- "description": "<h1>\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-lsmquant_logo_dark.png\">\n <img alt=\"nf-core/lsmquant\" src=\"docs/images/nf-core-lsmquant_logo_light.png\">\n </picture>\n</h1>\n\n[](https://github.com/nf-core/lsmquant/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/lsmquant/actions/workflows/linting.yml)[](https://nf-co.re/lsmquant/results)[](https://doi.org/10.5281/zenodo.XXXXXXX)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.3.2)\n[](https://docs.conda.io/en/latest/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/lsmquant)\n\n[](https://nfcore.slack.com/channels/lsmquant)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n\n## Introduction\n\n**nf-core/lsmquant** is a bioinformatics pipeline that performs preprocessing and analysis of light-sheet microscopy images of tissue cleared samples. The pipeline takes 2D single-channel 16-bit `.tif` images as input. The preprocessing consists of intesity adjustment, channel alignment, and tile stitching to reconstruct the 3D image. For mousebrain samples it offers a registration to the Allen Mouse Brain Reference Atlas for precise region annotation. Cell nuclei quantification is perfomed on the nuclear channel by a 3D-Unet.\n\n<div style=\"text-align: center;\">\n<img src=\"docs/images/lsmquant-metromap.svg\" alt=\"lasmquant metromap\">\n</div>\n\n## Basic workflow\n\n**Preprocessing**\n\n1. Intensity Adjustment\n2. Channel Alignment\n3. Iterative Stitching\n\n**ARA Registration**\n\n4. ARA Registration subworkflow (optional)\n5. Cell Nuclei Quantification\n\n**Full**\n\n1. Preprocessing\n2. Nuclei quantification\n\n## Pipeline Summary\n\nThe pipeline consists of two major workflows `preprocessing` and the `full` workflow. The `ara-regsitration` is an optional subworkflow that works only for whole mouse brain samples.\n\n### Preprocessing\n\nPreprocessing is performed on raw 2D single-channel 16-bit `.tif` images produced by a light sheet microscope. Three individual steps are performed:\n\n- **Intensity adjustments** to correct for the Gaussian shape of the lightsheet and intensity differences between adjacent tiles\n- **Image channel alignment** using a 2D rigid approach or a nonlinear 3D approach using Elastix.\n- **Image tile stitching** via an iterative 2D stitching approach by calculating z displacements and xy translations using phase correlation and SIFT.\n\n### Full\n\nQuantification of cell-nuclei is performed using a 3D-Unet. It is performed on the nuclear channel only, assuming that the corresponding image file names contain the pattern `C1`.\n\n### ARA Registration\n\nOptional registration to the Allen Reference Atlas (ARA) for functional brain region annotation can be perfomed before segmentation.\nThis includes the following two steps:\n\n- Downsampling of the high resolution stitched images\n- Registration to the ARA\n\n## 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 run the pipeline you need to provide a samplesheet with your data in the following structure:\n\n`samplesheet.csv`\n\n```csv\nsample_id,img_directory,parameter_file\nTEST1,path/to/image-files,path/to/parameter/file.csv\n```\n\nThe parameter csv file includes sample specific parameters that are used for processing the given data. It needs to follow a specific structure.\n\nPlease get the basic template file [here](../assets/params_template_lsmquant.csv).\n`parametersheet.csv`\n\nNow, you can run the pipeline using:\n\n<!-- TODO nf-core: update the following command to include all required parameters for a minimal example -->\n\n```bash\nnextflow run nf-core/lsmquant \\\n -profile <docker/singularity/.../institute> \\\n --input <samplesheet.csv> \\\n --outdir <OUTDIR> \\\n --stage <stage>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/lsmquant/usage) and the [parameter documentation](https://nf-co.re/lsmquant/parameters).\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/lsmquant/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/lsmquant/output).\n\n## Credits\n\nnf-core/lsmquant was originally written by Carolin Schwitalla.\n\nThe pipeline is mainly based on the NuMorph (Nuclear-Based Morphometry) toolbox developed by Krupa et al., 2021.\n\n> **NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images**\n>\n> Krupa O, Fragola G, Hadden-Ford E, Mory JT, Liu T, Humphrey Z, Rees BW, Krishnamurthy A, Snider WD, Zylka MJ, Wu G, Xing L, Stein JL.\n>\n> Cell Rep. 2021 Oct 12, doi: [10.1016/j.celrep.2021.109802](https://doi.org/10.1016%2Fj.celrep.2021.109802)\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n<!-- TODO nf-core: If applicable, make list of people who have also contributed -->\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 `#lsmquant` channel](https://nfcore.slack.com/channels/lsmquant) (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/lsmquant for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\n<!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->\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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