GitHub - dmontielg/Machine-Learning: Transcriptome variation between different brain tissues. For this analysis RNA sequencing data is used which contains 1259 samples and 10.574 genes. The data is generated by the Genotype Tissue Expression consortium.

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Transcriptome variation between different brain tissues. For this analysis RNA sequencing data is used which contains 1259 samples and 10.574 genes. The data is generated by the Genotype Tissue Expression consortium.

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1.supervised_methods.R		1.supervised_methods.R
2.classification.R		2.classification.R
3.pca.R		3.pca.R
README.md		README.md
expr4T.dat		expr4T.dat

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The goal of this project is to look for transcriptome variation between different brain tissues. For this analysis RNA sequencing data is used which contains 1259 samples and 10.574 genes. The data is generated by the Genotype Tissue Expression consortium.

Wageningen University and Research 2017 MBF Algorithms in Bioinformatics

We used different methonds including Supervised and Unsupervised Learning such as LDA, KNN, SVM, PCA among others.

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Transcriptome variation between different brain tissues. For this analysis RNA sequencing data is used which contains 1259 samples and 10.574 genes. The data is generated by the Genotype Tissue Expression consortium.

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