Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets
Joint Authors
Bruno, Andrew E.
Rao, Sreevidya Sadananda Sadasiva
Miecznikowski, Jeffrey C.
Liu, Song
Shepherd, Lori A.
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Biology
Abstract EN
Introduction.
The microarray datasets from the MicroArray Quality Control (MAQC) project have enabled the assessment of the precision, comparability of microarrays, and other various microarray analysis methods.
However, to date no studies that we are aware of have reported the performance of missing value imputation schemes on the MAQC datasets.
In this study, we use the MAQC Affymetrix datasets to evaluate several imputation procedures in Affymetrix microarrays.
Results.
We evaluated several cutting edge imputation procedures and compared them using different error measures.
We randomly deleted 5% and 10% of the data and imputed the missing values using imputation tests.
We performed 1000 simulations and averaged the results.
The results for both 5% and 10% deletion are similar.
Among the imputation methods, we observe the local least squares method with k=4 is most accurate under the error measures considered.
The k-nearest neighbor method with k=1 has the highest error rate among imputation methods and error measures.
Conclusions.
We conclude for imputing missing values in Affymetrix microarray datasets, using the MAS 5.0 preprocessing scheme, the local least squares method with k=4 has the best overall performance and k-nearest neighbor method with k=1 has the worst overall performance.
These results hold true for both 5% and 10% missing values.
American Psychological Association (APA)
Rao, Sreevidya Sadananda Sadasiva& Shepherd, Lori A.& Bruno, Andrew E.& Liu, Song& Miecznikowski, Jeffrey C.. 2013. Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets. Advances in Bioinformatics،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-498306
Modern Language Association (MLA)
Rao, Sreevidya Sadananda Sadasiva…[et al.]. Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets. Advances in Bioinformatics No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-498306
American Medical Association (AMA)
Rao, Sreevidya Sadananda Sadasiva& Shepherd, Lori A.& Bruno, Andrew E.& Liu, Song& Miecznikowski, Jeffrey C.. Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets. Advances in Bioinformatics. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-498306
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498306