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Activity Number: 123 - Unraveling Tissue Heterogeneity for Analyzing Omics Data in Cancer Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: WNAR
Abstract #309749
Title: Co-Expression Analysis of Complex Tissues
Author(s): Matthew Nicholson McCall* and Yun Zhang
Companies: University of Rochester Medical Center and J. Craig Venter Institute (JCVI)
Keywords: tissue composition; deconvolution; co-expression; network modeling

Tissue samples are not homogeneous entities; rather they are comprised of multiple cell types with distinct functions and corresponding transcriptomic profiles. The proportion of component cell types in a sample often varies from sample to sample, and many pathological processes alter cellular composition via mechanisms such as infiltration or differentiation. Analysis of complex tissue samples without accounting for differences in cellular composition can lead to erroneous conclusions. 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. 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 modified to deconvolute two component cell type mixtures and identify conditions in which a deconvolution approach would be beneficial.

Authors who are presenting talks have a * after their name.

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