Difference between revisions of "Missing value estimation methods for DNA microarrays"
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Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for dna | Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for dna | ||
microarrays. Bioinformatics, 17(6):520–525. | microarrays. Bioinformatics, 17(6):520–525. | ||
| − | [https://doi.org/10.1093/bioinformatics/17.6.520 | + | [https://doi.org/10.1093/bioinformatics/17.6.520 Permanent link to paper] |
=== Summary === | === Summary === | ||
Revision as of 15:22, 25 February 2020
Contents
Citation
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for dna microarrays. Bioinformatics, 17(6):520–525. Permanent link to paper
Summary
SVD, KNN and row average imputation are evaluated with different parameter settings on real data sets with regard to robustness, sensitivity and accuracy.
Study outcomes
Outcome O1
Rank of performance: KNN, SVD, row average, zero filling.
Outcome O2
"KNN is relatively insensitive to .. K within the range of k=10-20" (Figure 1)
Outcome O3
SVD "is sensitive to the type of data" and "is ideally suited .. in terms of .. constituent patterns"
Study design and evidence level
Just 4 imputation algorithms (SVD,KNN) are evaluated from which 2 are singular value substitutions (average,zero).
Analysis is performed over a broad range of hyperparameters (KNN: k=[1,1000], SVD: Eigengenes=[5,30]).
The imputation methods are only analyzed on data with less than 20%.
