SO Training: Statistics for Bioinformatics
Date: 02/Jun/2014 Time: 08:00 - 16/Jun/2014 Time: 15:00
Place: Meeting room: Sala de Juntes
Building:B6
Campus Nord
Jordi Girona, 31
08034 Barcelona
Building:B6
Campus Nord
Jordi Girona, 31
08034 Barcelona
Target group: The course is designed and expected to be of the interest for any of the scientific areas covered by the BSC, and is not focused to the solving of specificbioinformatics problems. We, therefore, think it is a good opportunity to both refresh and to improve our skills in statistics, and to provide novel methodologies that could be useful for many of the problems that include a large number of variables.
Cost: There is no registration fee.
Primary tabs
Day 1: Introduction to applied statistics with R (2nd June, 8.30h-14.30h)
a) Parametric and non-parametric methods
b) Linear models
c) Non-Linear Models
Day 2: Methods for dimension reductionality (10th June, 14.00h-20.00h)
a) Overview of clustering algorithms: supervised and non-supervised methods
1) Hierarchical methods
2) Partitioning methods
3) Model-based methods and Variable selection
4) Selection of optimal number of clusters
5) Accuracy of unsupervised methods
b) Overview of multivariate methods
1) Principal component analysis
2) Discriminant analysis
3) Multidimensional scaling
Day 3: Graphics and data visualization with R (16th June, 8.30h-14.30h)
a) Graphics environments
1) par and layout
2) lattice
3) ggplot
b) Special graphs
1) Venn diagrams
2) Compounds depictions
c) Genome Graphics
1) Manhattan plot
2) Chromosome ideograms
3) Circos plot
d) Heatmaps
a) Parametric and non-parametric methods
b) Linear models
c) Non-Linear Models
Day 2: Methods for dimension reductionality (10th June, 14.00h-20.00h)
a) Overview of clustering algorithms: supervised and non-supervised methods
1) Hierarchical methods
2) Partitioning methods
3) Model-based methods and Variable selection
4) Selection of optimal number of clusters
5) Accuracy of unsupervised methods
b) Overview of multivariate methods
1) Principal component analysis
2) Discriminant analysis
3) Multidimensional scaling
Day 3: Graphics and data visualization with R (16th June, 8.30h-14.30h)
a) Graphics environments
1) par and layout
2) lattice
3) ggplot
b) Special graphs
1) Venn diagrams
2) Compounds depictions
c) Genome Graphics
1) Manhattan plot
2) Chromosome ideograms
3) Circos plot
d) Heatmaps