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Preprocessing Steps for Agilent MicroRNA Arrays: Does the Order Matter?

Li-Xuan Qin1, Huei-Chung Huang1, Qin Zhou1

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Cancer Informatics
|September 19, 2015
PubMed
Summary

The optimal order for Agilent microRNA array data preprocessing is log transformation, followed by quantile normalization and median summarization. This specific sequence slightly improves differential expression analysis results.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Microarray Analysis

Background:

  • Microarray preprocessing research often focuses on individual steps, neglecting the impact of step order.
  • The relationship between normalization, log transformation, and probe set summarization order is understudied.

Purpose of the Study:

  • To investigate how the sequence of preprocessing steps affects differential expression analysis for Agilent microRNA array data.
  • To identify the most effective ordering of normalization, log transformation, and probe set summarization.

Main Methods:

  • Utilized 192 gynecologic tumor samples, generating benchmark and test datasets with and without array effects.
  • Applied different orderings of quantile normalization, log transformation, and median summarization to the test data.
Keywords:
log transformationmicroRNAmicroarraynormalizationpreprocessingprobe set summarization

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  • Assessed ordering effectiveness using differential expression analysis, comparing results to the benchmark and evaluating true/false positive rates.

    • Main Results:

    • The sequence of log transformation, quantile normalization (on probe-level data), and median summarization demonstrated a slight performance advantage over other tested orderings.
    • This specific preprocessing order showed improved accuracy in differential expression analysis.

    Conclusions:

    • The established ordering of log transformation, quantile normalization, and median summarization provides a reliable approach for Agilent microRNA array data.
    • Findings reduce uncertainty regarding the impact of preprocessing step order on analysis outcomes.