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| { | ||
| "@id": "./", | ||
| "@type": "Dataset", | ||
| "creativeWorkStatus": "InProgress", | ||
| "datePublished": "2025-01-27T14:46:25+00:00", | ||
| "description": "<h1>\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-molkart_logo_dark.png\">\n <img alt=\"nf-core/molkart\" src=\"docs/images/nf-core-molkart_logo_light.png\">\n </picture>\n</h1>\n\n[](https://github.com/nf-core/molkart/actions/workflows/ci.yml)\n[](https://github.com/nf-core/molkart/actions/workflows/linting.yml)[](https://nf-co.re/molkart/results)[](https://doi.org/10.5281/zenodo.XXXXXXX)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\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/molkart)\n\n[](https://nfcore.slack.com/channels/molkart)[](https://twitter.com/nf_core)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/molkart** is a bioinformatics pipeline that ...\n\n<!-- TODO nf-core:\n Complete this sentence with a 2-3 sentence summary of what types of data the pipeline ingests, a brief overview of the\n major pipeline sections and the types of output it produces. You're giving an overview to someone new\n to nf-core here, in 15-20 seconds. For an example, see https://github.com/nf-core/rnaseq/blob/master/README.md#introduction\n-->\n\n<!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core\n workflows use the \"tube map\" design for that. See https://nf-co.re/docs/contributing/design_guidelines#examples for examples. -->\n<!-- TODO nf-core: Fill in short bullet-pointed list of the default steps in the pipeline -->1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))\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\n<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.\n Explain what rows and columns represent. For instance (please edit as appropriate):\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nsample,fastq_1,fastq_2\nCONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz\n```\n\nEach row represents a fastq file (single-end) or a pair of fastq files (paired end).\n\n-->\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/molkart \\\n -profile <docker/singularity/.../institute> \\\n --input samplesheet.csv \\\n --outdir <OUTDIR>\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/molkart/usage) and the [parameter documentation](https://nf-co.re/molkart/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/molkart/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/molkart/output).\n\n## Credits\n\nnf-core/molkart was originally written by @kbestak, @FloWuenne.\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 `#molkart` channel](https://nfcore.slack.com/channels/molkart) (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/molkart 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", | ||
| "creativeWorkStatus": "Stable", | ||
| "datePublished": "2025-02-28T15:48:22+00:00", | ||
| "description": "<h1>\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-molkart_logo_dark.png\">\n <img alt=\"nf-core/molkart\" src=\"docs/images/nf-core-molkart_logo_light.png\">\n </picture>\n</h1>\n\n[](https://github.com/nf-core/molkart/actions/workflows/ci.yml)\n[](https://github.com/nf-core/molkart/actions/workflows/linting.yml)[](https://nf-co.re/molkart/results)[](https://doi.org/10.5281/zenodo.10650748)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/molkart)\n\n[](https://nfcore.slack.com/channels/molkart)[](https://twitter.com/nf_core)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/molkart** is a pipeline for processing Molecular Cartography data from Resolve Bioscience (combinatorial FISH). It takes as input a table of FISH spot positions (x,y,z,gene), a corresponding DAPI image (`TIFF` format) and optionally an additional staining image in the `TIFF` format. nf-core/molkart performs end-to-end processing of the data including image processing, QC filtering of spots, cell segmentation, spot-to-cell assignment and reports quality metrics such as the spot assignment rate, average spots per cell and segmentation mask size ranges.\n\n<p align=\"center\">\n <img title=\"Molkart Workflow\" src=\"docs/images/molkart_workflow.png\" width=100%>\n</p>\n\nImage preprocessing\n\n- Fill the grid pattern in provided images ([`Mindagap`](https://github.com/ViriatoII/MindaGap))\n- Optionally apply contrast-limited adaptive histogram equalization\n- If a second (membrane) image is present, combine images into a multichannel stack (if required for segmentation)\n\nCell segmentation\n\n- Apply cell segmentation based on provided images, available options are: - [`Cellpose`](https://www.cellpose.org/) - [`Mesmer`](https://deepcell.readthedocs.io/en/master/API/deepcell.applications.html#mesmer) - [`ilastik`](https://www.ilastik.org/) - [`Stardist`](https://github.com/stardist/stardist)\n- Filter cells based on cell size to remove artifacts\n\nSpot processing\n\n- Find duplicated spots near grid lines ([`Mindagap`](https://github.com/ViriatoII/MindaGap))\n- Assign spots to segmented cells\n\nQuality control\n\n- Create quality-control metrics specific to this pipeline\n- provide them to ([`MultiQC`](http://multiqc.info/)) to create a report\n\n## Usage\n\n:::note\nIf you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how\nto set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline)\nwith `-profile test` before running the workflow on actual data.\n:::\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nsample,nuclear_image,spot_locations,membrane_image\nsample0,sample0_DAPI.tiff,sample0_spots.txt,sample0_WGA.tiff\n```\n\nEach row represents an FOV (field-of-view). Columns represent the sample ID (all must be unique), the path to the respective nuclear image, the spot table, and optionally the path to the respective membrane image (or any additional image to improve segmentation).\n\nNow, you can run the pipeline using all default values with:\n\n```bash\nnextflow run nf-core/molkart \\\n -profile <docker/singularity/.../institute> \\\n --input samplesheet.csv \\\n --outdir <OUTDIR>\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/molkart/usage) and the [parameter documentation](https://nf-co.re/molkart/parameters).\n\n## Pipeline output\n\nThe pipeline outputs a matched cell-by-transcript table based on deduplicated spots and segmented cells, as well as preprocessing and segmentation intermediaries.\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/molkart/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/molkart/output).\n\n## Credits\n\nnf-core/molkart was originally written by @kbestak, @FloWuenne.\n\nWe thank [Maxime U Garcia](https://github.com/maxulysse) for his assistance and support in the development of this pipeline.\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 `#molkart` channel](https://nfcore.slack.com/channels/molkart) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nIf you use nf-core/molkart for your analysis, please cite it using the following doi: [10.5281/zenodo.10650749](https://doi.org/10.5281/zenodo.10650749)\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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