Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model
Authors
Keywords
-
Journal
Genes
Volume 12, Issue 2, Pages 311
Publisher
MDPI AG
Online
2021-02-23
DOI
10.3390/genes12020311
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Visualizing Single-Cell RNA-seq Data with Semisupervised Principal Component Analysis
- (2020) Zhenqiu Liu INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Efficient Solvers for Sparse Subspace Clustering
- (2020) Farhad Pourkamali-Anaraki et al. SIGNAL PROCESSING
- Challenges in unsupervised clustering of single-cell RNA-seq data
- (2019) Vladimir Yu Kiselev et al. NATURE REVIEWS GENETICS
- SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation
- (2019) Ruiqing Zheng et al. BIOINFORMATICS
- Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
- (2018) David van Dijk et al. CELL
- Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
- (2018) Jiarui Ding et al. Nature Communications
- VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder
- (2018) Dongfang Wang et al. GENOMICS PROTEOMICS & BIOINFORMATICS
- Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
- (2017) Bo Wang et al. NATURE METHODS
- SC3: consensus clustering of single-cell RNA-seq data
- (2017) Vladimir Yu Kiselev et al. NATURE METHODS
- pcaReduce: hierarchical clustering of single cell transcriptional profiles
- (2016) Justina žurauskienė et al. BMC BIOINFORMATICS
- De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
- (2016) Dominic Grün et al. Cell Stem Cell
- Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
- (2015) Jacob H. Levine et al. CELL
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
- (2015) Evan Z. Macosko et al. CELL
- Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
- (2015) Aleksandra A. Kolodziejczyk et al. Cell Stem Cell
- Single-cell messenger RNA sequencing reveals rare intestinal cell types
- (2015) Dominic Grün et al. NATURE
- Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
- (2015) Florian Buettner et al. NATURE BIOTECHNOLOGY
- Network construction and structure detection with metagenomic count data
- (2015) Zhenqiu Liu et al. BioData Mining
- Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data
- (2014) Zhenqiu Liu et al. BIOINFORMATICS
- Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
- (2014) Alex A Pollen et al. NATURE BIOTECHNOLOGY
- Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
- (2014) Dmitry Usoskin et al. NATURE NEUROSCIENCE
- Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
- (2014) A. P. Patel et al. SCIENCE
- Entering the era of single-cell transcriptomics in biology and medicine
- (2013) Rickard Sandberg NATURE METHODS
- Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
- (2010) Roel G.W. Verhaak et al. CANCER CELL
- Fast unfolding of communities in large networks
- (2008) Vincent D Blondel et al. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search