Predictability of human differential gene expression

Crow, M., Lim, N., Ballouz, S., Pavlidis, P., Gillis, J. (March 2019) Predictability of human differential gene expression. Proc Natl Acad Sci U S A. ISSN 0027-8424

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URL: https://www.ncbi.nlm.nih.gov/pubmed/30846554
DOI: 10.1073/pnas.1802973116

Abstract

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, approximately 0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the "DE prior." In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.

Item Type: Paper
Additional Information: 1091-6490 Crow, Megan ORCID: http://orcid.org/0000-0002-1172-5897 Lim, Nathaniel Ballouz, Sara Pavlidis, Paul Gillis, Jesse Journal Article United States Proc Natl Acad Sci U S A. 2019 Mar 7. pii: 1802973116. doi: 10.1073/pnas.1802973116.
Uncontrolled Keywords: differential expression metaanalysis replicability specificity transcriptomics
Subjects: bioinformatics > genomics and proteomics > analysis and processing
bioinformatics > computational biology
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
CSHL Authors:
Communities: CSHL labs > Gillis Lab
Depositing User: Matthew Dunn
Date: 7 March 2019
Date Deposited: 12 Mar 2019 19:57
Last Modified: 12 Mar 2019 19:57
Related URLs:
URI: https://repository.cshl.edu/id/eprint/37730

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