Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
r_symposium [2017/04/29 11:18] vincent_fugere |
r_symposium [2017/05/09 12:16] (current) sebastien.renaut |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| + | ** *Workshop material has been added in the abstract section below** | ||
| + | |||
| {{ :flyer_wiki.jpg?nolink |}} | {{ :flyer_wiki.jpg?nolink |}} | ||
| Line 38: | Line 40: | ||
| **The Bayesian Biologist: You are probably more Bayesian than you think** //by Max Farrell & Marc-Olivier Beausoleil//. Jump with us into the world of probabilities with a workshop on Bayesian inference. We are going to explore this statistical framework with simple and meaningful examples for biologists. We plan to guide you through some theory, history, applications, and get your hands dirty with some code. At the end of the workshop, you’ll be convinced that Bayesian statistics are a super powerful framework to interpret the world, and get a taste of the ways you might implement them in your own research. You have an idea of things you want us to discuss? Fill this survey: https://goo.gl/forms/By3aMFtNaxLJ2ICB2 or email [[maxwell.farrell@mail.mcgill.ca|Max]] or [[marc-olivier.beausoleil@mail.mcgill.ca|Marco]], we would like to hear your ideas! | **The Bayesian Biologist: You are probably more Bayesian than you think** //by Max Farrell & Marc-Olivier Beausoleil//. Jump with us into the world of probabilities with a workshop on Bayesian inference. We are going to explore this statistical framework with simple and meaningful examples for biologists. We plan to guide you through some theory, history, applications, and get your hands dirty with some code. At the end of the workshop, you’ll be convinced that Bayesian statistics are a super powerful framework to interpret the world, and get a taste of the ways you might implement them in your own research. You have an idea of things you want us to discuss? Fill this survey: https://goo.gl/forms/By3aMFtNaxLJ2ICB2 or email [[maxwell.farrell@mail.mcgill.ca|Max]] or [[marc-olivier.beausoleil@mail.mcgill.ca|Marco]], we would like to hear your ideas! | ||
| - | {{ :youre_probably_mbtyt.pdf | Marco's presentation}} | + | {{ :youre_probably_mbtyt.pdf | Marco's presentation}}\\ |
| - | + | [[https://github.com/beausoleilmo/qcbs_bayesian_workshop]]\\ | |
| - | [[https://github.com/beausoleilmo/qcbs_bayesian_workshop]] | + | |
| [[https://github.com/maxfarrell/qcbs_stan_workshop]] | [[https://github.com/maxfarrell/qcbs_stan_workshop]] | ||
| Line 47: | Line 47: | ||
| **Intro to gene expression analysis in R** //by Sébastien Renaut//. Next generation sequencing has promised cheap DNA sequences to the masses. While this may be true, the bottleneck has now shifted from generating data to analyzing it. Here, I will use transcriptome sequencing data (RNAseq) to quantify gene expression. I will introduce data formats commonly used in genomics (e.g.: .fastq,.bam,.sam) and I will use the R programming language to identify differentially expressed genes (e.g. DESeq2, edgeR packages), cluster samples based on gene expression, detects gene ontology categories which are over/under represented (goseq) and present various graphics to illustrate results. | **Intro to gene expression analysis in R** //by Sébastien Renaut//. Next generation sequencing has promised cheap DNA sequences to the masses. While this may be true, the bottleneck has now shifted from generating data to analyzing it. Here, I will use transcriptome sequencing data (RNAseq) to quantify gene expression. I will introduce data formats commonly used in genomics (e.g.: .fastq,.bam,.sam) and I will use the R programming language to identify differentially expressed genes (e.g. DESeq2, edgeR packages), cluster samples based on gene expression, detects gene ontology categories which are over/under represented (goseq) and present various graphics to illustrate results. | ||
| - | [[http://sebastien.renaut.com/rnaseq_workshop/Rcode/]] | + | [[https://github.com/seb951/rnaseq_workshop]] |
| **Predicting species geographical distribution using R** //by Pedro Henrique P. Braga and Julia Nordlund//. Species distribution models (SDM) have been widely applied to address many questions in | **Predicting species geographical distribution using R** //by Pedro Henrique P. Braga and Julia Nordlund//. Species distribution models (SDM) have been widely applied to address many questions in | ||
| Line 64: | Line 64: | ||
