project of cz5225 zhang jingxian: [email protected]
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Identifying biomarkers of drug Identifying biomarkers of drug response for cancer patientsresponse for cancer patients
Aims:Aims: To To develop of predictors of response to develop of predictors of response to
drugs drugs To learn how to get public microarray To learn how to get public microarray
datadata To learn how to preprocess microarray To learn how to preprocess microarray
raw dataraw data To annotate the genes of interestTo annotate the genes of interest
RequirementsRequirements
Each group investigates:Each group investigates: ONE kind of cancer patient drug responseONE kind of cancer patient drug response Need Two datasets from different studiesNeed Two datasets from different studies Download the raw dataDownload the raw data Use Bioconductor in R to prepossess raw Use Bioconductor in R to prepossess raw
datadata Identify certain number of genesIdentify certain number of genes Annotate those identified genes in your Annotate those identified genes in your
reportreport Each group needs only ONE reportEach group needs only ONE report
RequirementsRequirements
All kinds of affymatrix expression All kinds of affymatrix expression datasets related to drug response of datasets related to drug response of cancer patients are availablecancer patients are available
Dataset needs to contain at least 20 Dataset needs to contain at least 20 samplessamples
Dataset needs two comparable Dataset needs two comparable outcome groups: response vs. non-outcome groups: response vs. non-response; resistance vs. non-response; resistance vs. non-resistance, et al.resistance, et al.
Bioconductor & RBioconductor & R
http://www.bioconductor.orghttp://www.bioconductor.org
AdvantagesAdvantages Cross platformCross platform
Linux, windows and MacOSLinux, windows and MacOS Comprehensive and centralizedComprehensive and centralized
Analyzes both Affymetrix and two color spotted microarrays, and Analyzes both Affymetrix and two color spotted microarrays, and covers various stages of data analysis in a single environmentcovers various stages of data analysis in a single environment
Cutting edge analysis methodsCutting edge analysis methods New methods/functions can easily be incorporated and New methods/functions can easily be incorporated and
implementedimplemented QualityQuality check of data analysis methodscheck of data analysis methods
Algorithms and methods have undergone evaluation by Algorithms and methods have undergone evaluation by statisticians and computer scientists before launch. And in many statisticians and computer scientists before launch. And in many cases there are also literature referencescases there are also literature references
Good documentationsGood documentations Comprehensive manuals, documentations, course materials, Comprehensive manuals, documentations, course materials,
course notes and discussion group are availablecourse notes and discussion group are available A good chance to learn statistics and programmingA good chance to learn statistics and programming
Installation R & Installation R & BionconductorBionconductor
Install R from: Install R from: http://cran.stat.nus.edu.sg/
Open R platform then execute:Open R platform then execute:>source("http://bioconductor.org/>source("http://bioconductor.org/
biocLite.R") biocLite.R")
>biocLite() >biocLite()
Check library by execute: >library()Check library by execute: >library()
Case studyCase study Dataset source (GSE19697): Dataset source (GSE19697):
http://www.ncbi.nlm.nih.gov/geo
Extraction raw data into: Extraction raw data into: D://gse19697D://gse19697
Create title.txt :Create title.txt :
Open ROpen R Set workdir by execute: Set workdir by execute:
>setwd(>setwd(‘‘d://gse19697d://gse19697’’)) Load simpleaffy module by execute:Load simpleaffy module by execute:
>library(simpleaffy)>library(simpleaffy)
Load data by:Load data by: >eset <- read.affy('title.txt')>eset <- read.affy('title.txt')
Calculate expression by:Calculate expression by: >eset.rma <- call.exprs(eset,'rma')>eset.rma <- call.exprs(eset,'rma')
