The effect of tissue composition on gene co-expression
| Autor: | Matthew N. McCall, Marc K. Halushka, Yun Zhang, Jonavelle Cuerdo |
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| Rok vydání: | 2019 |
| Předmět: |
AcademicSubjects/SCI01060
Computer science Review Article deconvolution Correlation transcriptomics 03 medical and health sciences 0302 clinical medicine Animals Humans Gene Regulatory Networks Molecular Biology Gene 030304 developmental biology 0303 health sciences Models Genetic induced covariance tissue composition Component (thermodynamics) Composition (combinatorics) Expression (mathematics) co-expression Gene Expression Regulation Neoplastic cell-types Variable (computer science) Organ Specificity Deconvolution Transcriptome Tissue composition Biological system 030217 neurology & neurosurgery Information Systems |
| Zdroj: | Briefings in Bioinformatics |
| ISSN: | 1477-4054 1467-5463 |
| DOI: | 10.1093/bib/bbz135 |
| Popis: | Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest. |
| Databáze: | OpenAIRE |
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