Statistical Inference and Modeling for High-throughput Experiments
About this Course
In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation. Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. These courses make up two Professional Certificates and are self-paced: Data Analysis for Life Sciences: PH525.1x: Statistics and R for the Life Sciences PH525.2x: Introduction to Linear Models and Matrix Algebra PH525.3x: Statistical Inference and Modeling for High-throughput Experiments PH525.4x: High-Dimensional Data Analysis Genomics Data Analysis: PH525.5x: Introduction to Bioconductor PH525.6x: Case Studies in Functional Genomics PH525.7x: Advanced Bioconductor This class was supported in part by NIH grant R25GM114818.Created by: Harvard University
Level: Intermediate
Related Online Courses
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical... more
This proctored examination assesses all concepts, methods and techniques introduced across the following four courses within the LSE MicroBachelors program in Statistics Fundamentals: Statistics 1:... more
Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in thi... more
Demystify complex big data technologies Compared to traditional data processing, modern tools can be complex to grasp. Before we can use these tools effectively, we need to know how to handle big... more
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you... more