Panpipes: a pipeline for multiomic single-cell data analysis.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.11.532085v1?rss=1

Authors: Rich-Griffin, C., Curion, F., Thomas, T., Agarwal, D., Theis, F. J., Dendrou, C.

Abstract: Single-cell multiomic analysis of the epigenome, transcriptome and proteome allows for comprehensive characterisation of the molecular circuitry that underpins cell identity, cell state, and cell type-specific gene regulatory networks. Technological advances, whereby multiple omics modalities can be simultaneously profiled in individual cells in a highly parallel manner, are already beginning to provide a step-change in our capacity to comprehend complex tissue biology during development and ageing, in health and disease, and upon treatment. However, the holistic interpretation of such datasets still presents a challenge due to an absence of approaches for the systematic joint analysis and evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate the analysis of multimodal single-cell datasets by incorporating widely used Python-based tools to efficiently perform: quality control, preprocessing, integration, clustering, and reference mapping at scale in the multiomic setting. Panpipes combines established and state-of-the-art methods to allow reliable and customisable analysis and evaluation of multiomic single-cell datasets, enabling users to investigate individual and integrated modalities, and to empower decision-making prior to downstream analyses and data interpretation.

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