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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

Guy N Brock1, John R Shaffer, Richard E Blakesley

  • 1Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, Universtiy of Louisville, Louisville, KY 40292, USA. guy.brock@louisville.edu

BMC Bioinformatics
|January 12, 2008
PubMed
Summary
This summary is machine-generated.

Missing values in gene expression data hinder analysis. This study evaluates eight imputation methods, finding LSA, LLS, and BPCA competitive, and introduces entropy-based selection (EBS) and self-training selection (STS) schemes for optimal method selection.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data frequently contain missing values, necessitating imputation for downstream analyses.
  • Numerous imputation methods exist, but optimal selection criteria remain unclear.
  • This study evaluates eight imputation methods across diverse microarray experiment types.

Purpose of the Study:

  • To comprehensively evaluate existing gene expression imputation methods.
  • To identify the most effective imputation algorithms for different microarray data types.
  • To develop and validate selection schemes for choosing appropriate imputation methods.

Main Methods:

  • Extensive evaluation of eight imputation methods (LSA, LLS, BPCA, PLS, SVD, KNN, OLS) on time series, multiple exposures, and combined experimental designs.
  • Development of an entropy measure to quantify gene expression matrix complexity.
  • Introduction of entropy-based selection (EBS) and simulation-based self-training selection (STS) schemes.

Main Results:

  • LSA, LLS, and BPCA emerged as highly competitive imputation algorithms, with no single method superior across all datasets.
  • Method performance correlates with data complexity; global methods (PLS, SVD, BPCA) excel in low-complexity data, while neighbor-based methods (KNN, OLS, LSA, LLS) perform better in high-complexity data.
  • The EBS and STS schemes effectively guide the selection of appropriate imputation algorithms.

Conclusions:

  • Three imputation methods (LSA, LLS, BPCA) demonstrate strong, competitive performance.
  • Data complexity is a key factor influencing the success of different imputation strategies.
  • EBS and STS provide valuable, complementary tools for optimizing imputation method selection in microarray data analysis.