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Course -Genomics with R and Bioconductor - Berlin 16-20 September 2019

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  • Course -Genomics with R and Bioconductor - Berlin 16-20 September 2019

    Dear all,

    still a few places on our course " Genomics with R and Bioconductor"

    Where: Free University (FU) Berlin (Germany)

    When: 16-20 September 2019

    Instructor: Dr. Ludwig Geistlinger - CUNY Graduate School of Public Health and Health Policy, New York (USA)

    Registration deadline: August 20th

    Course:

    This course will provide biologists and bioinformaticians with practical statistical analysis skills to perform rigorous analysis of high-throughput genomic data. The course assumes basic familiarity with genomics and with R programming, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-throughput data generated by next-generation sequencing, including: exploratory data analysis, principal components analysis, clustering, differential expression, and gene set analysis.

    Programme:

    Session 1 – Introduction

    Monday - 09:30 to 17:30

    Lecture 1: Data distributions

    random variables
    distributions
    population and samples

    Hands-On 1: Introduction to R

    Lecture 2: Creating high-quality graphics in R

    Visualizing data in 1D, 2D & more than two dimensions
    Heatmaps
    Data transformations

    Hands-On 2: Graphics with base R and ggplot2

    Session 2 – Hypothesis testing

    Tuesday - 09:30 to 17:30

    Lecture 1: Hypothesis testing theory

    type I and II error and power
    multiple hypothesis testing: false discovery rate, familywise error rate
    exploratory data analysis (EDA)

    Hands-On 1: Standard tests & EDA

    Lecture 2: Hypothesis testing in practice

    hypothesis tests for categorical variables (chi-square, Fisher's exact)
    Monte Carlo simulation
    Permutation tests

    Hands-On 2: Permutation tests

    Session 3 - Bioconductor

    Wednesday – Classes from 09:30 to 17:30

    Lecture 1: Introduction to Bioconductor

    Incorporating Bioconductor in your data analysis
    ExpressionSet / SummarizedExperiment
    Annotation resources

    Hands-On 1: Leveraging Bioconductor annotation resources

    Lecture 2: Genomic intervals

    Introduction to genomic region algebra
    Basic operations: construction, intra- and inter-region operations
    Finding overlaps

    Hands-On 2: Solving common bioinformatic challenges with GenomicRanges

    Session 4 - Next-generation sequencing data

    Thursday - 09:30 to 17:30

    Lecture 1: High-throughput count data

    Characteristics of count data
    Exploring count data
    Modeling count data

    Hands-On 1: Analyzing next-generation sequencing data

    Lecture 2: Clustering and Principal Components Analysis

    Measures of similarity
    Hierarchical clustering
    Dimension reduction
    Principal components analysis (PCA)

    Hands-On 2: Clustering & PCA

    Session 5 - Differential expression and gene set analysis

    Friday - 09:30 to 17:30

    Lecture 1 - Differential expression analysis

    Normalization
    Experimental designs
    Generalized linear models

    Lab 1: Performing differential expression analysis with DESeq2

    Lecture 2 - Gene set analysis

    A primer on terminology, existing methods & statistical theory
    GO/KEGG overrepresentation analysis
    Functional class scoring & permutation testing
    Network-based enrichment analysis

    Lab 2: Performing gene set enrichment analysis with the EnrichmentBrowser

    For the full list of our courses and Workshops, please see: https://www.physalia-courses.org/courses-workshops

    Should you have any questions, please feel free to contact us

    Thanks and best regards,

    Carlo

    Carlo Pecoraro, Ph.D

    Physalia-courses DIRECTOR

    [email protected]

    http://www.physalia-courses.org/

    Twitter: @physacourses

    mobile: +49 17645230846

    https://groups.google.com/forum/#!fo...ysalia-courses
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