Biology & Life Sciences Basics
Fundamental principles and concepts
HarvardX: Statistical Inference and Modeling for High-throughput Experiments
A focus on the techniques commonly used to perform statistical inference on high throughput data.
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Course Insight
Suitable for intermediate learners. Works well as a continuation after mastering Biology & Life Sciences fundamentals. It bridges the gap toward advanced, production-level engineering.
Intermediate FriendlyCertification IncludedSelf-Paced Learning
SKILLS TO
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Practical ApplicationTrending
Real-world project implementationBest Practices
Industry standard workflows and guidelinesCareers:Data Scientist, Data Analyst, Machine Learning Engineer.
Quick Facts
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What You’ll Learn
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 Beginner 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.
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Outcomes
- Organizing high throughput data.
- Multiple comparison problem.
- Family Wide Error Rates.
- False Discovery Rate.
- Error Rate Control procedures.
- Bonferroni Correction.
- q-values.
- Statistical Modeling.
- Hierarchical Models and the basics of Bayesian Statistics.
- Exploratory Data Analysis for High throughput data.
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