Web site for this course

https://dputhier.github.io/ASG/

Teachers

Abbrev Name
DP Denis Puthier
JvH Jacques van Helden

Year

2016-2017

Audience

  • 2ème année du Master en Bioinformatique, biochimie structurale et génomique (BBSG).
  • Ecole doctorale

Travail personnel / Personal work

The instructions for the 2016 personal work can be found here

Resources

Tool About
R A free software environment for statistical computing and graphics
R markdown Documentation for R markdown language (used for the practicals)
Bioconductor A set of R libraries dedicated to statistical analysis of genomics data.
MeV: MultiExperiment Viewer A Java application designed to allow the analysis of expression data
Cluster 3.0 Implements the most commonly used clustering methods for gene expression
java Treeview Java-based tool to visualize trees prodced by hierarchical clustering togeter with a heatmap with expression proviles.

Schedule

Date From To Subject Teacher Concepts Material
Fri 04/11 14:00 18:00 Detecting differentially expressed genes (DEG) with microrarrays DP - Hypothesis testing
- Student \(t\) statistics
- Unbiased estimation of variance
- MA plot
- Volcano plots
-P-value distribution
- E-value
- Basics about Student and Welch’s t test
- Detecting differentially expressed genes in microarray data
Mon 07/11 14:00 18:00 Functional enrichment of DEG DP - Functional enrichment statistics
- The hypergeometric distribution
- Theory : on white board
- Hypergeometric distribution and enrichment statistics. An example application: DAVID (practical)
Tue 08/11 14:00 18:00 The multiple ways to correct for multiple testing JvH - False positive risk (FPR)
- Expected number of false positives (E-value)
- Family-Wise Error Rate (FWER)
- False Discovery Rate (FDR)
- Multiple testing corrections (slides)
- Multiple testing corrections (practical)
Tue 15/11 14:00 18:00 Supervised classification JvH - Discriminant analysis
- Cross-validation (k-fold, LOO)
- Data dimensionality and overfitting
- Variable selection
- Introduction to multivariate analysis
- Discriminant analysis (slides)
- Dimension reduction and PCA
- Practical: supervised classification
Mon 12/12 09:00 13:00 Overview of discrete distributions, with applications to NGS JvH - Geometric
- Binomial
- Poisson
-Hypergeometric
- Negative binomial
Tutorial [html][pdf][Rmd]
Mon 12/12 14:00 18:00 Detecting differentially expressed genes (DEG) with RNA-seq DP/JvH - RNA-Seq principles
- Normalizing RNA-seq counts
-Detecting differentially expressed genes (DEG)
- RNA-Seq DEG with DESeq2 [html] [pdf] [Rmd]
Wed 14/12 09:00 13:00 RNA-seq analysis (pursued) JvH []
[]
Wed 14/12 14:00 18:00 Preparation of the personal work DP Homework: analysis of psoriasis data [html] [Rmd] [bib]

Site content

Concept Description
Introduction to R - First steps with R and Siméon Denis Poisson (practical)
- R language: A quick tutorial (practical)
- R language: A quick tutorial (practical)
Occurrence statistics - The Poisson distribution in the context of Peak-calling (practical)
- The Poisson distribution in the context of k-mers occurence statistics (practical)
- Read mapping statistics and the binomial distribution (practical)
- Hypergeometric distribution and enrichment statistics. An example application: DAVID (practical)
- Application example: K-mer occurrences in ChIP-seq peaks (practical)
Microarray analysis - Introduction to multivariate analysis (slides)
- Transcriptome microarrays: study cases (slides)
- Normalization of Affymetrix DNA chip (slides) -
- Handling and normalizing affymetrix data with bioconductor (practical)
- Differential_expression (slides)
- Basics about Student and Welch’s t test
- Microarray data: selecting differentially expressed genes with R or TmeV (practical)
- Sampling distributions (practical) - Detecting differentially expressed genes in microarray data. Part I: exploring Student t statistics
The multiple ways to correct multiple testing - Multiple testing corrections (slides)
- Multiple testing corrections (practical)
Clustering (unsupervised classification) - Correlation analysis (slides)
- Clustering (slides)
- Clustering (slides DP)
- About distances
- Handling clustering methods: artificial datasets (practical)
- Clustering of microarray data
RNA-seq data analysis - RNA-Seq method (slides)
- RNA-seq read mapping (practical)
- The negative binomial and DESeq bioC package
- The negative binomial and DESeq bioC package
Supervised classification - Introduction to multivariate analysis
- Discriminant analysis (slides)
Visualization - Dimension reduction and PCA
Overview - Discrete distributions for NGS data analysis