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            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-stableexpression_logo_dark.png\">\n    <img alt=\"nf-core/stableexpression\" src=\"docs/images/nf-core-stableexpression_logo_light.png\">\n  </picture>\n</h1>\n\n[![Open in GitHub Codespaces](https://img.shields.io/badge/Open_In_GitHub_Codespaces-black?labelColor=grey&logo=github)](https://github.com/codespaces/new/nf-core/stableexpression)\n[![GitHub Actions CI Status](https://github.com/nf-core/stableexpression/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/stableexpression/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/stableexpression/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/stableexpression/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/stableexpression/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.04.0-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-3.5.1-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[![run with apptainer](https://custom-icon-badges.demolab.com/badge/run%20with-apptainer-4545?logo=apptainer&color=teal&labelColor=000000)](https://apptainer.org/)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/stableexpression)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23stableexpression-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/stableexpression)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/stableexpression** is a bioinformatics pipeline aiming to aggregate multiple count datasets for a specific species and find the most stable genes. The datasets can be either downloaded from public databases (EBI, NCBI) or provided directly by the user. Both RNA-seq and Microarray count datasets can be utilised.\n\n<p align=\"center\">\n    <img title=\"Stableexpression Workflow\" src=\"docs/images/nf_core_stableexpression.metromap.png\" width=100%>\n</p>\n\nIt takes as main inputs :\n\n- a species name (mandatory)\n- keywords for Expression Atlas / GEO search (optional)\n- a CSV input file listing your own raw / normalised count datasets (optional).\n\n**Use cases**:\n\n- **find the most suitable genes as RT-qPCR reference genes for a specific species (and optionally specific conditions)**\n- download all Expression Atlas and / or NCBI GEO datasets for a species (and optionally keywords)\n\n## Pipeline overview\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps:\n\n#### 1. Get accessions from public databases\n\n- Get [Expression Atlas](https://www.ebi.ac.uk/gxa/home) dataset accessions corresponding to the provided species (and optionally keywords)\n  This step is run by default but is optional. Set `--skip_fetch_eatlas_accessions` to skip it.\n- Get NBCI [GEO](https://www.ncbi.nlm.nih.gov/gds) **microarray** dataset accessions corresponding to the provided species (and optionally keywords)\n  This is optional and **NOT** run by default. Set `--fetch_geo_accessions` to run it.\n\n#### 2. Download data (see [usage](./conf/usage.md#3-provide-your-own-accessions))\n\n- Download [Expression Atlas](https://www.ebi.ac.uk/gxa/home) data if any\n- Download NBCI [GEO](https://www.ncbi.nlm.nih.gov/gds) data if any\n\n> [!NOTE]\n> At this point, datasets downloaded from public databases are merged with datasets provided by the user using the `--datasets` parameter. See [usage](./conf/usage.md#4-use-your-own-expression-datasets) for more information about local datasets.\n\n#### 3. ID Mapping (see [usage](./conf/usage.md#5-custom-gene-id-mapping--metadata))\n\n- Gene IDs are cleaned\n- Map gene IDS to NCBI Entrez Gene IDS (or Ensembl IDs) for standardisation among datasets using [g:Profiler](https://biit.cs.ut.ee/gprofiler/gost) (run by default; optional)\n- Rare genes are filtered out\n\n#### 4. Sample filtering\n\nSamples that show too high ratios of zeros or missing values are removed from the analysis.\n\n#### 5. Normalisation of expression\n\n- Normalize RNAseq raw data using TPM (necessitates downloading the corresponding genome and computing transcript lengths) or CPM.\n- Perform quantile normalisation on each dataset separately using [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.quantile_transform.html)\n\n#### 6. Merge all data\n\nAll datasets are merged into one single dataframe.\n\n#### 7. Imputation of missing values\n\nMissing values are replaced by imputed values using a specific algorithm provided by [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.quantile_transform.html). The user can choose the method of imputation with the `--missing_value_imputer` parameter.\n\n#### 8. General statistics for each gene\n\nBase statistics are computed for each gene, platform-wide and for each platform (RNAseq and microarray).\n\n#### 9. Scoring\n\n- The whole list of genes is divided in multiple sections, based on their expression level.\n- Based on the coefficient of variation, a shortlist of candidates genes is extracted for each section.\n- Run optimised, scalable version of [Normfinder](https://www.moma.dk/software/normfinder)\n- Run optimised, scalable version of [Genorm](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-7-research0034) (run by default; optional)\n- Compute stability scores for each candidate gene\n\n#### 10. Reporting\n\n- Result aggregation\n- Make [`MultiQC`](http://multiqc.info/) report\n- Prepare [Dash Plotly](https://dash.plotly.com/) app for further investigation of gene / sample counts\n\n## Test pipeline\n\nYou can test the execution of the pipeline locally with:\n\n```bash\nnextflow run nf-core/stableexpression -profile test,<docker/apptainer/conda/micromamba/...>\n```\n\n## Basic usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nTo search the most stable genes in a species considering all public datasets, simply run:\n\n```bash\nnextflow run nf-core/stableexpression \\\n   -profile <PROFILE (examples: docker / apptainer / conda / micromamba)> \\\n   --species <SPECIES (examples: arabidopsis_thaliana / \"drosophila melanogaster\")> \\\n   --outdir <OUTDIR (example: ./results)> \\\n   -resume\n```\n\n## More advanced usage\n\nFor more specific scenarios, like:\n\n- **fetching only specific conditions**\n- **using your own expression dataset(s)**\n\nplease refer to the [usage documentation](https://nf-co.re/stableexpression/usage).\n\n## Resource allocation\n\nFor setting pipeline CPU / memory usage, see [here](./docs/configuration.md).\n\n## Profiles\n\nSee [here](https://nf-co.re/stableexpression/usage#profiles) for more information about profiles.\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/stableexpression/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/stableexpression/output).\n\n## Support us\n\nIf you like nf-core/stableexpression, please make sure you give it a star on GitHub!\n\n[![stars - stableexpression](https://img.shields.io/github/stars/nf-core/stableexpression?style=social)](https://github.com/nf-core/stableexpression)\n\n## Credits\n\nnf-core/stableexpression was originally written by Olivier Coen.\n\nWe thank the following people for their assistance in the development of this pipeline:\n\n- R\u00e9my Costa\n- Shaheen Acheche\n- Janine Soares\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#stableexpression` channel](https://nfcore.slack.com/channels/stableexpression) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\n<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->\n<!-- If you use nf-core/stableexpression for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
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