Statistical Inference and Modeling for High-throughput Experiments from edX

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:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

What's inside

Learning objectives

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

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for learners in multiple testing, error rates, and error rate controlling procedures

Develops advanced statistical concepts, like hierarchical models and Bayesian Statistics

Emphasizes practical application of statistics to real-world problems in Genomics Data

Leverages R programming to enhance the connection between theoretical concepts and their implementation

Requires prerequisite knowledge in statistics and programming, which may pose a challenge for absolute beginners

Progresses in difficulty relatively quickly, making it demanding for learners with limited statistical background

Reviews summary

Well received stats course

Learners say this course is a well received introduction to statistical inference and modeling for high-throughput experiments. Reviewers say the material is presented in a way that's easy to understand even for those with a low knowledge of statistics.

Material is well organized

"(Note I took these before the recent reorganization. I believe most of the material from the first few courses has remained relatively the same.)"

Course concepts are easy to understand

"I cancelled because my knowledge in statistics was low and also my problems with R version installation did not allow me to complete the exercises"

"I have background programming & many IT years, math also... but during my first attempt it as impossible for me to understand the basic of 'why' are they doing this, 'why' are interpreting the results this way"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistical Inference and Modeling for High-throughput Experiments with these activities:

Review probability and linear algebra

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Refresh foundational knowledge in probability and linear algebra, which are essential prerequisites for the course, ensuring a strong foundation for understanding more advanced statistical concepts.

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Review notes from previous courses or textbooks.
Work through practice problems.
Attend review sessions if available.

Complete the R for Data Science tutorial

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Enhance proficiency in R programming, the primary software used in the course, to facilitate data analysis and modeling tasks.

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Access the R for Data Science tutorial.
Work through the interactive exercises.
Apply the concepts learned to the course assignments.

Review Introduction to Statistical Learning

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Provide a foundation in statistical concepts and methods, including linear models, regression, and classification, that will be used throughout the course.

View An Introduction to Statistical Learning: with... on Amazon

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Read chapters 1-3 of the book.
Work through the exercises in chapters 1-3.
Summarize the key concepts from each chapter.

Six other activities

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Solve practice problems on statistical concepts

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Reinforce understanding of statistical concepts by solving a variety of practice problems, building confidence in applying theory to practical scenarios.

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Review the lecture notes and textbook materials.
Attempt the practice problems at the end of each chapter.
Compare solutions with the provided answer key.

Create a cheat sheet on statistical formulas

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Summarize the key statistical formulas covered in the course, providing a quick reference for use during and after the course.

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Review the statistical formulas covered in the course.
Organize the formulas into a logical order.
Create a visually appealing cheat sheet.

Collect and analyze a small dataset

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Apply the statistical concepts and methods learned in the course to a real-world dataset, reinforcing understanding and building practical skills.

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Identify a dataset of interest.
Import the dataset into a statistical software package.
Explore the data using descriptive statistics and visualizations.
Fit a simple statistical model to the data.
Interpret the results of the model.

Create a comprehensive course study guide

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Organize and consolidate course materials into a single, comprehensive document, facilitating effective review and preparation for assessments.

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Gather lecture notes, slides, and assignments.
Summarize key concepts and formulas.
Include practice questions and solutions.

Attend a workshop on statistical modeling

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Gain exposure to advanced statistical modeling techniques and best practices from experts in the field, complementing the theoretical foundations covered in the course.

Browse courses on Statistical Modeling

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Identify relevant statistical modeling workshops.
Register and attend the workshop.
Actively participate in the discussions.

Contribute to an open-source statistical software project

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Deepen understanding of statistical software development and contribute to the broader statistical community, enhancing technical skills and fostering collaboration.

Browse courses on Open-Source Software

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Identify open-source statistical software projects.
Select a project to contribute to.
Submit bug reports, feature requests, or code contributions.

Career center

Learners who complete Statistical Inference and Modeling for High-throughput Experiments will develop knowledge and skills that may be useful to these careers:

Biostatistician

A Biostatistician designs and analyzes statistical studies for biological, medical, and public health research. They use statistical methods to analyze data, draw conclusions, and make predictions. This course can help you become a Biostatistician by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Biostatistician

Data Analyst

A Data Analyst collects, cleans, and analyzes data to extract meaningful insights. They use statistical methods and techniques to identify trends, patterns, and relationships in data. This course can help you become a Data Analyst by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Data Analyst

Statistician

A Statistician collects, analyzes, interprets, and presents data. They use statistical methods and techniques to solve problems and make informed decisions. This course can help you become a Statistician by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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Bioinformatician

A Bioinformatics Engineer designs and develops software tools and algorithms for analyzing and interpreting biological data. They use statistical methods and techniques to identify patterns and trends in data. This course can help you become a Bioinformatician by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Bioinformatician

Machine Learning Engineer

A Machine Learning Engineer designs and develops machine learning models to solve problems and make predictions. They use statistical methods and techniques to train and evaluate models. This course can help you become a Machine Learning Engineer by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

A Research Scientist conducts research to advance knowledge and understanding in a particular field. They use statistical methods and techniques to analyze data and draw conclusions. This course can help you become a Research Scientist by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Research Scientist

Quantitative Analyst

A Quantitative Analyst uses statistical methods and techniques to analyze financial data and make investment decisions. They use statistical methods and techniques to identify trends, patterns, and relationships in data. This course can help you become a Quantitative Analyst by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. They use statistical methods and techniques to identify trends, patterns, and relationships in data. This course can help you become a Data Scientist by providing you with a strong foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It can also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, and maintains software systems. They use statistical methods and techniques to analyze data and design algorithms. This course may help you become a Software Engineer by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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Actuary

An Actuary uses statistical methods and techniques to assess risk and uncertainty. They use statistical methods and techniques to analyze data and make predictions. This course may help you become an Actuary by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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Epidemiologist

An Epidemiologist investigates the causes and distribution of disease in populations. They use statistical methods and techniques to analyze data and make predictions. This course may help you become an Epidemiologist by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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Financial Analyst

A Financial Analyst uses statistical methods and techniques to analyze financial data and make investment decisions. They use statistical methods and techniques to identify trends, patterns, and relationships in data. This course may help you become a Financial Analyst by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Financial Analyst

Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. They use statistical methods and techniques to analyze data and make predictions. This course may help you become a Market Researcher by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

An Operations Research Analyst uses statistical methods and techniques to solve problems in business and industry. They use statistical methods and techniques to analyze data and make recommendations. This course may help you become an Operations Research Analyst by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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Risk Manager

A Risk Manager uses statistical methods and techniques to assess risk and uncertainty. They use statistical methods and techniques to analyze data and make recommendations. This course may help you become a Risk Manager by providing you with a foundation in statistical methods and concepts, including multiple comparison problems, error rate controlling procedures, false discovery rates, and q-values. It may also introduce you to statistical modeling and how it is applied to high-throughput data, such as next generation sequencing and microarray data.

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