Cargando…
Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models
Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a spar...
Autores principales: | Sekula, Michael, Gaskins, Jeremy, Datta, Susmita |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855158/ https://www.ncbi.nlm.nih.gov/pubmed/35186014 http://dx.doi.org/10.3389/fgene.2021.810816 |
Ejemplares similares
-
A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data
por: Sekula, Michael, et al.
Publicado: (2020) -
Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model
por: Wu, Dongyuan, et al.
Publicado: (2023) -
Clustering single-cell multimodal omics data with jrSiCKLSNMF
por: Ellis, Dorothy, et al.
Publicado: (2023) -
Bayesian differential analysis of cell type proportions: opinion
por: Karagiannis, Tanya T., et al.
Publicado: (2023) -
Bayesian inference on quasi-sparse count data
por: Datta, Jyotishka, et al.
Publicado: (2016)