Pesci: fast and user-friendly software to compare single-cell gene expression across species

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ID: 325507
2026
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Abstract
SUMMARY: Recent technological advances have propelled comparative functional genomics into the single-cell era, spurring a rapid development of methods to analyse these complex datasets. However, comparing single-cell gene expression across species to quantify expression similarity and ultimately identify homologous cell types remains an open problem. The ICC algorithm (Iterative Correlation of Coexpression) has been recently proposed as an attractive approach to tackle this challenge, but, to date, no software implementation is available. Here, we introduce Pesci (Pretty Easy Single-cell Comparisons using ICC), an efficient and user-friendly implementation of the ICC algorithm applied to pairwise comparisons of single-cell gene expression atlases across species. AVAILABILITY: Pesci is implemented in Python 3 (≥3.7). It is available for download on Linux, macOS and Windows via pip, conda and GitHub at https://github.com/eparey/pesci. The source code is permanently archived on Zenodo (https://doi.org/10.5281/zenodo.21477543). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7203736739 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Elise Parey, Laura Piovani, Ferdinand Marlétaz
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag623
URL
https://doi.org/10.1093/bioinformatics/btag623
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