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molkart

A pipeline for processing Molecular Cartography data from Resolve Bioscience (combinatorial FISH)

https://github.com/nf-core/molkart

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 10 DOI reference(s) in README
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.5%) to scientific vocabulary

Keywords

fish image-processing imaging molecularcartography nextflow nf-core pipeline segmentation single-cell spatial transcriptomics workflow

Keywords from Contributors

pipelines workflows
Last synced: 6 months ago · JSON representation ·

Repository

A pipeline for processing Molecular Cartography data from Resolve Bioscience (combinatorial FISH)

Basic Info
  • Host: GitHub
  • Owner: nf-core
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://nf-co.re/molkart
  • Size: 6.57 MB
Statistics
  • Stars: 12
  • Watchers: 167
  • Forks: 12
  • Open Issues: 11
  • Releases: 2
Topics
fish image-processing imaging molecularcartography nextflow nf-core pipeline segmentation single-cell spatial transcriptomics workflow
Created over 2 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/molkart

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

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Introduction

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.

Image preprocessing

  • Fill the grid pattern in provided images (Mindagap)
  • Optionally apply contrast-limited adaptive histogram equalization
  • If a second (membrane) image is present, combine images into a multichannel stack (if required for segmentation)

Cell segmentation

  • Apply cell segmentation based on provided images, available options are: - Cellpose - Mesmer - ilastik - Stardist
  • Filter cells based on cell size to remove artifacts

Spot processing

  • Find duplicated spots near grid lines (Mindagap)
  • Assign spots to segmented cells

Quality control

  • Create quality-control metrics specific to this pipeline
  • provide them to (MultiQC) to create a report

Usage

:::note If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data. :::

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,nuclear_image,spot_locations,membrane_image sample0,sample0_DAPI.tiff,sample0_spots.txt,sample0_WGA.tiff

Each 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).

Now, you can run the pipeline using all default values with:

bash nextflow run nf-core/molkart \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

[!WARNING] 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.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

The pipeline outputs a matched cell-by-transcript table based on deduplicated spots and segmented cells, as well as preprocessing and segmentation intermediaries. To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/molkart was originally written by @kbestak, @FloWuenne.

We thank Maxime U Garcia for his assistance and support in the development of this pipeline.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #molkart channel (you can join with this invite).

Citations

If you use nf-core/molkart for your analysis, please cite it using the following doi: 10.5281/zenodo.10650749

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Owner

  • Name: nf-core
  • Login: nf-core
  • Kind: organization
  • Email: core@nf-co.re

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md) 

# nf-core/molkart: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [anndata](https://anndata.readthedocs.io/en/latest/)

  > Isaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, F. Alexander Wolf anndata: Annotated data > bioRxiv 2021.12.16.473007; doi: https://doi.org/10.1101/2021.12.16.473007

- [Cellpose](https://www.cellpose.org/)

  > Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). https://doi.org/10.1038/s41592-020-01018-x
  > Pachitariu, M., Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022). https://doi.org/10.1038/s41592-022-01663-4

- [ilastik](https://www.ilastik.org/)

  > Berg, S., Kutra, D., Kroeger, T. et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods 16, 1226–1232 (2019). https://doi.org/10.1038/s41592-019-0582-9

- [Mesmer](https://deepcell.readthedocs.io/en/master/API/deepcell.applications.html)

  > Greenwald NF, Miller G, Moen E, Kong A, Kagel A, Dougherty T, Fullaway CC, McIntosh BJ, Leow KX, Schwartz MS, Pavelchek C, Cui S, Camplisson I, Bar-Tal O, Singh J, Fong M, Chaudhry G, Abraham Z, Moseley J, Warshawsky S, Soon E, Greenbaum S, Risom T, Hollmann T, Bendall SC, Keren L, Graf W, Angelo M, Van Valen D. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol. 2022 Apr;40(4):555-565. doi: 10.1038/s41587-021-01094-0. Epub 2021 Nov 18. PMID: 34795433; PMCID: PMC9010346.

- [Mindagap](https://github.com/ViriatoII/MindaGap)

  > Ricardo Guerreiro, Florian Wuennemann, & pvtodorov. (2023). ViriatoII/MindaGap: v0.0.3 (0.0.3). Zenodo. https://doi.org/10.5281/zenodo.8120559

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

  > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [Stardist](https://github.com/stardist/stardist)

  > Schmidt, U., Weigert, M., Broaddus, C., Myers, G. (2018). Cell Detection with Star-Convex Polygons. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11071. Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_30

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total
  • Create event: 10
  • Release event: 1
  • Issues event: 16
  • Watch event: 2
  • Delete event: 2
  • Issue comment event: 45
  • Push event: 29
  • Pull request review comment event: 29
  • Pull request review event: 55
  • Pull request event: 52
  • Fork event: 3
Last Year
  • Create event: 10
  • Release event: 1
  • Issues event: 16
  • Watch event: 2
  • Delete event: 2
  • Issue comment event: 45
  • Push event: 29
  • Pull request review comment event: 29
  • Pull request review event: 55
  • Pull request event: 52
  • Fork event: 3

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 150
  • Total Committers: 4
  • Avg Commits per committer: 37.5
  • Development Distribution Score (DDS): 0.433
Past Year
  • Commits: 150
  • Committers: 4
  • Avg Commits per committer: 37.5
  • Development Distribution Score (DDS): 0.433
Top Committers
Name Email Commits
Krešimir Beštak k****k@g****m 85
Florian Wuennemann f****e@g****m 58
nf-core-bot c****e@n****e 5
Krešimir Beštak 8****k 2
Committer Domains (Top 20 + Academic)
nf-co.re: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 32
  • Total pull requests: 56
  • Average time to close issues: 2 months
  • Average time to close pull requests: 8 days
  • Total issue authors: 3
  • Total pull request authors: 5
  • Average comments per issue: 1.03
  • Average comments per pull request: 1.54
  • Merged pull requests: 44
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 25
  • Average time to close issues: 3 months
  • Average time to close pull requests: 16 days
  • Issue authors: 3
  • Pull request authors: 5
  • Average comments per issue: 0.17
  • Average comments per pull request: 1.4
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kbestak (34)
  • FloWuenne (7)
  • giacuong171 (2)
  • jen-reeve (1)
Pull Request Authors
  • kbestak (37)
  • FloWuenne (20)
  • nf-core-bot (19)
  • felixS27 (1)
  • miri-2000 (1)
Top Labels
Issue Labels
enhancement (21) bug (7) documentation (3) WIP (1) feature-request (1) help wanted (1) imaging (1) ready-for-review (1)
Pull Request Labels
enhancement (3) ready-for-review (1)