Supervised discovery of interpretable gene programs from single-cell data Journal Article
| Authors: | Kunes, R. Z.; Walle, T.; Land, M.; Nawy, T.; Pe’er, D. |
| Article Title: | Supervised discovery of interpretable gene programs from single-cell data |
| Abstract: | Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene–gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs. © The Author(s) 2023. |
| Keywords: | controlled study; human tissue; human cell; genetics; neoplasm; neoplasms; cd8 antigen; cd8+ t lymphocyte; cd8-positive t-lymphocytes; cytology; metabolism; gene expression; gene expression profiling; computational biology; algorithms; cell specificity; immunology; algorithm; tumors; cell types; benchmarking; tumor immunity; macrophage; bioinformatics; leukocyte; t-cells; single cell analysis; single-cell analysis; gene expression data; procedures; single cells; matrix factorizations; factorization; humans; human; article; t cell exhaustion; immunometabolism; spectra's; cell data; checkpoint inhibitor therapy; factorization methods; factors analysis; gene sets; technical artifacts; metabolic pathway analysis |
| Journal Title: | Nature Biotechnology |
| Volume: | 42 |
| Issue: | 7 |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2024-07-01 |
| Start Page: | 1084 |
| End Page: | 1095 |
| Language: | English |
| DOI: | 10.1038/s41587-023-01940-3 |
| PUBMED: | 37735262 |
| PROVIDER: | scopus |
| PMCID: | PMC10958532 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is Dana Pe'er -- Source: Scopus |
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