################################################### ### chunk number 1: loadPacks ################################################### library(clippda) ################################################### ### chunk number 2: setWidth ################################################### options(width=60) ################################################### ### chunk number 3: liverdata ################################################### data(liverdata) data(liverRawData) data(liver_pheno) liverdata[1:4,] liverRawData[1:4,] ################################################### ### chunk number 4: decription1 ################################################### names(liverdata) dim(liverdata) ################################################### ### chunk number 5: checkNo.replicates ################################################### no.peaks <- 53 no.replicates <- 2 checkNo.replicates(liverRawData,no.peaks,no.replicates) ################################################### ### chunk number 6: preProcRepeatedPeakData ################################################### threshold <- 0.80 Data <- preProcRepeatedPeakData(liverRawData, no.peaks, no.replicates, threshold) ################################################### ### chunk number 7: difference ################################################### setdiff(unique(liverRawData$SampleTag),unique(liverdata$SampleTag)) setdiff(unique(Data$SampleTag),unique(liverdata$SampleTag)) ################################################### ### chunk number 8: spectrumFilter ################################################### TAGS <- spectrumFilter(Data,threshold,no.peaks)$SampleTag NewRawData2 <- Data[Data$SampleTag %in% TAGS,] dim(Data) dim(liverdata) dim(NewRawData2) ################################################### ### chunk number 9: no.replicates ################################################### length(liverRawData[liverRawData$SampleTag == 25,]$Intensity)/no.peaks length(liverRawData[liverRawData$SampleTag == 40,]$Intensity)/no.peaks ################################################### ### chunk number 10: coherencepeaks ################################################### Mat1 <- matrix(liverRawData[liverRawData$SampleTag == 25,]$Intensity,53,3) Mat2 <-matrix(liverRawData[liverRawData$SampleTag == 40,]$Intensity,53,4) cor(log2(Mat1)) cor(log2(Mat2)) ################################################### ### chunk number 11: coherencepeaks ################################################### Mat1 <- matrix(liverRawData[liverRawData$SampleTag == 25,]$Intensity,53,3) Mat2 <-matrix(liverRawData[liverRawData$SampleTag == 40,]$Intensity,53,4) sort(mostSimilarTwo(cor(log2(Mat1)))) sort(mostSimilarTwo(cor(log2(Mat2)))) ################################################### ### chunk number 12: confirmpreprocessing ################################################### names(NewRawData2) dim(NewRawData2) names(liverdata) dim(liverdata) setdiff(NewRawData2$SampleTag,liverdata$SampleTag) setdiff(liverdata$SampleTag,NewRawData2$SampleTag) summary(NewRawData2$Intensity) summary(liverdata$Intensity) ################################################### ### chunk number 13: sampleClusteredData ################################################### JUNK_DATA <- sampleClusteredData(NewRawData2,no.peaks) head(JUNK_DATA)[,1:5] ################################################### ### chunk number 14: column1 ################################################### as.vector(t(matrix(liverdata[liverdata$SampleTag %in% 156,]$Intensity,53,2))[,1:5]) length(as.vector(t(matrix(liverdata[liverdata$SampleTag %in% 156,]$Intensity,53,2)))) as.vector(t(matrix(NewRawData2[NewRawData2$SampleTag %in% 156,]$Intensity,53,2))[,1:5]) length(as.vector(t(matrix(NewRawData2[NewRawData2$SampleTag %in% 156,]$Intensity,53,2)))) ################################################### ### chunk number 15: createClassObject ################################################### OBJECT=new("aclinicalProteomicsData") OBJECT@rawSELDIdata=as.matrix(NewRawData2) #OBJECT@rawSELDIdata=as.matrix(liverdata) OBJECT@covariates=c("tumor" , "sex") OBJECT@phenotypicData=as.matrix(liver_pheno) OBJECT@variableClass=c('numeric','factor','factor') OBJECT@no.peaks=no.peaks OBJECT ################################################### ### chunk number 16: ExtractComponetsOfeSet ################################################### head(proteomicsExprsData(OBJECT)) head(proteomicspData(OBJECT)) ################################################### ### chunk number 17: biologicalParameters ################################################### intraclasscorr <- 0.60 signifcut <- 0.05 Data=OBJECT sampleSizeParameters(Data, intraclasscorr, signifcut) Z <- as.vector(fisherInformation(Data)[2,2])/2 Z sampleSize(Data, intraclasscorr, signifcut) ################################################### ### chunk number 18: contourplot ################################################### m <- 2 DIFF <- seq(0.1,0.50,0.01) VAR <- seq(0.2,4,0.1) beta <- c(0.90,0.80,0.70) alpha <- 1 - c(0.001, 0.01,0.05)/2 Corr <- c(0.70,0.90) Z <- 2.4 Indicator <- 1 observedPara <- c(1,0.4) #the variance you computed from pilot data #observedPara <- data.frame(var=c(0.7,0.5,1.5),diFF=c(0.37,0.33,0.43)) sampleSizeContourPlots(Z,m,DIFF,VAR,beta,alpha,observedPara,Indicator) ################################################### ### chunk number 19: contourplot ################################################### observedVAR=1 observedDIFF=0.4 ################################################### ### chunk number 20: scatterplot ################################################### Z <- 2.460018 m <- 2 DIFF <- seq(0.1,0.50,0.01) VAR <- seq(0.2,4,0.1) beta <- c(0.90,0.80,0.70) alpha <- 1 - c(0.001, 0.01,0.05)/2 observedDIFF <- 0.4 observedVAR <- 1.0 observedSampleSize <- 80 Indicator <- 1 Angle <- 60 sampleSize3DscatterPlots(Z,m,DIFF,VAR,beta,alpha,observedDIFF,observedVAR,observedSampleSize,Angle,Indicator) ################################################### ### chunk number 21: Writing the BibTex.bib file ################################################### write(paste( "@Article{Kunetal2006, author={Kun, Y. and Jianzhong, L. and Gao, H.}, title={The impact of sample imbalance on identifying differentially expressed genes}, journal={BMC Bioinformatics}, year=2006, volume={7 (Suppl 4):S8}, }", "@Article{Cairnsetal2009, title = {Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison}, author = {Cairns, DA. and Barrett, JH. and Billingham, LJ. and Stanley, AJ. and Xinarianos, G. and Field, JK. and Johnson, PJ. and Selby, PJ. and Banks, RE.}, journal= {Proteomics}, year = {2009}, volume = {9}, pages={74-86} }", "@incollection{li2005, author = {Li, X. and Gentleman, R. and Lu, X. and Shi, Q. and Iglehart, J.D. and Harris, L. and Miron, A.}, title = {Mass spectrometry protein data}, booktitle = {Bioinformatics and Computational Biology Solutions Using R and Bioconductor}, editor = {Gentleman, R. and carey, V. and Huber, W. and Irizarry, R. and Dudoit, S.}, address = {New York}, publisher = {Springer}, year = {2005}, chapter = {6}, pages = {91--108}, }", "@Article{DobinandSimon2005, title = {Sample Size Determination in Microarray Experiments for Class Comparison and Prognostic Classification}, author = {Dobbin, K. and Simon, R.}, journal= {Biostatistics}, year = {2005}, volume= {6}, pages={27-38} }", "@Article{WeiandBumgarner2004, title = {Sample size for detecting differentially expressed genes in microarray experiments. }, author = {Wei, C. and Li, J. and Bumgarner, R.}, journal= {BMC genomics}, year = {2004}, volume= {5(87): doi :10.1186/1471-2164-5-87}, }", "@Article{Jorstadetal2007, title = {Understanding sample size: what determines the required number of microarrays for an experiment}, author = {Jørstad, TS. and Langaas, M. and Bones, AM.}, journal= {TRENDS in Plant Science}, year = {2007}, volume= {12(2)}, pages={46-50} }", "@Article{Birkneretal2006, title = {Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data}, author = {Birkner, MD. and Hubbard, AE. and van der Laan, MJ. and Skibola, CF. and Hegedus, CM. and Smith, MT.}, journal= {Stat Appl Genet Mol Biol}, year = {2006}, volume= {5(1) Article 11}, }", "@TechReport{Nyangomaetal2009, author={Nyangoma, SO. and Ferreira, JA. and Collins, SI. and Altman, DG. and Johnson, PJ. and Billingham, LJ.}, title={Sample size calculations for planning clinical proteomic profiling studies using mass spectrometry}, year={2009}, month={Jan}, institution={Cancer Research UK Institute for Cancer Studies, University of Birmingham}, type={Biostatistics Working Papers}, number={1} }", "@TechReport{tuszynski2003, author={Tuszynski, J.}, title={caMassClass: A package for processing and clissifying mass spectrometry data}, year={2003}, month={}, institution={SAIC and the National Cancer Institute}, type={Software}, number={} }", " @Article{gentleman2004, author = {Gentleman, R. C. and Carey, V. J. and Bates, D. M. and others}, title = {Bioconductor: Open software development for computational biology and bioinformatics}, journal = {Genome Biology}, volume = {5}, year = {2004}, pages = {R80}, url = {http://genomebiology.com/2004/5/10/R80}, }", "@Article{BoguskiandMcIntosh2003, title = {Biomedical informatics for proteomics}, author = {Boguski, M. and McIntosh, M.}, journal= {Nature}, year = {2003}, volume= {422(6928)}, pages={233-7} }", sep="\n"), file="LittBib.bib")