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Outlier Detection using Projection Quantile Regression for Mass Spectrometry Data with Low Replication

1 , 1 , 1 , , 1

BMC Research Notes

BioMed Central

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      Abstract

      Background

      Mass spectrometry (MS) data are often generated from various biological or chemical experiments and there may exist outlying observations, which are extreme due to technical reasons. The determination of outlying observations is important in the analysis of replicated MS data because elaborate pre-processing is essential for successful analysis with reliable results and manual outlier detection as one of pre-processing steps is time-consuming. The heterogeneity of variability and low replication are often obstacles to successful analysis, including outlier detection. Existing approaches, which assume constant variability, can generate many false positives (outliers) and/or false negatives (non-outliers). Thus, a more powerful and accurate approach is needed to account for the heterogeneity of variability and low replication.

      Findings

      We proposed an outlier detection algorithm using projection and quantile regression in MS data from multiple experiments. The performance of the algorithm and program was demonstrated by using both simulated and real-life data. The projection approach with linear, nonlinear, or nonparametric quantile regression was appropriate in heterogeneous high-throughput data with low replication.

      Conclusion

      Various quantile regression approaches combined with projection were proposed for detecting outliers. The choice among linear, nonlinear, and nonparametric regressions is dependent on the degree of heterogeneity of the data. The proposed approach was illustrated with MS data with two or more replicates.

      Related collections

      Most cited references 9

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      Regression Quantiles

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        Procedures for Detecting Outlying Observations in Samples

         Frank Grubbs (1969)
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          • Record: found
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          • Article: not found

          Sample Criteria for Testing Outlying Observations

           Frank Grubbs (1950)
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            Author and article information

            Affiliations
            [1 ]Department of Statistics, Korea University, Seoul, Korea
            Contributors
            Journal
            BMC Res Notes
            BMC Res Notes
            BMC Research Notes
            BioMed Central
            1756-0500
            2012
            15 May 2012
            : 5
            : 236
            22587344 3514222 1756-0500-5-236 10.1186/1756-0500-5-236
            Copyright ©2012 Eo et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Technical Note

            Medicine

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