Data Analysis for Genomics will teach students how to harness the wealth of genomics data arising from new technologies, such as microarrays and next generation sequencing, in order to answer biological questions, both for basic cell biology and clinical applications.
The purpose of this course is to enable students to analyze and interpret data generated by modern genomics technology, specifically microarray data and next generation sequencing data. We will focus on applications common in public health and biomedical research: measuring gene expression differences between populations, associated genomic variants to disease, measuring epigenetic marks such as DNA methylation, and transcription factor binding sites.
The course covers the necessary statistical concepts needed to properly design experiments and analyze the high dimensional data produced by these technologies. These include estimation, hypothesis testing, multiple comparison corrections, modeling, linear models, principal component analysis, clustering, nonparametric and Bayesian techniques. Along the way, students will learn to analyze data using the R programming language and several packages from the Bioconductor project.
Currently, biomedical research groups around the world are producing more data than they can handle. The training and skills acquired by taking this course will be of significant practical use for these groups. The learning that will take place in this course will allow for greater success in making biological discoveries and improving individual and population health.
If you've ever wanted to get started with data analysis in genomics and you'd learn R along the way, this looks like a great place to start. The course is set to start sometime in "early 2014."
HarvardX: Data Analysis for Genomics