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Updated: Mar 30, 2026

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The impact of package selection and versioning on single-cell RNA-seq analysis.

Joseph M Rich1, Lambda Moses2, Pétur Helgi Einarsson3

  • 1Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; USC-Caltech MD/PhD Program, Keck School of Medicine, Los Angeles, CA 90033, USA.

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Summary

Standard single-cell RNA sequencing analysis using Seurat and Scanpy yields substantially different results. Even software version changes can impact differential expression, highlighting the need for careful tool evaluation and reproducible research.

Keywords:
ScanpySeuratopen source softwaresingle-cell RNA sequencing

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing cellular heterogeneity.
  • Seurat and Scanpy are leading software packages for scRNA-seq data analysis.
  • It is generally assumed that these packages produce comparable results.

Purpose of the Study:

  • To compare the analytical outputs of Seurat and Scanpy across standard scRNA-seq workflow steps.
  • To quantify the differences in results generated by these two widely used bioinformatics tools.
  • To assess the impact of software versions on scRNA-seq analysis outcomes.

Main Methods:

  • Comparative analysis of Seurat and Scanpy algorithms for scRNA-seq data processing.
  • Evaluation of differences in filtering, dimensionality reduction, clustering, and differential expression analysis.
  • Assessment of results variability due to software version updates.

Main Results:

  • Seurat and Scanpy demonstrate substantial differences in outputs across multiple analytical steps.
  • The magnitude of these differences rivals variability from reduced sequencing depth or cell number.
  • Software version changes significantly impact differential expression results.

Conclusions:

  • Users must critically assess bioinformatics tools used in scRNA-seq analysis.
  • Prioritizing transparency and reproducibility in software development is crucial for reliable scRNA-seq research.
  • The choice of scRNA-seq analysis package can significantly influence biological conclusions.