n<img align=\"right\" height=\"160\" src=\"docs/logo.png\">\nIRescue quantifies the expression fo transposable elements (TEs) subfamilies in single cell RNA sequencing (scRNA-seq) data, performing UMI-deduplication with sequencing errors correction and probabilistic assignment of multi-mapping reads by Expectation-Maximization (EM). The output is written on a sparse matrix compatible with Seurat, Scanpy and other toolkits.\n\n## Content\n\n- [Installation](#installation)\n - [Using conda](#conda)\n - [Using pip](#pip)\n - [Container (Docker/Singularity)](#container)\n- [Usage](#usage)\n - [Quick start](#quick_start)\n - [Output files](#output_files)\n - [Load IRescue data with Seurat](#seurat)\n- [Cite](#cite)\n\n## <a name=\"installation\"></a>Installation\n\n### <a name=\"conda\"></a>Using conda (recommended)\n\nWe recommend using conda, as it will install all the required packages along IRescue.\n\n```bash\nconda create -n irescue -c conda-forge -c bioconda irescue\n```\n\n### <a name=\"pip\"></a>Using pip\n\nIf for any reason it's not possible or desiderable to use conda, it can be installed with pip and the following requirements must be installed manually: `python>=3.8`, `samtools>=1.12`, `bedtools>=2.30.0`, and fairly recent versions of the GNU utilities are required, specifically `gawk>=5.0.1`, `coreutils>=8.30` and `gzip>=1.10` (older versions are untested).\n\n```bash\npip install irescue\n```\n\n### Build from source\n\nBy building the package directly from the source, you can try out the features and bug fixes that will be implemented in the future release. As above, you need to install some requirements manually. Be aware that builds from the development branches may be unstable.\n\n```bash\ngit clone https://github.com/bodegalab/irescue\ncd irescue\npip install .\n```\n\n### <a name=\"container\"></a>Container (Docker/Singularity)\n\nDocker and Singularity containers are available for each conda release of IRescue. Choose the `TAG` corresponding to the desired IRescue version [from the Biocontainers repository](https://quay.io/repository/biocontainers/irescue?tab=tags) and pull or execute the container with Docker or Singularity:\n\n```bash\n# Get latest biocontainers tag (with curl and python3, otherwise check the above link for the desired version/tag)\nTAG=$(curl -s -X GET https://quay.io/api/v1/repository/biocontainers/irescue/tag/ | python3 -c 'import json,sys;obj=json.load(sys.stdin);print(obj[\"tags\"][0][\"name\"])')\n\n# Run with Docker\ndocker run quay.io/biocontainers/irescue:$TAG irescue --help\n\n# Run with Singularity\nsingularity exec https://depot.galaxyproject.org/singularity/irescue:$TAG irescue --help\n```\n\n## <a name=\"usage\"></a>Usage\n\n```sh\nirescue --help\n```\n\nThe only required input is a BAM file annotated with cell barcode and UMI sequences as tags (by default, `CB` tag for cell barcode and `UR` tag for UMI; override with `--cb-tag` and `--umi-tag`).\n\nYou can obtain it by aligning your reads using [STARsolo](https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md). It is advised to keep secondary alignments in BAM file, that will be used in the EM procedure to assign multi-mapping reads (e.g. `--outFilterMultimapNmax 100 --winAnchorMultimapNmax 100` or more), and remember to output all the needed SAM attributes (e.g. `--outSAMattributes NH HI AS nM NM MD jM jI XS MC ch cN CR CY UR UY GX GN CB UB sM sS sQ`).\n\nRepeatMasker annotation will be automatically downloaded for the chosen genome assembly (e.g. `-g hg38`), or provide your own annotation in bed format (e.g. `-r TE.bed`).\n\n```sh\nirescue -b genome_alignments.bam -g hg38\n```\n\nIf you already obtained gene-level counts (using STARsolo, Cell Ranger, Alevin, Kallisto or other tools), it is advised to provide the whitelisted cell barcodes list as a text file (`-w barcodes.tsv`). This will significantly improve performance by processing viable cells only.\n\nFor optimal run time, use at least, e.g.: `-p 8`.\n\n### <a name=\"output_files\"></a>Output files\n\nIRescue generates TE counts in a sparse matrix readable by [Seurat](https://github.com/satijalab/seurat) or [Scanpy](https://github.com/scverse/scanpy) into a `counts/` subdirectory. Optional outputs include a description of equivalence classes with UMI deduplication stats `ec_dump.tsv.gz` and a subdirectory of temporary files `tmp/` for debugging purpose. A detailed logging is enabled by `--verbose` and written to standard error.\n\n```\nirescue_out/\n\u251c\u2500\u2500 counts/\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 barcodes.tsv.gz\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 features.tsv.gz\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 matrix.mtx.gz\n\u251c\u2500\u2500 ec_dump.tsv.gz\n\u2514\u2500\u2500 tmp/\n```\n\n### <a name=\"seurat\"></a>Load IRescue data with Seurat\n\nTo integrate TE counts into an existing Seurat object containing gene expression data, they can be added as an additional assay:\n\n```R\n# import TE counts from IRescue output directory\nte.data <- Seurat::Read10X('./IRescue_out/', gene.column = 1, cell.column = 1)\n\n# create Seurat assay from TE counts\nte.assay <- Seurat::CreateAssayObject(te.data)\n\n# subset the assay by the cells already present in the Seurat object (in case it has been filtered)\nte.assay <- subset(te.assay, colnames(te.assay)[which(colnames(te.assay) %in% colnames(seurat_object))])\n\n# add the assay in the Seurat object\nseurat_object[['TE']] <- irescue.assay\n```\n\nThe result will be something like this:\n```\nAn object of class Seurat \n32276 features across 42513 samples within 2 assays \nActive assay: RNA (31078 features, 0 variable features)\n 1 other assay present: TE\n```\n\nFrom here, TE expression can be normalized. To normalize according to gene counts or TE+gene counts, normalize manually or merge the assays. Reductions can be made using TE, gene or TE+gene expression.\n\n## <a name=\"cite\"></a>Cite\n\nPolimeni B, Marasca F, Ranzani V, Bodega B.\n*IRescue: uncertainty-aware quantification of transposable elements expression at single cell level.*\nbioRxiv 2022.09.16.508229; doi: https://doi.org/10.1101/2022.09.16.508229\n",
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