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Transcriptome diversity is a systematic source of variation in RNA-sequencing data.

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Transcriptome diversity, measured by Shannon entropy, explains significant gene expression variability and is the strongest factor in PEER analysis. This metric reveals associations with technical and biological factors across datasets.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology

Background:

  • RNA sequencing (RNA-seq) is crucial for gene expression analysis.
  • Interpreting and removing artifactual signals in RNA-seq data remains challenging.
  • Biological (sex, age) and technical (batches, sequencing technology) factors introduce biases.

Purpose of the Study:

  • To identify a simple metric that explains major variability in gene expression.
  • To investigate the relationship between this metric and existing analytical factors like PEER.
  • To assess the generalizability of this metric across different organisms and datasets.

Main Methods:

  • Calculation of transcriptome diversity using Shannon entropy.
  • Analysis of gene expression variability and its association with transcriptome diversity.
  • Evaluation of transcriptome diversity as a factor within Probabilistic Estimation of Expression Residuals (PEER).

Main Results:

  • Transcriptome diversity explains a substantial portion of gene expression variability.
  • Transcriptome diversity is the strongest known factor encoded within PEER covariates.
  • Significant associations were found between transcriptome diversity and multiple technical/biological variables across diverse datasets.

Conclusions:

  • Transcriptome diversity offers a straightforward explanation for major sources of variation in gene expression.
  • This metric simplifies the interpretation of complex covariates like PEER factors.
  • Transcriptome diversity is a robust indicator of variability in gene expression studies.