ABSTAT14 Advanced Biostatistics for Bioinformatics Tool Users using R 
IMPORTANT DATES for this Course

Instructors: 
Lisete Sousa is an Assistant Professor in Faculty of Sciences of the University
of Lisbon (FCUL), and coordinates the MSc in Biostatistics. She is an integrated member of the Center of Statistics
and Applications (CEAUL) since 2004 when she obtained her PhD in Probability and Statistics (Lisbon University and
Dortmund University  Germany). Since then she has taught the course Statistical Methods in Bioinformatics
in the University of Lisbon, among other statistical courses for BSc. and MSc. Since 2001, her main research interests
are the development of statistical methodologies for detecting differentially expressed genes, and the prediction of
transmembrane proteins topology based on hidden Markov models but adopting a Bayesian perspective. She collaborates with
researchers from Instituto de Medicina Molecular (IMM) and Instituto de Ciência Aplicada e
Tecnologia of FCUL (ICAT). She has been a GTPB instructor in the BFB course in 2010 and 2012. 
Carina Silva Fortes graduated in Statistics and Operational Research and holds a MSc degree in Probability and
Statistics from Faculdade de Ciências da Universidade de Lisboa. She is Adjunct Professor at Escola Superior de
Tecnologia da Saúde de Lisboa (ESTeSL  IPL) and teaches Applied Mathematics and Biostatistics there. She obtained
a PhD in Probability and Statistics in 2012. She has coordinated the course "Statistical Data Analysis with R" at ESTeSL. She is a member
and collaborator of CEAUL (Center of Statistics and Applications). She is the Coordinator of
the scientific area of Mathematics of ESTeSL. Her main research interests are Statistics in Health Sciences, ROC analysis,
Molecular Genetics (Microarray Data Analysis) and Bayesian Statistics. She has been a GTPB instructor in the BFB course in
2010, 2011 and 2012. 
Course description 
This course is a standalone advanced Biostatistics course that can be taken as a continuation of the Introductory Biostatistics for Bioinformatics
(IBSTAT) course that precedes it in GTPB. It 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 handson 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, worked with Bioinformatics tools. Care has been taken not to use any proprietary data or software, so that the handson experience can carry on after the course, providing maximum user independence. We will be using custombuilt R scripts and packages that are available from the CRAN and Bioconductor repositories. MethodologyThis 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 handson sessions. We will focus on each method using examples taken from real world Bioinformatics practice.  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. 
Course PrerequisitesIntermediate level knowledge in Statistics is necessary. There is no time to provide basic knowledge, so we will need to assume that accepted candidates have selfassessed 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. 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 easeup interaction and increase productivity, but people that prefer the original R environment on the command line will be able to follow that preference. 
Detailed Program 
Instituto Gulbenkian de Ciência, Apartado 14, 2781901 Oeiras, Portugal Last updated: July 25th 2014 