DeepLearningProjV3

DeepLearningProjV3

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  DEEP LEARNING FOR FEATURESELECTION IN CASE AND CONTROL PREDICTION Ana Sanchez Graduate Student Project Day Spring 2015
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  Introduction • Deep learningis a method that uses sets of machine learning algorithms to model data. • Learning representations of data algorithms are used to obtain information that may otherwise not be detected. • In this project, deep learning is used for case and control prediction in genome wide association studies, specifically to construct new feature vectors.
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  Methods • SNP datais used to implement deep learning. Currently experimenting with Upper GI Cancer data. • The data contains approximately 5000 subjects (case and control) and was reduced to 10 case subjects and 10 control subjects. • There are 491,774 SNP features per subject. The SNPs are condensed to patches of 100.
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  Methods - Patching •The patches are generated by starting at index 0 to 99, then shifting by 50 SNPs and obtaining the next 100 SNPs until the final SNP (ex: 1st patch 0- 99, 2nd patch 49-149, 3rd patch 100-199, etc.). • Python program is written to accomplish patching.
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  Python Script forPatching
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  Output of Patching A196680 X 100 matrix. The matrix represents 9834 patches per patient.
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  Methods – DeepLearning with K-Means • To implement Deep learning, K-Means is used. • The K-Means algorithm clusters data by separating samples in N groups within cluster sum of squares. • Number of clusters must be specified.
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  Methods – DeepLearning with K-Means • For the purpose of this project, Scikit-Learn was used to implement the K-Means algorithm. • Scikit-Learn is open source software that provides libraries for machine learning in Python. • Using imported libraries like numpy, sklearn clusters, and sklearn metrics simple programs are written to handle the complexities of the algorithm. • For K-Means, the number of K clusters were K= 1000 and K=10000.
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  Python Program forK-Means
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  Output for K-Means Onefile contains a 196680 x 1 matrix. This represents the label for each patch. A second file contains a 1000 x 100 matrix. This is the distance of each data point to the centroid. In other words, the coordinates of the data points to each cluster.
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  Methods – Constructinga Distance Matrix • At this point, three files have been created from the original set, Patches, Centers, and Labels. • The next step is to construct a pairwise distance matrix. This will construct a file that contains the distance of every data point, in each patch, to every center cluster. • Python program is written with patch and center files as input.
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  Python Program forDistance Matrix
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  Output for DistanceMatrix A distance matrix 196680 x 1000 results.
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  Output for DistanceMatrix Screen Shot of word count for distance matrix output. Shows 196680 rows. When dividing 196680000 by the rows, the columns are obtained. In this case there are 1000 columns. Therefore, a 196680 x 1000 matrix resulted. This matrix represents the number of patches by the number of K centers.
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  Methods – ConstructingNew Feature Vector file • Now that the Distance Matrix is obtained, the final New Feature Vector file is created. • Since the matrix has196680 x K, the sum of each column for every 9834 rows needs to be obtained. 9834 rows represent 1 patient and should be represented in the final file. • A python program was written that uses the Distance Matrix file as input.
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  Python Program forNew Features
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  Output of NewFeatures A New Vector matrix results, 20 x 1000.
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  Output of NewFeatures Screen Shot of word count for new feature vector output. Shows 20 rows. When dividing 20000 by 20, the number of columns is obtained. In this case, the number of columns is 1000. This represents the original 20 patients with their new features, 1000, based on the patching, kmeans, pair wise, and summation methods that were applied.
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  Encountered Issues throughoutProject • Large files • Need for extra programs to download • When using Sci Kit, running normal K-means is extremely time consuming. Instead MiniBatchKmeans is used. • MBK makes subsets of input data, greatly reduces computation time. Performs only slightly worse. • Even with MBK, computation time for K=100000 is too long and was not used. • Still, long runtimes( k=1000 (~30 mins), k=10000 (~60mins), k=100000 (>16hours)). • Long runtimes even for simple scripts due to large data.
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  Comparison of Kmeansand MiniBatchKmeans Image and comparison from SciKit-Learn website.
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  Conclusions • The processwas completed twice, for K= 1000, and K = 10000. • Using deep learning ideas, a 20 x 491,774 data set was properly processed into a 20x1000 file and a 20x10000 file. • This significant reduction in file size saves memory. • Classification algorithms can implemented on the new files. Prediction Accuracy may be higher for the new files than raw data.
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  References and Acknowledgments •Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011. • Michael K. K. Leung, Hui Yuan Xiong, Leo J. Lee, and Brendan J. Frey Deep learning of the tissue-regulated splicing code Bioinformatics 2014 30: i121-i129. • Rasool Fakoor, Faisal Ladhak, Azade Nazi, Manfred Huber Using Deep Learning to enhance cancer diagnosis and classification Proceedings of the 30th International Conference on Ma- chine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28 Special Thanks to Mohammadreza Esfandiari, Payam, for guidance and assistance throughout project.