<p>Lecture, two hours; discussion, two hours. Study covers specific basic statistical concepts required to be successful modern molecular biologist, and applies these tools to analyze real-world datasets, such as RNA sequencing. Topics include distributions, confidence intervals, and p-values; linear and logistic statistical models; high dimensional summaries through unsupervised learning (e.g., PCA and clustering); and permutation-based statistical tests. Analysis carried out using statistical programming language R. Study includes lectures, R how-to sessions, and student presentations. Letter grading.</p>

Molecular Biology

College of Letters and Science

MOL BIO 254D - Concepts in Molecular Biosciences: Statistical Tools and Concepts for Molecular Biologists

Concepts in Molecular Biosciences: Statistical Tools and Concepts for Molecular Biologists

Obtain summaries of data using numbers and graphs including confidence statements about the
data (confidence intervals, p-values, etc.)

Perform dimensionality reduction and clustering in R

Summarize observations with many attributes (high dimensional) using unsupervised learning, i.e.,
principal component analysis (PCA), non-linear dimensionality reduction, and clustering

Practical data analysis in the statistical programing language R

Recognize statistical models, what they are, why we need them.

General Catalog

Concepts in Molecular Biosciences: Statistical Tools and Concepts for Molecular Biologists

MOL BIO 254D

3 units