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:
- Extracting histopathological features
- Deconvolution of bulkRNAseq data
- Building a multi-task cell type model to predict cell type abundances on a tile-level
- Predicting tile-level cell type abundances using the multi-task models
- 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.
- 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.
- Create apptainer/singularity containers from Docker images
# 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
- Download retrained models to extract the histopathological features, available from Fu et al., Nat Cancer, 2020
- Download from (Retrained_Inception_v4)
- Unzip the folder
- 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 'example_workflow_params.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.
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.
SysBioOncology/SPoTLIghT was originally written by Joan Kant, Óscar Lapuente-Santana & Federica Eduati.
If you would like to contribute to this pipeline, please see the contributing guidelines.
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.