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Abstract Details
Activity Number:
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248
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract - #305435 |
Title:
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Normalization of Metabolomics Data
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Author(s):
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Matthew Mitchell*+
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Companies:
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Metabolon Inc.
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Address:
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5306 Tahoe Drive, Durham, NC, 27713, United States
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Keywords:
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normalization ;
p is much greater than n ;
omics sciences ;
metabolomics
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Abstract:
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Data in -omics fields such as genomics and proteomics undergo normalization in order to remove systematic variation. Some common methods include quantile normalization for genomic data and dividing by the total ion count for proteomics. However, these methods are based on certain assumptions concerning the underlying sources of variation. Some of these assumptions do not hold for metabolomics data. One key difference is that systematic variation from daily instrument drift does not affect all classes of metabolites in the same way. To avoid confounding the experimental design with the instrument run-day, treatment groups are balanced across the run. Hence, for each metabolite, the expected value is the same on each run day, if there were no instrument drift. Then to remove this drift, one may simply divide by the mean or median. This normalization is compared to other methods. For human plasma, the values resulting from these normalization techniques are compared to targeted values (absolute quantitation), and normalizing each metabolite by the median on each run day outperformed the other methods.
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