The comparative analysis of single-cell RNA sequencing (scRNA-seq) datasets across various biological conditions, technology platforms and tissue types reveals crucial insights into cellular heterogeneity and tissue architecture. Recent advances in high-resolution technologies have enabled the profiling of gene expression at the single-cell level, yet inherent heterogeneities between platforms and differences in cell type composition make data integration challenging. We present the FIRM R package, which offers a streamlined workflow for the flexible integration of scRNA-seq data using a re-scaling algorithm that accounts for the effects of cell type composition. FIRM achieves accurate mixing of shared cell type identities and superior preservation of the original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis.

References

• Abraham, G., & Inouye, M. (2014). Fast principal component analysis of large-scale genome-wide data. PloS One, 9(4), e93766. https://doi.org/10.1371/journal.pone.0093766
• Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420. https://doi.org/10.1038/nbt.409
• Ezran, C., Liu, S., Chang, S., Ming, J., Botvinnik, O., Penland, L., Tarashansky, A., de Morree, A., Travaglini, K. J., Zhao, J., Wang, G., Hasegawa, K., Sin, H., Sit, R., Okamoto, J., Sinha, R., Zhang, Y., Karanewsky, C. J., Pendleton, J. L., …Krasnow, M. A. (2025a). A molecular cell atlas of mouse lemur, an emerging model primate. Nature, 644(8075), 173–184. https://doi.org/10.1038/s41586-025-09114-8
• Ezran, C., Liu, S., Chang, S., Ming, J., Guethlein, L. A., Wang, M. F., Dehghannasiri, R., Olivieri, J., Frank, H. K., Tarashansky, A., Koh, W., Jing, Q., Botvinnik, O., Antony, J., The Tabula Microcebus Consortium, Pisco, A. O., Karkanias, J., Yang, C., Ferrell Jr, J., … Krasnow, M. A. (2025b). Mouse lemur cell atlas informs primate genes, physiology and disease. Nature, 644(8075), 185–196. https://doi.org/10.1038/s41586-025-09114-8
• Hie, B., Bryson, B., & Berger, B. (2019). Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nature Biotechnology, 37(6), 685–691. https://doi.org/10.1038/s41587-019-0113-3
• Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019). Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods, 16(12), 1289–1296. https://doi.org/10.1038/s41592-019-0619-0
• Ming, J., Lin, Z., Zhao, J., Wan, X., Consortium, T. T. M., Ezran, C., Liu, S., Yang, C., & Wu, A. R. (2022). FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets. Briefings in Bioinformatics, 23(5), Article bbac167. https://doi.org/10.1093/bib/bbac167
• Qiu, Y. (2021). Sparse eigenvalue computation Toolkit as a redesigned ARPACK. https://spectralib.org/(open in a new window).
• Schaum, N., Karkanias, J., Neff, N. F., May, A. P., Quake, S. R., Wyss-Coray, T., Darmanis, S., Batson, J., Botvinnik, O., Chen, M. B., Chen, S., Green, F., Jones, R., Maynard, A., Penland, L., Pisco, A. O., Sit, R. V., Stanley, G. M., Webber, J. T., …Wyss-Coray, T. (2018). Single-cell transcriptomics of 20 mouse organs creates a Tabula muris. Nature, 562(7727), 367–372. https://doi.org/10.1038/s41586-018-0590-4

To cite this article: Shuzhen Ding , Zhou Yu & Jingsi Ming (08 Jun 2026): FIRM: an R package for large-scale flexible integration of single-cell data, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2026.2679093

To link to this article: https://doi.org/10.1080/24754269.2026.2679093