The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Open accessAbstract
Motivation: Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets. Results: We present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis. Availability and implementation: The new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/. Supplementary information: Supplementary data are available at Bioinformatics online.
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SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
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Cited by
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,694
“The 1048 pathway enrichment analysis was implemented using the GSEApy package [86].”
MethodProgressive plasticity during colorectal cancer metastasis
Moorman, A., Benitez, E. K., Cambuli, F., Jiang, Q., Mahmoud, A., Lumish, M., Hartner, S., Balkaran, S., Bermeo, J., Asawa, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08150-0 · Cited by 229
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137
Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution
Watson, B. R., Paul, B., Rahman, R. U., Amir-Zilberstein, L., Segerstolpe, Å., Epstein, E. T., Murphy, S., Geistlinger, L., Lee, T., Shih, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55325-4 · Cited by 123
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing
Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wang, Y., Xing, C., Xiong, Y., & Du, H. (2024) · Nature Communications · doi:10.1038/s41467-024-50194-3 · Cited by 102
Metadata
- Authors
- Fang, Z., Liu, X., & Peltz, G.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-11-25
- Volume
- 39
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W4309944312
- Cited by
- 1,170
- References
- 6
- FWCI
- 52.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- National Institute of Health
- National Institute for Drug Addiction (5U01DA04439902)
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