News: Course: Applied Statistics and Bioinformatics with R and Bioconductor. (Berlin, 22-26 January 2018)
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19 months ago by
Berlin
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Applied Statistics and Bioinformatics with R and Bioconductor


Dates

22-26 January 2018


Where

Berlin


Overview

This course will provide biologists and bioinformaticians with practical statistical and data analysis skills to perform rigorous analysis of high-throughput biological data. The course assumes some 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-dimensional data generated by high-throughput sequencing, including: exploratory data analysis, principal components analysis, unsupervised clustering, batch effects, linear modeling for differential expression, gene set analysis


Preparation

Come to the first class with the following installed:

● R and Bioconductor: www.bioconductor.org/install

● R Studio: https://www.rstudio.com/products/rstudio/download3/

● Github desktop client (or any other Github client): https://desktop.github.com/

Additionally, please create an account at www.github.com, and use it to introduce yourself at https://github.com/waldronlab/AppStatTrento/issues.


Labs

Each day will include a hands-on lab session, that students should attempt and hand in before the following day by committing to the course Github repository. A selection of labs will be reviewed the following day.


Program


Monday 22nd – Classes from 09:30 to 17:30

Session 1 – Introduction

Lecture 1: Data distributions

● random variables

● distributions

Lecture 2: Statistical inference and sampling

● populations and samples

● Central Limit Theorem

● t-distribution

Lab 1: Introduction to R and Bioconductor

Lab 2: Creating graphics


Tuesday 23nd – Classes from 09:30 to 17:30

Session 2– Hypothesis testing

Lecture 1: hypothesis testing concepts

● type I and II error and power

● confidence intervals

● multiple hypothesis testing: false discovery rate, familywise error rate

Lecture 2: hypothesis testing in practice

● hypothesis tests for categorical variables (chi-square, Fisher's exact)

● Monte Carlo simulation

● permutation tests

● bootstrap simulation

● exploratory data analysis

Lab: bootstrap simulation and permutation tests


Wednesday 24th – Classes from 09:30 to 17:30

Session 3 - Linear modeling

Lecture 1: linear modeling

● linear regression and multiple regression

● model matrix and model formulae

Lecture 2: generalized linear models for count data

● intro to generalized linear models

● logistic regression and log-linear models

● Poisson and Negative Binomial error models

● Zero-inflated models

Lab: RNA-seq differential expression workflow


Thursday 25th – Classes from 09:30 to 17:30

Session 4 - Unsupervised methods

Lecture 1: distances and PCA

● distance in high dimensions

● singular value decomposition

● principal components analysis and multidimensional scaling

Lecture 2: unsupervised clustering

● unsupervised clustering

● batch effects

Lab 1: applications of unsupervised methods to shotgun metagenomics microbiome data analysis

Lab 2: option to work on students’ own data.


Friday 26th – Classes from 09:30 to 17:30

Session 5 - Gene set and multi-omics data analysis

Lecture 1 - gene set enrichment analysis

● background on gene set testing

● types and interpretations of gene set tests

● advantages and pitfalls of gene set testing

Lab 1 - gene set analysis with applications to gene expression and multi-omics experiments

Lab 2 - multi-omics data analysis

Lab 3 - option to work on students’ own data.


For more information about the course, please visit our WEBSITE

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