News: Workshop: Data analysis in high throughput biology. 29 May-2 June 2017 in Berlin
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24 months ago by
Berlin
carlopecoraro2840 wrote:

Data analysis in high throughput biology

This worlshop will run from the 29th of May to the 2nd of June 2017 at the Botanischer Garten und Botanisches Museum Berlin Freie Universität Berlin Königin-Luise-Straße 6-8 14195 Berlin (https://www.physalia-courses.org/courses/course3/).

Instructor: Dr. January Weiner (Max Planck Institute for Infection Biology; https://www.physalia-courses.org/instructors/t14/).

Course overview

High throughput (HT) techniques such as transcriptomics or metabolomics are of great significance in many areas of biology. With the standard techniques becoming more affordable and new techniques being introduced all the time, the amount of data sets generated is staggering. However, statistical and computational analysis of HT data sets present many challenges. In this course, the students will gain the ability to independently process and analyse HT data sets, select the appropriate tools, functionally interpret the results as well as learn the paradigms of computational biology and statistics which will allow them to efficiently communicate with computational biologists. Intended audience

In general, the course is aimed at biologists who would like to take their data analysis in their own hands. While an aptitude for computational work is necessary, the main goal of the course is the application of biological and statistical knowledge to HT sets with as little effort as necessary.

basic computer skills (a rudimentary knowledge of programming principles in any language is recommended, but not mandatory)
basic understanding of statistics
basic understanding of molecular techniques for generating high throughput data

The students should be comfortable with using a computer and have at least a rudimentary understanding of computer programming. However, no specific skills are necessary; the students will learn basic R programming in this course.

Basic skills in statistics are necessary. The students should understand the concepts of statistical hypothesis testing and p-values. However, an in-depth introduction to these concepts will also be provided. Target student skills

overview of commonly used high-throughput data types
techniques for data clean-up and preparation for analysis
understanding of computational problems associated with high-throughput data analysis
statistical problems and solutions in analysis of HT data
practical skills in analysis methods of HT data:
    basic differential analysis (limma, DESeq, alternative and non-parametric techniques)
    set enrichment techniques (GSEA, gene ontologies, metabolic profiling and more)
    multivariate approaches to data analysis (PCA / ICA, PLS, multiple correspondance analysis)
    basic approaches in machine learning: cross
Communication skills in statistics and computational biology

After the course, the student should be able to prepare, analyse and interpret a HT data set, including multivariate and machine learning techniques. Teaching format

On each day, the course will consist of four parts:

Lecture: theoretical introduction to the days focus
Hands-on guide: guided practical session in R where students replicate the analysis performed by the teacher. While the lecture is general, here specific R techniques and R packages are introduced
Guided self-study: students are given excercises and problems to solve and work on them individually under the guidance of the teacher
Individual project work: each student will receive a transcriptomic (RNASeq or microarray) data set to analyse throughout the course
Lecture: wrap-up and side notes; preparation for the following day

Course plan

Day 1: Introduction to statistical reasoning and R 
    Lecture: "Statistics gone wrong: basics of statistical problems in HT applications"
    Hands-on guide: working with R: first steps
    Guided self-study: using R for data loading and basic statistical calculations
    Individual project work: loading data for the individual project
    Lecture: "On the importance of lab books - documentation and organization in computational projects"


Day 2: Data preparation and basic differential analyses
    Lectures:
        “Steps in HT data analysis and overview of HT techniques”
        “Differential analysis in transcriptomics”
    Hands-on guide: documentation with knitr, data pre-processing, QC
    Guided self-study: creating self-documenting R code; basic steps in transcriptomic analyses
    Individual project work: basic analysis of the individual data sets
    Lecture: "So you have a list of thousand gene names: why do we do HT analyses?"


Day 3: Functional analysis, gene set enrichments and biological interpretation
    Lecture: "Methods of functional analysis in gene set enrichment analyses"
    Hands-on guide: gene set enrichment techniques in R
    Guided self-study: comparing results of different gene set enrichment techniques
    Individual project work: biological interpretation of the results
    Lecture: "Common mistakes in functional analysis of HT data"


Day 4: Machine learning and multivariate approaches
    Lecture: "Introduction to multivariate approaches and ML techniques"
    Hands-on guide: Practical guide to multivariate and ML techniques in R
    Individual project work
    Lecture: "How to know when you are done?"


Day 5: Evaluation of individual project
    Individual work on project reports
    Project report evaluation
    Discussion round
    Lecture: "Course wrap-up: where to go from here?"

Further information:

There two packages available: 1) “only-course” costs 430 euros (VAT included), which includes refreshments and course material; 2) “all-inclusive” costs 695 euros (VAT included), which includes refreshments, course material, accommodation and meals (breakfast, lunch, dinner).

Registration deadline: April 24th, 2017 (https://www.physalia-courses.org/courses/course3/)

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