TY  - JOUR
AU  - Kunes, Russell Z
AU  - Walle, Thomas
AU  - Land, Max
AU  - Nawy, Tal
AU  - Pe'er, Dana
TI  - Supervised discovery of interpretable gene programs from single-cell data.
JO  - Nature biotechnology
VL  - 42
IS  - 7
SN  - 0733-222X
CY  - New York, NY
PB  - Springer Nature
M1  - DKFZ-2023-01914
SP  - 1084-1095
PY  - 2024
N1  - #EA:F230# / 2024 Jul;42(7):1084-1095
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:37735262
DO  - DOI:10.1038/s41587-023-01940-3
UR  - https://inrepo02.dkfz.de/record/283127
ER  -