ArticleBioinformatics (Oxford, England)2017
fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Functional data analysis for computational biology.Bioinformatics (Oxford, England) · 2019Article
- The SMAD2/3 interactome reveals that TGFβ controls mNature · 2018Article
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Abstract
Summary: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We show how this method differs from other measures of correlation, and exemplify how it can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers. Availability and Implementation: An R/Bioconductor package is available at http://bioconductor.org/packages/fCCAC/ . Contact: pmb59@cam.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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