Dear ResearchGate Community,
I am currently engaged in single-cell analysis for my research project and would greatly appreciate your insights and experiences regarding the use of Seurat and ScanPy.
I have been exploring both Seurat and ScanPy as tools for analyzing single-cell RNA sequencing (scRNA-seq) data. However, I would like to gather more information about these packages directly from researchers who have bioinformatic hands-on experience with them.
Specifically, I would be grateful if you could share your thoughts on the following:
1. Which package (Seurat or ScanPy) have you used for scRNA-seq analysis, and what were your primary reasons for choosing it? Is it depending on familiarity with programming languages (R for Seurat and Python for Scanpy)?
2. What are the notable features, strengths, or advantages of the packages you have worked with?
3. Were there any challenges or limitations you encountered while using the packages, and how did you address them?
4. Have you encountered any specific use cases or applications where one platform outperformed the other?
5. Are there any particular resources, tutorials, or best practices you found helpful when working with Seurat or ScanPy?
Your firsthand experiences and insights would be immensely valuable in helping me make an informed decision about which package to choose and understanding potential considerations for my single-cell analysis workflows.
Thank you in advance for taking the time to share your expertise. I look forward to hearing from you and benefiting from your valuable insights.
Best regards,
Emil Lagumdzic Institute of Immunology Department of Pathobiology
University of Veterinary Medicine Vienna
Both Seurat and Scanpy are widely used packages for scRNA-seq analysis, but their usage may vary depending on the research community and individual preferences. Here are some general observations:
While familiarity with programming languages can be a factor in choosing between Seurat and Scanpy, it is not the sole determining factor. Other considerations may include the specific functionalities, performance, available resources and tutorials, compatibility with other analysis tools, and the research goals of the project.
Maybe the main difference between Seurat and Scanpy lie in the methods used for marker gene selection and differentially expressed genes analysis, since they use different formulas to calculate fold-change values, based on the raw count and mean-log values, for Seurat and Scanpy, respectively.
Highest care should be taken when analyzing genes with zero expression since Seurat and Scanpy produce spurious results. A more deep and informative comparison can be found in this preprint:
Preprint A comparison of marker gene selection methods for single-cel...
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