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Biomarker Identification for Cancer Drug Response Using Bioconductor

This project aims to develop predictors for drug response in cancer patients by identifying relevant biomarkers. Participants will work with microarray data, preprocess it, identify genes of interest, and annotate the findings. Requirements include analyzing datasets from different studies, using Bioconductor in R, and submitting a detailed report. Advantages of Bioconductor and R for data analysis are highlighted, along with installation guidelines and a case study demonstration. The second part of the assignment focuses on genetics of gene expression, utilizing SNP expression quantitative trait locus (eQTL) analysis to identify various genetic causes of differential gene expression.

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Biomarker Identification for Cancer Drug Response Using Bioconductor

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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. Extraction 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 changed markers between two groups 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. Heatmap:

  16. > significant <- pairwise.filter(pc.result,fc=log2(1), tt=0.001) • > pid<-rownames(significant@means) • >eset.hm<-eset.rma[pid,] • > install.packages("RColorBrewer") • > library(RColorBrewer) • > hmcol <- colorRampPalette(brewer.pal(10, "RdBu"))(256) • > spcol <- ifelse(eset.hm$title == "pCR", "goldenrod", "skyblue") • > heatmap(exprs(eset.hm), col = hmcol, ColSideColors = spcol)

  17. Assignment 2 • Genetics of gene expression (eQTL) • Aim: to identify potential genetics various that causes differential expression • Deadline of report: two weeks before final examination

  18. Genetics of gene expression

  19. SNP

  20. expression Quantitative Trait Locus (eQTL) • tries to find genomic variation to explain expression traits. • One difference between eQTL mapping and traditional QTL mapping is that, traditional mapping study focuses on one or a few traits, while in most of eQTL studies, thousands of expression traits will be analyzed and thousands of QTLs will be declared.

  21. GGdata: all 90 hapmap CEU samples, 47K expression, 4mm SNP

  22. Chromosome 17

  23. > biocLite(“GGtools”) • >biocLite(“GGdata”) • >library(GGtools) • >library(GGdata) • > c17 = getSS("GGdata", "17") • >/////get(“CSDA", revmap(illuminaHumanv1SYMBOL)) • > t1 = gwSnpTests(genesym("CSDA") ~ male, c17, chrnum("17")) • > /////t1 = gwSnpTests(probeId(" GI_21359983-S ") ~ male, c17, chrnum("17")) • > topSnps(t1) • >plot_EvG(genesym("CSDA"), rsid("rs7212116"), c17) • >//c_full = getSS(“GGdata", as.character(1:22))

  24. Requirements for assignment 2 • Identify the genetics cause (eQTL) of the genes selected in assignment 1 • Get SNPs with significant association (<10e-4) from each chromosome • Paste the plot image for each association • Annotate SNPs in dbSNP • Submit a report for each group

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