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SPoTLIghT, a computational framework to extract interpretable features of the spatial distribution of multiple cell types by combining unannotated pathology images with bulk transcriptomics.

https://github.com/sysbiooncology/spotlight

Science Score: 67.0%

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Repository

SPoTLIghT, a computational framework to extract interpretable features of the spatial distribution of multiple cell types by combining unannotated pathology images with bulk transcriptomics.

Basic Info
  • Host: GitHub
  • Owner: SysBioOncology
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 8.68 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 3 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

SysBioOncology/SPoTLIghT

Nextflowrun with singularity

Introduction

Our pipeline, SPoTLIghT, as presented in our paper, can be used to derive spatial graph-based interpretable features from H&E slides and is available as a Nextflow pipeline.

The pipeline comprises the following modules:

  1. Extracting histopathological features
  2. Deconvolution of bulkRNAseq data
  3. Building a multi-task cell type model to predict cell type abundances on a tile-level
  4. Predicting tile-level cell type abundances using the multi-task models
  5. Compute spatial features using the tile-level cell type abundances

The training of the cell type models have been perfomed using fresh frozen (FF) slides for the TCGA-SKCM dataset (melanoma) as described in the paper. The trained models are provided here.

See also the figures below.

Workflow part 1 Workflow part 2

Usage

Software

  • Docker version 28.0.4, build b8034c0
  • Apptainer version 1.0.
  • Nextflow version 24.10.5 build 5935

These were the versions used for testing the pipeline.

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow.

  1. Create apptainer/singularity containers from Docker images

```sh

Easiest route (internet access needed)

apptainer build spotlight.sif docker://joank23/spotlight apptainer build immunedeconvr.sif docker://joank23/immunedeconvr

Alternative route

Usecase: if working on an HPC that does not have docker & internet access for building the image

A) on you local desktop

1. save docker as tar or tar.gz (compressed)

docker pull joank23/spotlight docker pull joank23/immunedeconvr docker save joank23/spotlight > spotlight.tar docker save joank23/immunedeconvr > immunedeconvr.tar

2. Move to HPC (optionally)

3. Build apptainer images (.sif) from docker (.tar)

apptainer build spotlight.sif docker-archive:spotlight.tar apptainer build immunedeconvr.sif docker-archive:immunedeconvr.tar

```

  1. Download retrained models to extract the histopathological features, available from Fu et al., Nat Cancer, 2020
    1. Download from (RetrainedInceptionv4)
    2. Unzip the folder
    3. Extract the files to a folder called Retrained_Inception_v4.

IMPORTANT: Please rename your images file names, so they only include "-", to follow the same sample coding used by the TCGA.

Now, you can run the pipeline using:

Since the SKCM multi-task models are provided, the 'exampleworkflowparams.yml' can be used to predict the cell type abundances for other H&E images and optionally to compute the spatial features.

For more information see examples.

bash nextflow run SysBioOncology/SPoTLIghT \ -profile <docker/singularity/.../institute> \ -params-file assets/example_workflow_params.yml \ --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.

Credits

SysBioOncology/SPoTLIghT was originally written by Joan Kant, Óscar Lapuente-Santana & Federica Eduati.

Contributions and Support

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

Citations

Lapuente-Santana, Ó., Kant, J. & Eduati, F. Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study. npj Precis. Onc. 8, 254 (2024). https://doi.org/10.1038/s41698-024-00749-w

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

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

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: SysBioOncology
  • Login: SysBioOncology
  • Kind: organization

Citation (CITATIONS.md) 

# SysBioOncology/SPoTLIghT: 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

- [PC-CHiP](https://github.com/gerstung-lab/PC-CHiP)

    > Fu, Y., Jung, A.W., Torne, R.V. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 1, 800–810 (2020). https://doi.org/10.1038/s43018-020-0085-8

- [immunedeconv](https://github.com/omnideconv/immunedeconv)

    > Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., ..., List, M., Aneichyk, T. (2019). Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics, 35(14), i436-i445. https://doi.org/10.1093/bioinformatics/btz363 

- [EPIC](https://gfellerlab.shinyapps.io/EPIC_1-1/)
  
  > Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. ELife, 6, e26476. https://doi.org/10.7554/eLife.26476

- [quanTIseq](http://icbi.at/software/quantiseq/doc/index.html)

    > Finotello, F., Mayer, C., Plattner, C. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med 11, 34 (2019). https://doi.org/10.1186/s13073-019-0638-6

- [MCPCounter](https://github.com/ebecht/MCPcounter)

> Becht, E., Giraldo, N.A., Lacroix, L. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17, 218 (2016). https://doi.org/10.1186/s13059-016-1070-5

- [xCell](http://xcell.ucsf.edu/)

> Aran, D., Hu, Z., & Butte, A. J. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology, 18(1), 220. https://doi.org/10.1186/s13059-017-1349-1

## 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.

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Dependencies

subworkflows/nf-core/utils_nextflow_pipeline/meta.yml cpan
subworkflows/nf-core/utils_nfcore_pipeline/meta.yml cpan
subworkflows/nf-core/utils_nfschema_plugin/meta.yml cpan
env/immunedeconvr/Dockerfile docker
  • base latest build
  • mambaorg/micromamba 1-bookworm-slim build
env/spotlight/Dockerfile docker
  • python 3.8.10-slim build
  • ubuntu 20.04 build
env/spotlight/requirements.txt pypi
  • Pillow ==9.4.0
  • dask ==2022.12.1
  • joblib ==1.2.0
  • matplotlib ==3.6.2
  • networkx ==3.0
  • numpy ==1.24.1
  • opencv-python ==4.6.0.66
  • openpyxl *
  • pandas ==1.5.2
  • pyarrow ==10.0.1
  • scikit-learn ==1.2.0
  • scipy ==1.10.0
  • seaborn ==0.12.2
  • six ==1.16.0
  • tensorflow ==2.11.0
  • tf_slim ==1.1.0
  • tiffslide ==2.4.0
  • tornado ==6.2