2019
DOI: 10.1093/bioinformatics/btz778
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GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution

Abstract: Motivation 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 lev… Show more

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Cited by 9 publications
(6 citation statements)
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“…A Gaussian Process prior (GP prior 41 ) is used to infer the non-parametric terms. Gaussian processes are highly flexible and have been used extensively in other molecular biology applications, such as gene-expression time courses 42 46 , single-cell transcriptomics 47 49 and spatial proteomics 50 , 51 .…”
Section: Introductionmentioning
confidence: 99%
“…A Gaussian Process prior (GP prior 41 ) is used to infer the non-parametric terms. Gaussian processes are highly flexible and have been used extensively in other molecular biology applications, such as gene-expression time courses 42 46 , single-cell transcriptomics 47 49 and spatial proteomics 50 , 51 .…”
Section: Introductionmentioning
confidence: 99%
“…Determining an optimal (weighted) combination of kernels is known as multiple kernel learning (MKL); see, e.g. Bach et al (2004) , Gönen and Alpayd (2011) , Lanckriet et al (2004a ), Strauß et al (2019) , Wang et al (2017) , Yu et al (2010) . A challenge associated with these approaches is how best to define the kernel function(s), for which there may be many choices.…”
Section: Introductionmentioning
confidence: 99%
“…A Gaussian Process prior (GP prior, (Stein, 2012)) is used to infer the non-parametric terms. Gaussian processes are highly flexible and have been used extensively in other molecular biology applications, such as gene-expression time courses (Kirk and Stumpf, 2009; Kirk et al ., 2012; Stegle et al ., 2010; Cooke et al ., 2011; Babtie et al ., 2014), single-cell transcriptomics (Reid and Wernisch, 2016; Boukouvalas et al ., 2018; Strauss et al ., 2020) and spatial proteomics (Crook et al ., 2019; Shin et al ., 2019).…”
Section: Introductionmentioning
confidence: 99%