Most features of a cell are determined by gene programs — sets of co-expressed genes that execute a specific function. By incorporating existing knowledge about gene programs and cell types, the Spectra factor analysis method improves how we decode single-cell transcriptomic data and offers insights into challenging tumor immune contexts.
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Levitin et al. De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Mol. Syst. Biol. 15, e8557 (2019). Unsupervised Bayesian factorization approach for identifying highly coherent gene programs.
Buettner, F. et al. f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq. Genome Biol. 18, 212 (2017). The first method to successfully identify gene programs from scRNA-seq data using pathway annotations.
Kartha, V. K. et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2, 100166 (2022). This dataset was used to benchmark Spectra’s performance.
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–755 (2021). ScRNA-seq datasets from patients with breast cancer before and after immunotherapy that we used to associate gene expression programs in tumor-infiltrating immune cells with surrogates of therapy response.
Salcher, S. et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell 40, 1503–1520 (2022). An scRNA-seq atlas of lung tumors from 318 patients with data from over 1.2 million cells that we used to interrogate Spectra’s generalizability.
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This is a summary of: Kunes, R. Z. et al. Supervised discovery of interpretable gene programs from single-cell data. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01940-3 (2023).
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Decoding the building blocks of cellular processes from single-cell transcriptomics data. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01967-6