Cargando…
scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multipl...
Autores principales: | Qian, Kun, Fu, Shiwei, Li, Hongwei, Li, Wei Vivian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8935111/ https://www.ncbi.nlm.nih.gov/pubmed/35313930 http://dx.doi.org/10.1186/s13059-022-02649-3 |
Ejemplares similares
-
Author Correction: scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
por: Qian, Kun, et al.
Publicado: (2022) -
scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data
por: Li, Wei Vivian, et al.
Publicado: (2021) -
scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution
por: Kapourani, Chantriolnt-Andreas, et al.
Publicado: (2021) -
scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
por: Sun, Tianyi, et al.
Publicado: (2021) -
scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
por: Heinen, Tobias, et al.
Publicado: (2022)