| **Joint modelling** //by Guillaume Blanchet//. Natural systems are complex and understanding them is a challenging task. In recent years, there has been an explosion in the amount of data that were gathered and made available that can potentially increase our knowledge of why and how species distribute as they do. It is now possible to obtain highly precise environmental and habitat characteristics for large areas of the world, traits are now available for a wealth of species and it is now possible to obtain high quality phylogenies for large groups of species. But how can we link these data together to better understand and predict the distribution of multiple species in a single model? In recent years, joint species distribution models (JSDMs) have emerged as an attractive way to approach such question. In this workshop, I will show you how to construct JSDMs using Bayesian hierarchical models. I will also briefly discuss the concept behind hierarchical models and how they can be used in a community ecology context. | **Joint modelling** //by Guillaume Blanchet//. Natural systems are complex and understanding them is a challenging task. In recent years, there has been an explosion in the amount of data that were gathered and made available that can potentially increase our knowledge of why and how species distribute as they do. It is now possible to obtain highly precise environmental and habitat characteristics for large areas of the world, traits are now available for a wealth of species and it is now possible to obtain high quality phylogenies for large groups of species. But how can we link these data together to better understand and predict the distribution of multiple species in a single model? In recent years, joint species distribution models (JSDMs) have emerged as an attractive way to approach such question. In this workshop, I will show you how to construct JSDMs using Bayesian hierarchical models. I will also briefly discuss the concept behind hierarchical models and how they can be used in a community ecology context. | ||
| - | [[https://github.com/guiblanchet/jointmodelling]] | + | [[https://github.com/guiblanchet/jointmodelling]]\\ |
| [[https://github.com/guiblanchet/HMSC]] | [[https://github.com/guiblanchet/HMSC]] | ||
| **Open Science and Reproducibility in R** //by Monica Granados//. Imagine if every paper you ever publish from now on could be reproduced by anyone around the world. Or a platform that gives you the power to integrate new data seamlessly into a manuscript complete with text and figures. In this workshop, we will be covering how to work in the open using R, R Markdown and GitHub. These three open platforms allow us to host data, analyze, visualize and produce a manuscript in one reproducible workflow. You will learn how to set up a repository in GitHub and manage branches, draw data from GitHub into R, write an R Markdown script for your manuscript and how to upload the R Markdown script into GitHub for reproducibility. The advantages of open, reproducible science are many. When working collaboratively, reproducible workflows allow collaborators to contribute simultaneously to the project with version control to preserve different iterations of the project. Working in the open also allows you share your research more widely, facilitating collaborative opportunities. At the end of the workshop we will also discuss the wider movement of open science, how it is helping breakdown economic barriers in science and education and how you can contribute. | **Open Science and Reproducibility in R** //by Monica Granados//. Imagine if every paper you ever publish from now on could be reproduced by anyone around the world. Or a platform that gives you the power to integrate new data seamlessly into a manuscript complete with text and figures. In this workshop, we will be covering how to work in the open using R, R Markdown and GitHub. These three open platforms allow us to host data, analyze, visualize and produce a manuscript in one reproducible workflow. You will learn how to set up a repository in GitHub and manage branches, draw data from GitHub into R, write an R Markdown script for your manuscript and how to upload the R Markdown script into GitHub for reproducibility. The advantages of open, reproducible science are many. When working collaboratively, reproducible workflows allow collaborators to contribute simultaneously to the project with version control to preserve different iterations of the project. Working in the open also allows you share your research more widely, facilitating collaborative opportunities. At the end of the workshop we will also discuss the wider movement of open science, how it is helping breakdown economic barriers in science and education and how you can contribute. | ||
| - | [[r_symposium_open_science|Open Sci workshop]] | + | [[r_symposium_open_science|Open Sci workshop page]] |
