ArticleNature biotechnology2024
Supervised discovery of interpretable gene programs from single-cell data.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Trial
- Article
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- scENature communications · 2026Article
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Differential T cell clonal dynamics underlie outcomes to frontline chemoimmunotherapy in advanced gastric cancer.Cell reports. Medicine · 2026Article
- Hierarchical classification of immune cell transcriptomes at population-scale.bioRxiv : the preprint server for biology · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Identifying clinically relevant cell state interactions in the tumor microenvironment of IDH-mutant gliomas using CSI-TME.Molecular systems biology · 2026Article
- Supervised deep learning with gene functional annotation for cell classification.PLoS computational biology · 2026Article
- Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data.Nature communications · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs.Cell genomics · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Genome-scale mapping of variant, enhancer and gene function in primary human CD4+ T cells.bioRxiv : the preprint server for biology · 2026Article
- Redefining cellular reprogramming with advanced genomic technologies.Nature reviews. Genetics · 2026Review
- Isoform-specific single-cell perturb-seq reveals distinct functions of alternative promoters in drug response.Nucleic acids research · 2026Article
- CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretable inflammation landscape of circulating immune cells.Nature medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.