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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
diff --git a/intro_ComBiol_20172018.Rmd b/intro_ComBiol_20172018.Rmd
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+---
+title: "Computational Biology"
+author: "michael.blum@univ-grenoble-alpes.fr"
+date: "`r Sys.Date()`"
+output:
+ ioslides_presentation:
+ css: style.css
+---
+
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(fig.align="center",echo = TRUE)
+options(Encoding="UTF-8")
+```
+
+## Schedule
+###First part of the course (michael.blum@univ-grenoble-alpes.fr) is about how to build phylogenetic trees based on DNA sequences. You will have to work, not me!
+
+- October 3, 09h45-12h45, D208: Introduction and mathematical computations
+- October 10, 09h45-12h45, E300: Mathematical computations, practical in R, and report writing
+- October 17, 09h45-12h45, E300: Practical in R, and report writing
+- October 20: Deadline to submit your report
+
+
+###Second part of the course (Olivier François)
+
+## An example of Phylogeny: Hominidae
+```{r, echo=FALSE,out.width = "500px"}
+knitr::include_graphics("tree_primates.jpg")
+```
+
+## A second example of phylogeny: US criminal case: State of Washington v Anthony Eugene Whitfield
+```{r, echo=FALSE,out.width = "400px"}
+knitr::include_graphics("Washington_case.jpg")
+```
+
+## A third example of phylogeny: Tracking SARS back to its source
+```{r, echo=FALSE,out.width = "800px"}
+knitr::include_graphics("sars.jpg")
+```
+
+***
+### There are different methods to reconstruct phylogeny.
+- Distance-based methods (this course).
+- Parcimonious approach (old school).
+- Maximum likelihood (computer intensive).
+
+###What are you going to learn about data science (methods) if you are moderately interested in phylogeny.
+- Agglomerative hierarchical clustering: how to make clusters.
+- Bootstrap methods: how to evaluate uncertainty.
+
+##Agglomerative hierarchical clustering
+```{r, echo=FALSE,out.width = "800px"}
+knitr::include_graphics("agglo_clustering.jpg")
+```
+
+##Hierarchical clustering
+
+###Cluster dissimilarity
+A measure of dissimilarity between sets of observations (Euclidean distance,…).
+
+###Linkage criteria
+Distance between sets of observations as a function of the pairwise distances between observations (see [Wikipedia webpage about Hierarchical hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) for examples).
+
+##What are you going to learn about data science (software) if you are moderately interested in phylogeny.
+- How to use [R](https://cran.r-project.org/).
+- How to use [RStudio](https://www.rstudio.com/).
+- Perform dynamic reporting with [R Markdown](http://rmarkdown.rstudio.com/) documents.
+
+This presentation has been written in .Rmd.
+
+
diff --git a/intro_ComBiol_20172018.html b/intro_ComBiol_20172018.html
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+
+
+
First part of the course (michael.blum@univ-grenoble-alpes.fr) is about how to build phylogenetic trees based on DNA sequences. You will have to work, not me!
+
+
+
October 3, 09h45-12h45, D208: Introduction and mathematical computations
+
October 10, 09h45-12h45, E300: Mathematical computations, practical in R, and report writing
+
October 17, 09h45-12h45, E300: Practical in R, and report writing
+
October 20: Deadline to submit your report
+
+
+
Second part of the course (Olivier François)
+
+
An example of Phylogeny: Hominidae
+
+
+
+
A second example of phylogeny: US criminal case: State of Washington v Anthony Eugene Whitfield
+
+
+
+
A third example of phylogeny: Tracking SARS back to its source
+
+
+
+
+
+
There are different methods to reconstruct phylogeny.
+
+
+
Distance-based methods (this course).
+
Parcimonious approach (old school).
+
Maximum likelihood (computer intensive).
+
+
+
What are you going to learn about data science (methods) if you are moderately interested in phylogeny.
+
+
+
Agglomerative hierarchical clustering: how to make clusters.
+
Bootstrap methods: how to evaluate uncertainty.
+
+
+
Agglomerative hierarchical clustering
+
+
+
+
Hierarchical clustering
+
+
Cluster dissimilarity
+
+
A measure of dissimilarity between sets of observations (Euclidean distance,…).