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 === | ||
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=== Study outcomes === | === Study outcomes === | ||
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==== Outcome O1 ==== | ==== Outcome O1 ==== | ||
Rank of performance: KNN, SVD, row average, zero filling. | Rank of performance: KNN, SVD, row average, zero filling. | ||
Latest revision as of 15:22, 25 February 2020
Contents
1 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
2 Summary
SVD, KNN and row average imputation are evaluated with different parameter settings on real data sets with regard to robustness, sensitivity and accuracy.
3 Study outcomes
3.1 Outcome O1
Rank of performance: KNN, SVD, row average, zero filling.
3.2 Outcome O2
"KNN is relatively insensitive to .. K within the range of k=10-20" (Figure 1)
3.3 Outcome O3
SVD "is sensitive to the type of data" and "is ideally suited .. in terms of .. constituent patterns"
4 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%.
