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bioRxiv · 10.1101/2024.03.30.587427

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

Martini, L.·Bardini, R.·Di Carlo, S.
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Abstract

Multi-omics single-cell data represent an excellent opportunity to investigate biological complexity in general and generate new insights into the biological complexity of heterogeneous multicellular populations. Considering one omics pool at a time captures partial cellular states, while combining data from different omics collections allows for a better reconstruction of the intricacies of cell regulations at a particular time. However, multi-omics data provide only an opportunity. Computational approaches can leverage such opportunities, given that they raise the challenge of consistent data integration and multi-omics analysis. This work showcases a bioinformatic workflow combining existing methods and packages to analyze transcriptomic and epigenomic single-cell data separately and jointly, generating a new, more complete understanding of cellular heterogeneity.

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Martini, L., Bardini, R., Di Carlo, S.. 2024-03-30. A starting guide on multi-omic single-cell data joint analysis: basic practices and results. https://doi.org/10.1101/2024.03.30.587427

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