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
|8/29 (Thur)||Lecture 1: Introduction to high-throughput data analysis (Wu) [Notes]||Course information. Introduction to high-throughput data. Feature selection from high-throughput data.||9/5 (Thur)||Lecture 2: Introduction to Proteomics and Metabolomics (Yu) [Notes]||Basic concept of metabolomics and proteomics, LC/MS technology, challenges in data processing.||9/12 (Thur)||Lecture 3: Analyzing data from capture-based next generation sequencing assays (Qin) [Notes]||An overview of capture-based assays that use next generation sequencing technologies, including ChIP-seq, ATAC-seq and Hi-C, with examples of statistical methods for analyzing data produced from such experiments||Reading assignment 1: (a) NGS review, (b) DE detection with DESeq||9/19 (Thur)||Lecture 4: Basic concepts of molecular genetics and population genetics (Hu) [Notes]||Brief introduction to the biological backgrounds needed for statistical genetics, concepts from population genetics that are most relevant to association analysis.||9/26 (Thur)||Lecture 5: Microbiome (Hu) [Notes]||Statistical method and data analysis for microbiome data.||Reading assignment 2: paper 1, paper 2, paper 3||10/3 (Thur)||Lecture 6: Introduction to biomedical imaging (Guo) [Notes]||Introduction of imaging techniques, various imaging modalities and data acquisition and structure.||10/10 (Thur)||Lecture 7: Statistical Analysis of Neuroimaging Data (Risk) [Notes]||An overview of the analysis of neuroimaging data with a focus on functional magnetic resonance imaging (fMRI), including experimental design, data processing, and statistical analysis. We will also discuss accelerated acquisition techniques and the statistical implications.||10/17 (Thur)||Lecture 8: Brain Network estimation (Kundu) [Notes]||Discussion on some Integrative methods for brain network estimation which fuses data across multiple imaging modalities or multiple fMRI experiments to obtain greater reliability in estimating brain networks.||Reading assignment 3: paper 1, paper 2|