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Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets
- Source :
- Advances in Bioinformatics, Vol 2013 (2013), Advances in Bioinformatics
- Publication Year :
- 2013
- Publisher :
- Hindawi Limited, 2013.
-
Abstract
- 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 is most accurate under the error measures considered. The k-nearest neighbor method with 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 has the best overall performance and k-nearest neighbor method with has the worst overall performance. These results hold true for both 5% and 10% missing values.
- Subjects :
- Statistics::Applications
Article Subject
Microarray
Microarray analysis techniques
Computer science
Affymetrix microarray
Biomedical Engineering
Word error rate
Missing data
computer.software_genre
Quantitative Biology::Genomics
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Computer Science Applications
lcsh:Biology (General)
Preprocessor
Imputation (statistics)
Data mining
lcsh:QH301-705.5
lcsh:Statistics
lcsh:HA1-4737
computer
Affymetrix microarrays
Research Article
Subjects
Details
- ISSN :
- 16878035 and 16878027
- Volume :
- 2013
- Database :
- OpenAIRE
- Journal :
- Advances in Bioinformatics
- Accession number :
- edsair.doi.dedup.....4081515ddaebb174c9fe28651745ecba
- Full Text :
- https://doi.org/10.1155/2013/790567