Introduction to Large-Scale Biomedical Data Analysis

Class Information


This is an overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics.

This course will be co-taught by several BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and active areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.

Prerequisites: BIOS 501 or equivalent, or permission from the instructor.


Class schedule and notes

Date Lecture Title Description Homework
8/24 (Thur) Lecture 1: Introduction to next generation sequencing data (1) (Wu) [Notes] Course information. Introduction to next generation sequencing technology. RNA-seq: biological motivations, experimental procedure, challenges and opportunities in statistical data analysis.
8/31 (Thur) Lecture 2: Introduction to next generation sequencing data (2) (Qin) [Notes] ChIP-Seq: experimental procedure and statistical data analysis.
9/7 (Thur) Lecture 3: Introduction to Proteomics and Metabolomics (Yu) [Notes] LC/MS technology, challenges in data processing. Biological pathways Reading assignment 1: (a) NGS review, (b) DE detection with DESeq
9/14 (Thur) Lecture 4: Introduction to biomedical imaging (Guo) [Notes] An overview of imaging techniques, acquisition methods and data structures/characteristics for different imaging modalities.
9/21 (Thur) Lecture 5: Statistical Analysis of Neuroimaging Data (Guo) [Notes] Data processing techniques, study designs, analysis strategies, research questions and goals.
9/28 (Thur) Lecture 6: Role of Brain Networks in Imaging Genetics (Kundu) [Notes] The importance of brain networks in differentiating between healthy and mentally ill subjects, methods on how to estimate the brain network which may or may not rely on additional clinical, demographic and genetic information. Reading assignment 2
10/5 (Thur) Lecture 7: Basic concepts of molecular genetics and population genetics (Hu) [Notes] Brief introduction to the biological backgrounds needed for statistical genetics, concepts from population geneticsĀ that are most relevant to association analysis.
10/12 (Thur) Lecture 8: Microbiome (Hu) [Notes] Statistical method and data analysis for microbiome data. Reading assignment 3: paper 1, paper 2, paper 3