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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models.

Brian E Vestal1, Elizabeth Wynn2, Camille M Moore3

  • 1Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, CO, 80206, USA. vestalb@njhealth.org.

BMC Bioinformatics
|November 17, 2022
PubMed
Summary

The new lmerSeq R package offers improved analysis for RNA Sequencing (RNA-Seq) data with complex dependencies. It provides better error control and statistical power compared to existing methods for analyzing transformed RNA-Seq counts.

Keywords:
Correlated dataLinear mixed modelsRNA-Seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • RNA Sequencing (RNA-Seq) analysis with dependent observations requires specialized tools.
  • Longitudinal sampling designs introduce complexity in RNA-Seq data analysis.

Purpose of the Study:

  • To introduce the lmerSeq R package for analyzing transformed RNA-Seq counts.
  • To provide a flexible tool for linear mixed effects models with dependent RNA-Seq data.

Main Methods:

  • Fitting linear mixed effects models to transformed RNA-Seq counts.
  • Utilizing an R package designed to handle complex observational dependencies.

Main Results:

  • The lmerSeq package demonstrated superior performance in simulations.
  • lmerSeq showed better nominal error rate control and statistical power than existing methods.

Conclusions:

  • Existing R packages have limitations in variance structures and transformation methods for RNA-Seq data.
  • lmerSeq offers greater flexibility and improved analytical results for transformed RNA-Seq data.