Transcriptomics 3 is a continuation of Transcriptomics 1 and 2. In Transcriptomics 1, we learned how to convert raw reads from a Next Generation Sequencer into a table of expression and visualize the table of expression using PCA, which we then used to develop hypotheses. In Transcriptomics 2, we looked at statistical methods for determining differentially expressed elements in known groups of samples. We explored Student’s t-test, Bayesian methods such as Deseq and EdgeR, and Factor Regression Analysis to dissect the influence of multiple influential factors. Here, we will explore different methods for identifying groups of samples without prior knowledge (clustering) and then examine methods for developing classifiers from known samples to classify unknown samples.

We will be inquiring into the clustering and the classification methods using a biological example from the publication Daemen et al. (Modeling precision treatment of breast cancer, Genome Biol. 2013. We will not repeat the analysis presented in the paper; rather we will re-analyze data from the paper and consider the differences between our own analysis and those of the authors.

Passing Grade & Criteria
70% is the minimum score to pass
50% : Total quizzes
50% : Course project

After completing the theoretical and practical assignments, you will receive a certificate of completion supported by Pine Biotech and the Tauber Bioinformatics Research Center.

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