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CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners.

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We introduce system-level coexpression analysis, a novel method that considers all gene expression data without tissue standardization. This approach reveals valuable biological insights and is accessible through the CoGTEx resource.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Coexpression analysis is crucial for understanding biological pathways, gene regulation, and human diseases.
  • Current coexpression estimations often rely on tissue-level standardization, potentially overlooking valuable information from overall gene expression levels.
  • There is a need for coexpression methods that incorporate diverse cell types and account for non-uniform data across tissues.

Purpose of the Study:

  • To challenge the assumption that variance is more important than mean gene expression levels in coexpression analysis.
  • To develop and validate a "system-level" coexpression estimation method that does not rely on tissue standardization.
  • To provide a comprehensive resource for exploring both tissue-level and system-level coexpression data from human GTEx data.

Main Methods:

  • GTEx v8 expression data underwent global normalization, batch processing, and filtering.
  • Principal Component Analysis (PCA), clustering, and t-distributed Stochastic Neighbor Embedding (tSNE) were used to define 42 distinct tissue clusters.
  • Coexpression was calculated for 33,445 genes across 42 tissue clusters, using sampling to avoid tissue overrepresentation and repeated 20 times for robust estimation. Pearson, Spearman, and G-statistic metrics were computed at both system-level (TPM scale) and tissue-level (z-score scale).

Main Results:

  • Tissue-level coexpression estimations were validated against existing databases.
  • System-level coexpression estimations demonstrated differences from tissue-level estimations, with both revealing valuable information about biological pathways.
  • Coexpression estimations were found to be associated with transcriptional regulation, and the CoGTEx resource was presented for analyzing human adult tissue coexpression data.

Conclusions:

  • System-level coexpression represents a novel and valuable metric for generating biological hypotheses and predictions.
  • The CoGTEx resource offers a unique platform for viewing, comparing, and downloading both system- and tissue-level coexpression estimations.
  • This study highlights the importance of considering overall gene expression levels in coexpression analysis for a more comprehensive understanding of biological systems.