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Related Experiment Video

Updated: Jun 28, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Joseph M Rich^1,2, Lambda Moses¹, Pétur Helgi Einarsson³

¹Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Biorxiv : the Preprint Server for Biology

|April 15, 2024

Summary

Standard single-cell RNA sequencing (scRNA-seq) analysis workflows using Seurat and Scanpy yield significantly different results due to underlying algorithmic variations. Users must carefully assess these tools for reproducible scRNA-seq data analysis.

Keywords:

Scanpy Seurat open source software single-cell RNA-seq

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Last Updated: Jun 28, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for analyzing cellular heterogeneity.
Standard scRNA-seq workflows involve data processing, normalization, dimensionality reduction, clustering, and differential expression analysis.
Seurat and Scanpy are the dominant software packages for scRNA-seq analysis, often assumed to produce comparable results.

Conclusions:

The choice of scRNA-seq analysis package (Seurat vs. Scanpy) can substantially impact biological interpretations.
There is a critical need for enhanced transparency, consistency, and reproducibility in bioinformatics software development.
Users should critically evaluate the tools used in their scRNA-seq analyses to ensure robust findings.