Compare two groups by:Compare two groups by: >pc.result <- >pc.result <-
pairwise.comparison(eset.rma, "title", pairwise.comparison(eset.rma, "title", c("pCR", "RD"), eset)c("pCR", "RD"), eset)
Filter significant changed markers Filter significant changed markers between two groups by:between two groups by: >significant <- >significant <-
pairwise.filter(pc.result,fc=log2(1.5), tt=0.001)pairwise.filter(pc.result,fc=log2(1.5), tt=0.001)
Plot significant changed markers:Plot significant changed markers: >plot(significant)>plot(significant)
Annotate selected markers:Annotate selected markers: >significant>significant
Annotate selected markers:Annotate selected markers:
Heatmap:Heatmap:
> significant <- pairwise.filter(pc.result,fc=log2(1), > significant <- pairwise.filter(pc.result,fc=log2(1), tt=0.001) tt=0.001)
> pid<-rownames(significant@means) > pid<-rownames(significant@means) >eset.hm<-eset.rma[pid,] >eset.hm<-eset.rma[pid,] > install.packages("RColorBrewer")> install.packages("RColorBrewer") > library(RColorBrewer)> library(RColorBrewer) > hmcol <- colorRampPalette(brewer.pal(10, > hmcol <- colorRampPalette(brewer.pal(10,
"RdBu"))(256) "RdBu"))(256) > spcol <- ifelse(eset.hm$title == "pCR", > spcol <- ifelse(eset.hm$title == "pCR",
"goldenrod", "skyblue") "goldenrod", "skyblue") > heatmap(exprs(eset.hm), col = hmcol, > heatmap(exprs(eset.hm), col = hmcol,
ColSideColors = spcol)ColSideColors = spcol)
Assignment 2Assignment 2
Genetics of gene expression (eQTL)Genetics of gene expression (eQTL) Aim: to identify potential genetics Aim: to identify potential genetics
various that causes differential various that causes differential expressionexpression
Deadline of report: two weeks before Deadline of report: two weeks before final examinationfinal examination
Genetics of gene expressionGenetics of gene expression
SNPSNP
expression Quantitative expression Quantitative Trait Locus (eQTL) Trait Locus (eQTL)
tries to find genomic variation to explain tries to find genomic variation to explain expression traits.expression traits.
One difference between eQTL mapping One difference between eQTL mapping and traditional QTL mapping is that, and traditional QTL mapping is that, traditional mapping study focuses on one traditional mapping study focuses on one or a few traits, while in most of eQTL or a few traits, while in most of eQTL studies, studies, thousands of expression traits thousands of expression traits will will be analyzed and thousands of QTLs will be be analyzed and thousands of QTLs will be declared.declared.
GGdata: all 90 hapmap CEU samples, GGdata: all 90 hapmap CEU samples, 47K expression, 4mm SNP47K expression, 4mm SNP
Chromosome 17Chromosome 17
> biocLite(“GGtools”)> biocLite(“GGtools”) >biocLite(“GGdata”)>biocLite(“GGdata”) >library(GGtools)>library(GGtools) >library(GGdata)>library(GGdata) > c17 = getSS("GGdata", "17")> c17 = getSS("GGdata", "17") >/////get(“CSDA", revmap(illuminaHumanv1SYMBOL))>/////get(“CSDA", revmap(illuminaHumanv1SYMBOL)) > t1 = gwSnpTests(genesym("CSDA") ~ male, c17, chrnum("17"))> t1 = gwSnpTests(genesym("CSDA") ~ male, c17, chrnum("17")) > /////t1 = gwSnpTests(probeId(" GI_21359983-S ") ~ male, c17, > /////t1 = gwSnpTests(probeId(" GI_21359983-S ") ~ male, c17,
chrnum("17"))chrnum("17")) > topSnps(t1)> topSnps(t1) >plot_EvG(genesym("CSDA"), rsid("rs7212116"), c17)>plot_EvG(genesym("CSDA"), rsid("rs7212116"), c17) >//c_full = getSS(“GGdata", as.character(1:22))>//c_full = getSS(“GGdata", as.character(1:22))
Requirements for Requirements for assignment 2assignment 2
Identify the genetics cause (eQTL) of Identify the genetics cause (eQTL) of the genes selected in assignment 1the genes selected in assignment 1
Get SNPs with significant association Get SNPs with significant association (<10e-4) from (<10e-4) from each chromosomeeach chromosome
Paste the plot image for each Paste the plot image for each associationassociation
Annotate SNPs in dbSNPAnnotate SNPs in dbSNP Submit a report for each groupSubmit a report for each group