Statistical Inference and Modeling for High-Throughput Data

High-throughput technologies generate vast amounts of biological data, but extracting meaningful scientific insights requires rigorous statistical discipline. This text-based course guides you through the essential mathematical concepts and modeling techniques needed to analyze large-scale datasets without getting lost in the noise. You will learn how to make reliable statistical inferences while accounting for the unique challenges of high-dimensional biological data. By completing this course, you will transition from basic statistical concepts to confidently designing, modeling, and interpreting high-throughput experiments. You will develop a strong foundation in handling multiple comparisons, estimating variance, and fitting linear models to complex genomic and molecular data. What you'll learn: - Understand the foundational concepts of statistical inference, hypothesis testing, and p-values in biological contexts. - Master error-rate control methods, including Family-Wise Error Rate and False Discovery Rate adjustments. - Apply linear models and regression techniques to isolate biological signals from experimental noise. - Learn modern empirical Bayes methods to stabilize variance estimation in small-sample high-throughput designs. - Practice exploratory data analysis and diagnostic plotting to assess model fit and identify confounding factors. We begin with a clear introduction to key terminology, probability distributions, and the core principles of statistical testing. From there, you will progress step-by-step through advanced modeling techniques, multiple testing corrections, and practical estimation strategies, all explained through clear written text and illustrative code snippets. This course is designed for beginners in bioinformatics, life science researchers, and data analysts who want to build a solid mathematical foundation for genomic and high-throughput data analysis. No advanced statistical background is required to start. Start your journey toward mastering high-throughput data analysis today.