Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning

Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

(2020) Ziwei Wang et al.   Briefings in Functional Genomics

Spectral Anomaly Detection in Large Graphs Using a Complex Moment-Based Eigenvalue Solver

(2020) Yasunori Futamura et al.   ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A-Civil Engineering

Identification of expression signatures for non-small-cell lung carcinoma subtype classification

(2019) Ran Su et al.   BIOINFORMATICS

Differential variation analysis enables detection of tumor heterogeneity using single-cell RNA-sequencing data

(2019) Emily F. Davis-Marcisak et al.   CANCER RESEARCH

A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression

(2019) Zachary F. Gerring et al.   PLoS Genetics

BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches

(2019) Bin Liu et al.   NUCLEIC ACIDS RESEARCH

Cancer classification of single-cell gene expression data by neural network

(2019) Bong-Hyun Kim et al.   BIOINFORMATICS

Spectral clustering with adaptive similarity measure in Kernel space

(2018) Xiucai Ye et al.   Intelligent Data Analysis

Bias, robustness and scalability in single-cell differential expression analysis

(2018) Charlotte Soneson et al.   NATURE METHODS

OUP accepted manuscript

(2018)   BIOINFORMATICS

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

(2018) Yanglan Gan et al.   BMC Medical Genomics

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

SC3: consensus clustering of single-cell RNA-seq data

(2017) Vladimir Yu Kiselev et al.   NATURE METHODS

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

(2017) Alexandra-Chloé Villani et al.   SCIENCE

Robust Similarity Measure for Spectral Clustering Based on Shared Neighbors

(2016) Xiucai Ye et al.   ETRI JOURNAL

Identification of Differentially Expressed Genes in RNA-seq Data of Arabidopsis thaliana: A Compound Distribution Approach

(2016) Arfa Anjum et al.   JOURNAL OF COMPUTATIONAL BIOLOGY

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

(2016) Zhicheng Ji et al.   NUCLEIC ACIDS RESEARCH

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

(2016) I. Tirosh et al.   SCIENCE

Identification of cell types from single-cell transcriptomes using a novel clustering method

(2015) Chen Xu et al.   BIOINFORMATICS

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

(2015) Evan Z. Macosko et al.   CELL

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis

(2015) Minzhe Guo et al.   PLoS Computational Biology

Bayesian approach to single-cell differential expression analysis

(2014) Peter V Kharchenko et al.   NATURE METHODS

Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

(2014) Q. Deng et al.   SCIENCE

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

(2009) Da Wei Huang et al.   Nature Protocols

WGCNA: an R package for weighted correlation network analysis

(2008) Peter Langfelder et al.   BMC BIOINFORMATICS