Statistical Inference and Modeling for High-throughput Experiments
About this courseSkip 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.
At a glance
- Language: English
- Video Transcript: English
- Associated skills: Bayesian Statistics, Matrix Algebra, Functional Genomics, Next-Generation Sequencing, Statistics, Data Analysis, Statistical Inference, R (Programming Language), Software Engineering, Biology, Exploratory Data Analysis, Research, Linear Model, Statistical Modeling, Data Warehousing, DNA Sequencing, Bioconductor (Bioinformatics Software), Maximum Likelihood, Life Sciences
What you'll learnSkip What you'll learn
- Organizing high throughput data
- Multiple comparison problem
- Family Wide Error Rates
- False Discovery Rate
- Error Rate Control procedures
- Bonferroni Correction
- Statistical Modeling
- Hierarchical Models and the basics of Bayesian Statistics
- Exploratory Data Analysis for High throughput data
More about this courseSkip More about this course
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