Abstract Details
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Activity Number:
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343
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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| Abstract - #309597 |
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Title:
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Efficient Outlier Identification in Lung Cancer Study
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Author(s):
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Shibing Deng*+
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Companies:
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Pfizer, Inc.
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Keywords:
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outlier analysis ;
gene expression ;
cancer
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Abstract:
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Cancer outlier profile analysis (COPA) has been applied to microarray gene expression data in cancer research to address heterogeneity in cancer patient population, where a subset of patients demonstrate amplified or suppressed expression in some genes. Several statistics have been proposed in the literature in recent years to identify the outlier tumor samples. We proposed a new method, named Maximum Squared Difference (MSD) and compared its performance to the existing methods in literature and found MSD was more sensitive to identify outlier samples when outliers are relatively rare in the population (< 20%) which is a common situation in oncology. Statistical properties of MSD was studied and the method was applied to a lung cancer dataset from TCGA.
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Authors who are presenting talks have a * after their name.
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