1. Missing Data Imputation Using Evolutionary k- Nearest Neighbour Algorithm for Gene Expression Data
Hiroshi de Silva, A. Shehan Perera
Department of Computer Science and Engineering
University of Moratuwa

2. Presentation Outline Gene Expression Data
Missing Data Imputation Methods
Complete Case Analysis
Available Case Analysis
Mean Imputation
Median/ Mode Imputation
Machine Learning Approaches
Advantages of kNNImputation
Disadvantages of kNNImputation
EvlkNNImputation
Methodology
Data
Evolution and Results
Summary

3. Gene Expression Data
Image: “Gene Expression, ” Wikipedia, the free encyclopedia.

4. Missing Data
Missing values of gene expression data occur for many reasons such as:
Insufficient resolution
Image corruption
Due to dust or scratches on the slide
As a result of the robotic methods used to create them.

5. Missing Data Imputation Methods
Complete Case Analysis
Available Case Analysis
Mean/ Median/ Mode Substitution
Multiple Imputation
Machine Learning Approaches

6. Complete Case Analysis
Discard of missing data.
Dropping such cases with missing data has yield biased or inconclusive results even though such techniques are still widely used in software engineering.

7. Available Case Analysis
Different subsets of data are taken to different aspects of the same study due to inability in taking the full dataset because some values of variables have incomplete data.
Complete case analysis and available case analysis both are reducing the sample sizes of the datasets.

8. Mean Imputation
Missing value imputation of numerical data is mostly handled in general by mean substitution in several works.
Can distort the distribution for the variable which is used for imputation by underestimating the standard deviation.

9. Median Imputation Mode Imputation
Median imputation is also used to assure robustness since mean is affected by outliers of a dataset.
Mode Imputation
For the categorical attributes, the mode imputation is used instead of mean and median of a dataset .

10. Machine Learning Approaches
Many machine learning algorithms solve missing data problem in an efficient way.
Advantage of using a machine learning approach is that the missing data treatment is independent of the learning algorithm used.

11. Advantages of kNNImpute
It does not require to create a predictive model for each attribute with missing values in the dataset.
Treat instances with multiple missing values.
It considers the correlation structure of data.
Predict both qualitative and quantitative attributes

12. Disadvantages of kNNImpute
The results depend on the parameter k.
The time required by the algorithm to calculate the distance between instances.
Evl-kNNImpute
Optimized k for a given dataset
Assign weights to each attribute/ feature in a dataset.

13. Methodology Genetic Algorithm k- Nearest Neighbor Trained Model
Original Data with missing values
Fitness
Score
Weights
Data instances without missing values
k- Nearest Neighbor
Complete dataset with missing values
Trained Model
Imputed Dataset

14. Time taken for optimization
Data
Data Set
Features
Instances
Time taken for optimization
gasch2 51
204
28.85 sec
spo 76
1597
19.8 min
seq 14
500
44.12 sec

15. Evolution and Results

16. Evolution and Results

17. Evolution and Results

18. Summary
Mean imputation is better when there are very few missing values in a dataset but often this is not the case when it comes to gene expression data as those datasets contain a considerable amount of missing data.
This is where the need of a supervised learning algorithm occurs and the proposed algorithm is designed to overcome the disadvantages in the kNNImpute algorithm.
By looking at the results we can conclude that EvlkNNImpute outperforms the mean imputation and kNNImputation approaches.

19. References
[1] O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman, “Missing value estimation methods for DNA microarrays,” Bioinforma. Oxf. Engl., vol. 17, no. 6, pp. 520–525, Jun
[2] “Gene expression,” Wikipedia, the free encyclopedia. 09-Jan-2016.
[3] E. Acuña and C. Rodriguez, “The Treatment of Missing Values and its Effect on Classifier Accuracy,” in Classification, Clustering, and Data Mining Applications, D. D. Banks, D. F. R. McMorris, D. P. Arabie, and P. D. W. Gaul, Eds. Springer Berlin Heidelberg, 2004, pp. 639–647.
[4] S. Chu, J. DeRisi, M. Eisen, J. Mulholland, D. Botstein, P. O. Brown, and I. Herskowitz, “The transcriptional program of sporulation in budding yeast,” Science, vol. 282, no. 5389, pp. 699–705, Oct
[5] A. P. Gasch, M. Huang, S. Metzner, D. Botstein, S. J. Elledge, and P. O. Brown, “Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p,” Mol. Biol. Cell, vol. 12, no. 10, pp. 2987–3003, Oct
[6] A. Gelman and J. Hill, “Missing-data imputation,” in Data Analysis Using Regression and Multilevel%2FHierarchical Models, Cambridge University Press, 2006.
[7] A. Mockus, “Missing Data in Software Engineering,” in Guide to Advanced Empirical Software Engineering, F. Shull, J. Singer, and D. I. K. Sjøberg, Eds. Springer London, 2008, pp. 185–200.

20. THANK YOU !
Q & A
ICTER 2016