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Strategies for aggregating gene expression data: the collapseRows R function.

Jeremy A Miller1, Chaochao Cai, Peter Langfelder

  • 1Interdepartmental Program for Neuroscience, UCLA, Los Angeles, California, USA.

BMC Bioinformatics
|August 6, 2011
PubMed
Summary
This summary is machine-generated.

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Summarizing high-dimensional genomic data is essential. The R function collapseRows offers robust methods for collapsing variables, improving gene expression meta-analysis and cell type deconvolution.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-dimensional genomic analyses require summarizing multiple related variables into a single representative.
  • Existing methods for collapsing, combining, reducing, or aggregating variables are limited.
  • Network-based approaches offer alternative strategies for variable summarization.

Purpose of the Study:

  • To introduce and evaluate the R function collapseRows for variable summarization in genomic data.
  • To compare standard statistical and network-based collapsing methods.
  • To demonstrate the utility of collapseRows in gene expression meta-analysis, co-expression module summarization, and cell type deconvolution.

Main Methods:

  • Implementation of multiple collapsing methods within the R function collapseRows.

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  • Evaluation of performance in gene expression meta-analysis by collapsing probes for single genes.
  • Assessment of co-expression module summarization strategies.
  • Application to cell type deconvolution using marker gene aggregation.

    • Main Results:

    • Collapsing probes by highest average expression optimizes between-study consistency in microarray meta-analysis.
    • Optimal collapsing strategy for co-expression modules depends on the specific analysis goal.
    • Identifying the most highly connected 'hub' marker gene improves cell type abundance prediction accuracy.
    • The userListEnrichment function facilitates biological interpretation of collapsed gene lists.

    Conclusions:

    • The R function collapseRows provides robust and biologically relevant tools for genomic data summarization.
    • Both standard statistical and network-based collapsing methods are effective.
    • The function aids in gene expression meta-analysis, module analysis, and cell type deconvolution.