SC3: consensus clustering of single-cell RNA-seq data
نویسندگان
چکیده
منابع مشابه
A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data
Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. ...
متن کاملA Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data
Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. ...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملModel-based clustering for RNA-seq data
MOTIVATION RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important te...
متن کاملNonparanormal Distributions & Causal Inference with Single-Cell RNA-Seq Data
Background. Single-cell RNA-Seq is a new technique that can measure gene expression levels in individual cells. We would like to use single-cell RNA-seq data to learn genetic regulatory networks. This is a natural task for causal-model structurelearning algorithms, which aim to learn the causal relationships between the measured variables. Causal algorithms perform poorly in high dimensions unl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nature Methods
سال: 2017
ISSN: 1548-7091,1548-7105
DOI: 10.1038/nmeth.4236