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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Differential correlation for sequencing data.

Charlotte Siska1, Katerina Kechris2

  • 1Computational Bioscience Program, Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. charlotte.siska@ucdenver.edu.

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|January 21, 2017
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Summary
This summary is machine-generated.

A new R package, Discordant, identifies differential correlation in sequencing data using mixture models. Spearman

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential correlation (DC) methods are typically designed for continuous data, not count-based sequencing data.
  • Sequencing data, often modeled with negative binomial distributions, poses challenges for standard correlation metrics.
  • Existing DC methods require adaptation for the unique characteristics of high-throughput sequencing data.

Purpose of the Study:

  • To develop and evaluate a novel R package, Discordant, for identifying differential correlation in sequencing data.
  • To compare the performance of various correlation metrics for detecting DC in count-based omics data.
  • To enhance the computational efficiency and versatility of DC analysis for sequencing datasets.

Main Methods:

  • Developed the R package Discordant utilizing mixture models and the Expectation Maximization (EM) algorithm.
  • Implemented and tested multiple correlation metrics suitable for sequencing data, including Spearman's correlation.
  • Incorporated subsampling within the EM algorithm to reduce computational runtime and address feature pair independence assumptions.

Main Results:

  • Spearman's correlation demonstrated superior performance for identifying differential correlation in simulated and real breast cancer miRNA-Seq and RNA-Seq data.
  • The Discordant package with Spearman's correlation showed increased power in ROC and sensitivity/specificity analyses, improving identification of validated breast cancer miRNAs.
  • Subsampling significantly reduced runtime with minimal impact on analytical performance, while additional DC types offered greater versatility at a slight power cost.

Conclusions:

  • The Discordant R package provides a robust method for differential correlation analysis in sequencing data.
  • Spearman's correlation is recommended for sequencing data, though Discordant supports alternative metrics for diverse applications.
  • Extensions including additional DC types and EM algorithm subsampling enhance Discordant's versatility and efficiency for multi-omics studies.