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Last updated:

May 17, 2022

Duration:

6 months

FREE

This course includes:

6 months

Badge on Completion

Certificate of completion

6 months

Description

Covers the basics of R software and the key capabilities of the Bioconductor project (a widely-used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology and rooted in the open-source statistical computing environment R), including importation and preprocessing of high-throughput data from microarrays and other platforms. Also introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of preprocessing and normalization, statistical inference, multiple comparison corrections, Bayesian Inference in the context of multiple comparisons, clustering, and classification/machine learning.

Course Objectives

Upon successful completion of this course, students will be able to: 1) Understand the basics of how microarray technology works; 2) Understand and critique existing methodology for the analysis of microarray data; 3) Write R code to import and analyze microarray data.

Readings

Bioinformatics and Computational Biology Solutions Using R and Bioconductor edited by Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit

Course Curriculum

    • Introduction to Molecular Biology and Array Technology 03:00:00
    • Introduction to Differential Expression 05:00:00
    • Probe Level Data and Background Adjustments 04:10:00
    • Normalization 05:00:00
    • Advanced Differentail Exprresion: Introduction to Emprical Bayes, Mutliple Comparasons 05:00:00
    • Distances, Clustering, and Prediction 05:00:00
    • Pre-processing Affymetrix GeneChips: Expression and SNP 05:00:00

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