SAFE-clustering: Single-cell Aggregated (from Ensemble) clustering for 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. ...
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One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this paper we study some existing scRNA-Seq imputation methods and propose a novel iterativ...
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Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditio...
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...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2018
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bty793