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Project of CZ5225

Project of CZ5225. Zhang Jingxian: g0800791@nus.edu.sg. Identifying biomarkers of drug response for cancer patients. Aims: To d evelop of predictors of response to drugs To learn how to get public microarray data To learn how to preprocess microarray raw data

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Project of CZ5225

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  1. Project of CZ5225 Zhang Jingxian: g0800791@nus.edu.sg

  2. Identifying biomarkers of drug response for cancer patients • Aims: • To develop of predictors of response to drugs • To learn how to get public microarray data • To learn how to preprocess microarray raw data • To annotate the genes of interest

  3. Requirements • Each group investigates: • ONE kind of cancer patient drug response • Need Two datasets from different studies • Download the raw data • Use Bioconductor in R to prepossess raw data • Identify certain number of genes • Annotate those identified genes in your report • Each group needs only ONE report

  4. Requirements • All kinds of affymatrix expression datasets related to drug response of cancer patients are available • Dataset needs to contain at least 20 samples • Dataset needs two comparable outcome groups: response vs. non-response; resistance vs. non-resistance, et al.

  5. Bioconductor & R • http://www.bioconductor.org

  6. Advantages • Cross platform • Linux, windows and MacOS • Comprehensive and centralized • Analyzes both Affymetrix and two color spotted microarrays, and covers various stages of data analysis in a single environment • Cutting edge analysis methods • New methods/functions can easily be incorporated and implemented • Qualitycheck of data analysis methods • Algorithms and methods have undergone evaluation by statisticians and computer scientists before launch. And in many cases there are also literature references • Good documentations • Comprehensive manuals, documentations, course materials, course notes and discussion group are available • A good chance to learn statistics and programming

  7. Installation R & Bionconductor • Install R from: http://cran.stat.nus.edu.sg/ • Open R platform then execute: >source("http://bioconductor.org/biocLite.R") >biocLite() • Check library by execute: >library()

  8. Case study • Dataset source (GSE19697): http://www.ncbi.nlm.nih.gov/geo

  9. Decompress raw data into: D://gse19697 • Create title.txt :

  10. Open R • Set workdir by execute: • >setwd(‘d://gse19697’) • Load simpleaffy module by execute: • >library(simpleaffy) • Load data by: • >eset <- read.affy('title.txt')

  11. Calculate expression by: • >eset.rma <- call.exprs(eset,'rma') • Compare two groups by: • >pc.result <- pairwise.comparison(eset.rma, "title", c("pCR", "RD"), eset)

  12. Filter significant change markers between two goups by: • >significant <- pairwise.filter(pc.result,fc=log2(1.5), tt=0.001)

  13. Plot significant changed markers: • >plot(significant) • Annotate selected markers: • >significant

  14. Annotate selected markers:

  15. Materials submitted • ONE report: • Introduction of your project • Datasets used in the project • Procedures of processing data • Annotation of selected markers • Conclusion • File: title.txt

  16. Useful resources • http://www.bioconductor.org/ • http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#biocon_simpleaffy • http://www.ncbi.nlm.nih.gov/geo/ • http://www.bioconductor.org/packages/release/bioc/html/simpleaffy.html

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