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Updated: Aug 21, 2026
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
Pesci: fast and user-friendly software to compare single-cell gene expression across species
Elise Parey1, Laura Piovani1, Ferdinand Marlétaz1
1Centre for Life's Origins and Evolution (CLOE), Department of Genetics, Evolution & Environment, University College London, Gower Street, London WC1E 6BT, UK.
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.
