Course description: This course is targeted for Biostatistical techniques often employed in analytical tools for high throughput data and multivariate data. Participants can expect to attend a thorough set of lectures that will reveal the conceptual frameworks that are needed to understand the methods. Extensive hands-on practice will be the main vehicle for providing the skills and user independence. To keep things in context, the course is exclusively based on biological examples. We will be using custom-built R scripts and packages that are available from the CRAN and/or Bioconductor repositories. Care has been taken not to use any proprietary data or software, so that the hands-on experience can carry on after the course, providing maximum user independence. We will be using custom-built R scripts and packages that are available from the CRAN and Bioconductor repositories.
Methodology This intensive course will introduce a relatively high number of concepts and methods. To keep it highly practical, we will spend most of the time in hands-on sessions.
- We will focus on each method using examples taken from biological data.
- We will then dissect the method, identifying the concepts and exploring their interrelationships.
- The applicability and limitations of each method will be emphasized.
- The use of the method will be illustrated using appropriate Bioinformatics tools and biological data resources.
Target Audience
Everybody using Bioinformatics methods is implicitly using statistical methods. Moreover, proper judgement of the results often calls for a deeper level of understanding than what is required to solve scholarly exercises. We will look into particular areas such Simulation, Bayesian Inference, Hidden Markov Chains and Multivariate Data Analysis methods with the attitude, eyes and brains of an experienced statistician that wants to understand how the methods work and systematic way. Course Pre-requisites Intermediate level knowledge in Statistics is necessary. There is no time to provide basic knowledge, so we will need to assume that accepted candidates have self-assessed for it in the following areas:
- probability
- conditional probability
- distributions
- statistical tests
- hypothesis testing
- inference
This level can also be obtained by attending another course in GTPB: The IBSTAT course. Basic Familiarity with the R environment will be necessary. Please follow the exercise that we provide. Install R from http://cran.r-project.org/ following the instructions. Download and unzip the Tutorial folder that is made available here.
- Visualize the slides in "Tutorial R.pdf"
- Follow the exercise in "Basic_Exercise.pdf"
- For reference, we also provide a script with a correct set of R statements in sequence "Tutorial_script.R"
Additionally, we suggest that candidates acquire familiarity with RStudio by visiting the following resources:
- Introduction to RStudio (basics)
- Tutorial R and R Studio (complete)
R Studio will be used in the course to ease-up interaction and increase productivity, but people that prefer the original R environment on the command line will be able to follow that preference
Learning objectives:
Learning outcomes:
Exercises