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Related Experiment Video

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scEvoNet: a gradient boosting-based method for prediction of cell state evolution.

Aleksandr Kotov1,2, Andrei Zinovyev2,3,4, Anne-Helene Monsoro-Burq5,6,7

  • 1Faculté Des Sciences d'Orsay, Université Paris Saclay, Orsay, France.

BMC Bioinformatics
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

scEvoNet is a new Python tool that analyzes single-cell RNA sequencing data to predict cell type evolution. It identifies genes involved in evolutionary divergence and co-option, aiding the study of cell state dynamics.

Keywords:
CancerCell statesCell typesDifferentiationEvolutionGene programsGradient boostingscRNA-seq

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) reveals cell states but lacks tools for evolutionary analysis.
  • Understanding cell state evolution requires tracking molecular profile changes, like gene activation or co-option.
  • Existing methods primarily focus on marker genes, not the dynamic evolution of cell states.

Purpose of the Study:

  • To introduce scEvoNet, a Python tool for predicting cell type evolution from scRNA-seq data.
  • To enable the identification of genes driving evolutionary divergence and co-option across species or in cancer.
  • To provide a computational framework for analyzing cell state dynamics and similarities.

Main Methods:

  • Development of scEvoNet, a Python package for scRNA-seq data analysis.
  • Construction of cell state confusion matrices and bipartite gene-cell state networks.
  • Identification of shared genes between cell states in cross-species or cancer datasets.

Main Results:

  • scEvoNet successfully predicts cell type evolution in developmental and cancer datasets.
  • The tool identifies genes indicative of evolutionary divergence and gene co-option.
  • It facilitates the measurement of cell state similarities and the screening of relevant genes.

Conclusions:

  • scEvoNet is a valuable tool for exploring cell state dynamics and evolutionary trajectories.
  • The package is freely available, promoting further research in evolutionary and cancer genomics.
  • Analyzing transcriptomic continua aids in understanding cell state changes during development and evolution.