Transcriptomics 3
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.
Course Features
- Lectures 14
- Quizzes 2
- Duration 6 hours
- Skill level All levels
- Language English
- Students 403
- Certificate Yes
- Assessments Yes
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Data Details and Pre-Processing
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Clustering: Unsupervised Analysis
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Classification: Supervised Analysis
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SHREYESHI DHAR
Introduction to Machine learning
Transcriptomics 3 encompasses the concepts of unsupervised and supervised analysis of biological data. This course contains explanations of various ML algorithms such as LDA,swLDA and SVM. We also learn about the development of classifiers from known samples to classify unknown samples. The course allows us to perform the hands-on practical on the t-bio-server platform where we can deal with a real world biological data for preparatory and other additional analysis.
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Ayushman KB
Put on your Machine Learning goggles!
Transcriptomics-3 offers corresponding examples to run on the t-bio server platform along with great conceptual explanations to ML algorithms, such as; LDA, swLDA, and SVM. The course covers both supervised & unsupervised learning algorithms and uses an actual cancer-dataset to perform exploratory and predictive analysis on the same.
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Pratim Chakraborti
The ML for Biological Data
This course culminates into the actual algorithms of Machine Learning which when applied sequentially gives meaningful insight from the data. The carefully curated quiz acts as a benchmark to the learning process.
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sahil
Highly recommended
This course gave an detailed knowledge about clustering and the classification methods using a biological example from the publication .
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Despoina
Introduction to ML
Learn to analyze your transcriptomics data using Machine Learning techniques effectively!
4 Comments
This course culminates into the actual algorithms which when applied sequentially gives meaningful insight from the data. The carefully curated quiz acts as a benchmark to the learning process.
Great course with so much hands-on Machine learning applications.
Brilliant!!!! experience to be gained. It’s a very nicely arranged and curated curriculum, gives a great opportunity to learn the post-processing methods for analysis of the data from the statistical concepts to machine learning algorithms. It consist of informative videos to make the concepts crystal clear for the learner and quiz is also very apt. Feeling happy to learn a lot from this course.
It was a great course. Has some good insights into different approaches one can take for grouping samples using gene expression data. Definitely recommended for a beginner.