Panpipes is a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customisable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
See our documentation and our preprint
These workflows make use of cgat-core
Available workflows:
- "ingest" : Ingest data and compute quality control metrics
- "preprocess" : Filter and normalize per modality
- "integration" : Integrate and batch correct using single and multimodal methods
- "clustering" : Cluster cells per modality
- "refmap" : Map queries against reference datasets
- "vis" : Visualize metrics from other pipelines in the context of experiment metadata
- "qc_spatial" : Ingest spatial transcriptomics data (Vizgen, Visium) and compute quality control metrics
- "preprocess_spatial" : Filtering and normalize spatial transcriptomics data
- "deconvolution_spatial" : Deconvolve cell types of spatial transcriptomics slides
See installation instructions here
We recommend installing panpipes in a conda environment, we provide a minimal conda config file in pipeline_env.yaml
conda env create --file=pipeline_env.yml
In this environment, you can install nightly version of panpipes, i.e. cloning this repo and installing it from main.
git clone https://github.com/DendrouLab/panpipes.git
cd panpipes
pip install -e .
Oxford BMRC Rescomp users find additional advice in docs/installation_rescomp
Since panpipes v0.4.0
,the ingest
workflow expects different headers for the RNA and Protein modalities from the previous releases.
Check the example submission file and the documentation for more detailed instructions.
Created and Maintained by Charlotte Rich-Griffin and Fabiola Curion. Additional contributors: Sarah Ouologuem, Devika Agarwal, Lilly May, Kevin Rue-Albrecht, Giulia Garcia, Lukas Heumos.