ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objecti...

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Bibliographic Details
Published in:Scientific reports 2020-10, Vol.10 (1), p.17483-17483, Article 17483
Main Authors: Bui, Thuy Tien, Lee, Daniel, Selvarajoo, Kumar
Format: Article
Language:English
Subjects:
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Animals
Cell Hypoxia
Computational Biology - methods
Computer Graphics
Embryonic Stem Cells - metabolism
Enzyme Inhibitors - pharmacology
Epoxy Compounds - pharmacology
Escherichia coli - metabolism
Gene Expression Profiling
Gene Expression Regulation
Humanities and Social Sciences
Male
Mice
Mice, Inbred C57BL
multidisciplinary
Principal Component Analysis
Programming Languages
Saccharomyces cerevisiae - metabolism
Scattering, Radiation
Science
Science (multidisciplinary)
Systems Biology
Transcriptome
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Summary:Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-74564-1