Update material for CompBiol course

gitcompbiol.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
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+Encoding: UTF-8
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+RnwWeave: Sweave
+LaTeX: pdfLaTeX

intro_ComBiol_20172018.Rmd

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+---
+title: "Computational Biology"
+author: "[email protected]"
+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 ([email protected]) 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.
+
+

style.css

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+}