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The effect of tissue composition on gene co-expression
Yun Zhang1,2, Jonavelle Cuerdo3, Marc K Halushka4
1Department of Biostatistics and Computation Biology, University of Rochester, Rochester, NY, USA.
Briefings in Bioinformatics
|December 10, 2019
Summary
Tissue sample cell composition significantly impacts gene co-expression network estimation. This study shows deconvolution methods can adjust for cellular differences, improving co-expression analysis accuracy for genomic profiling studies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Variable cellular composition in tissue samples complicates genomic profiling interpretation.
- Existing methods focus on differential expression, neglecting tissue composition's effect on co-expression.
Purpose of the Study:
- To investigate how variable cell-type composition affects gene co-expression network estimation.
- To develop and evaluate methods for adjusting co-expression estimates based on tissue cellularity.
Main Methods:
- Mathematical decomposition of tissue-level gene correlations.
- Application of deconvolution methods, typically used for tumor-stromal mixtures, to two-component cell-type mixtures.
- Validation using simulated and real biological data.
Main Results:
- Tissue composition significantly influences correlation-based co-expression estimates.
- Deconvolution methods can effectively adjust for cellular heterogeneity in co-expression analysis.
- Uncorrelated cell-type-specific markers are optimal for deconvoluting expression and co-expression patterns.
Conclusions:
- Accounting for cellular composition is crucial for accurate gene co-expression analysis.
- Deconvolution offers a viable strategy to correct for tissue heterogeneity in co-expression studies.
- A user-friendly application is provided for exploring these effects.
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