Bioinformatics Workshop on R An APBioNet-NUS Project

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Printable version Part I: Introduction to R Object types, data structures, data manipulation, importing and exporting data, Standard R commands and fuinctions More about time series objects Part II Programming with R Intro to programming, writing efficient code, object orientation, case study: Gene Correlation Part III Plotting with R Graphics devices, high-level plotting, Case study: Plotting experimental results low-level plotting, par Part IV BioStatistics with R Basic statistical functions, exploratory data analysis, regression models, correlation, T-tests, significance, covariance, ANOVA, hypothesis testing Part V Julian's material Day 1 Friday 13th June 2008 Morning Session 9am-12.15pm Welcome to BioInformatics Workshop on R 8.45am-9.00am Registration and Walk-in Payment Guest-of-honour: tbd Welcome to participants from AMBIS Vice-President Olivo Miotto Setting up your laptops 9.00am-10.30am Session I : Surviving R Objectives Getting Help in R Importing and Exporting data Manipulating your data Presenting your DATA (Graphics) Worksheet for session: Available at Course Materials Webpage Presentation slides for session: Available at Course Materials Webpage 10.30am-10.45am Morning /Tea Break with refreshments 10.45am-12.15pm Session II : Bioconductor-ing Objectives Bioconductor packages Familiarising with Expression data class Data Annotation Databases available, GO, biomaRt References: Gentleman et al (2004) Genome Biology 2004, 5:R80 "Bioconductor: open software for computational biology and bioinformatics" Dataset for session: Available at the Course Materials Webpage Worksheet for session: Available at Course Materials Webpage Presentation slides for session: Available at Course Materials Webpage 12.15pm-1.15pm Lunch Afternoon Session 1.15pm-5.30pm Welcome to the hands-on session of the BioInformatics Workshop on BioConductor 1.15pm-3.00pm Session III : Real World Dataset ( Chang Breast Cancer Paper) Objectives Preprocessing Heatmaps and Clustering PCA Analysis Differential Gene Expression Analysis ROC curves References: Chang et al. (2003) | From Elsevier | From Lancet | Via NUS | Lancet 2003, 362:362-369 "Gene Expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer" Dataset for session: Available at the Course Materials Webpage Worksheet for session: Available at Course Materials Webpage 3.00pm-3.15pm Afternoon Coffee/Tea Break with refreshments 3.15pm-5.00pm Session IV: Real World Dataset (CNV/SNP analysis) Objectives Finding Breakpoints Simple SNP studies References: tba Worksheet for session: Available at Course Materials Webpage 5.00pm-5.30pm Discussion 5.30pm-5.45pm Farewell and Certificate Presentation