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GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution
Author(s) -
Magdalena E. Strauß,
Paul Kirk,
John E. Reid,
Lorenz Wernisch
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz778
Subject(s) - computer science , data mining , deconvolution , cluster analysis , bayesian probability , markov chain monte carlo , inference , sampling (signal processing) , parametric statistics , source code , algorithm , artificial intelligence , statistics , mathematics , filter (signal processing) , computer vision , operating system
Many methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters.

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