Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data.
About The Course
The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund project that was recently expanded to its second phase. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of genes, manipulation of the extracellular microenvironment conditions, i.e., growing cells on different surfaces, and more. These perturbations are applied to various types of human cells including induced pluripotent stem cells from patients, differentiated into various lineages such as neuron or cardiomyocytes. Then, to better understand the molecular networks that are affected by these perturbations, changes in levels of many different variables are measured including: mRNA, protein, and metabolites, as well as cellular phenotypic changes such as changes in cell morphology. In most cases, the data that is collected is genome-wide and from across different regulatory layers.
The BD2K-LINCS Data Coordination and Integration Center (DCIC) is commissioned to organize, analyze, visualize and integrate this with other publicly available relevant resources. In this course we will introduce the various Centers that collect data for LINCS, describing the experimental data procedures and the various data types. We will then cover the design and collection of meta-data and how meta-data is linked to ontologies. We will then describe data processing and data normalization methods to clean and harmonize LINCS data. This will follow a discussion about how the data is served as RESTful APIs and for this we will cover concepts from client-server computing. Most importantly, the course will focus on various methods of analysis include: clustering, gene-set enrichment analysis, Bayesian network analysis, and supervised machine learning application to LINCS data and other relevant data. The course will be taught by members of the Ma'ayan Lab at Mount Sinai, Medvedovic Lab at the University of Cincinatti, Schurer Lab at the University of Miami, and other members of the BD2K-LINCS Team as well as members of other BD2K and LINCS NIH funded centers.
Frequently Asked Questions
Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the course will receive a Statement of Accomplishment signed by the Course Director.
What are the pre-requisites for the class?
The course is designed to accommodate students from diverse backgrounds. Specifically, background in molecular biology, statistics, and computer science is most helpful, but such background is not assumed or required.
How difficult is the class?
The class can be easy if the student is only concerned with playing a relatively passive role. However, students are encouraged to engage in the course and take initiative and exercise their creativity. This may require more time and effort but would be more fun and rewarding.
Basic courses in statistics and molecular biology are useful but not required. Ability to write short scripts in languages such as Python would be useful but not necessary.