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Activity Number:
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609
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304774 |
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Title:
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Effects of Missing Value Imputation on Down-Stream Analyses in the Microarray Data
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Author(s):
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Sunghee Oh*+ and George C. Tseng and Guy N. Brock
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Companies:
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University of Pittsburgh and University of Pittsburgh and University of Louisville
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Address:
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130 Desoto Street, 311 Parran Hall, Dept of Biostatistics, Pittsburgh, PA, 15232,
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Keywords:
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microarray experiment ; missing value ; imputation method ; down-stream analysis ; root mean squared error (RMSE) ; quantitative measure
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Abstract:
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Despite advances and the popular usage, the microarray experiment produces some missing entries and a large number of genes may be affected. However, many downstream algorithms for gene expression analysis require a complete matrix as an input. For now, there exists no uniformly superior imputation method. In addition, imputation methods have been mostly compared in terms of variants of RMSEs which compare true expression values to imputed values. The drawback of RMSE-based evaluation is that the measure does not reflect the true biological effect in down-stream analyses. We investigate how missing value imputation process affects the biological results of differentially expressed genes discovery, clustering and classification. Quantitative measures reflecting the true biological effects in each down-stream analysis will be used to imputation methods and compared to RMSE-base evaluation.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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