Package CONOR. August 29, 2013

Transcription

1 Package CONOR August 29, 2013 Type Package Title CONOR Version Date Author Jason Rudy and Faramarz Valafar Maintainer Jason Rudy Description CrOss-platform NOrmalization in R: normalize gene expression data to be comparable for different microarray platforms. Depends CLSOCP, tseries, outliers, nortest, quadprog, zoo,preprocesscore, fields, fpc, flexclust, plyr, CONORData License GPL-2 LazyLoad yes NeedsCompilation yes Repository CRAN Date/Publication :28:40 R topics documented: CONOR Index 6 1

2 2 CONOR CONOR Functions for cross-platform normalization of microarray data. Description Usage The functions below perform cross-platform normalization of microarray data. distran(platform1.data, platform2.data, p1.assayclasses = NULL, L = 4, cluster = "pam", corr = "pearson", p1.names = 0, p2.names = 0, skip.match=false) dwd(platform1.data, platform2.data, platform1.train = NULL, platform2.train = NULL, p1.names = 0, p2.names = 0, p1.train.names = 0, p2.train.names = 0, skip.match = FALSE, use.sparse = TRUE) eb(platform1.data, platform2.data, par.prior = TRUE, filter = FALSE, prior.plots = FALSE, p1.names = 0, p2.names = 0, skip.match=false) gq(platform1.data, platform2.data, p1.names = 0, p2.names = 0, skip.match=false) mrs(platform1.data, platform2.data, p1.names = 0, p2.names = 0, skip.match = FALSE) nordi(platform1.data, platform2.data, pvalue = 0.01, alpha = 0.05, p1.names = 0, p2.names = 0, skip.match=false) qd(platform1.data, platform2.data, b = 8, p1.names = 0, p2.names = 0, skip.match=false) qn(platform1.data, platform2.data, p1.names = 0, p2.names = 0, skip.match=false) xpn(platform1.data, platform2.data, K = 10, L = 4, p1.names = 0, p2.names = 0, gene.cluster = "kmeans", assay.cluster = "kmeans", corr = "pearson", iterations = 30, skip.match = FALSE) Arguments platform1.data Expression data from platform 1. Should be formatted as a data.frame, with each column representing an array and each row a gene. platform2.data Expression data from platform 2. Should be formatted as a data.frame, with each column representing an array and each row a gene. p1.names One column of platform1.data may contain gene names. The column number containing the gene names should be specified as p1.names. If p1.names is zero, the rownames attribute will be used. Default value is zero. The gene names for platform 1 should correspond to the gene names for platform 2.

3 CONOR 3 p2.names One column of platform2.data may contain gene names. The column number containing the gene names should be specified as p2.names. If p2.names is zero, the rownames attribute will be used. Default value is zero. The gene names for platform 2 should correspond to the gene names for platform 1. platform1.train Training data set for use with DWD. platform2.train Training data set for use with DWD. p1.train.names The column number containing the gene names for platform 1 training data. p2.train.names The column number containing the gene names for platform 2 training data. p1.assayclasses Known classes for the distran method. cluster Clustering method used by distran. par.prior Parameter for eb. par.prior specifies whether to use a parametric or nonparametric prior distribution. filter prior.plots pvalue alpha b skip.match use.sparse gene.cluster assay.cluster corr iterations K Parameter for eb. Generate prior plots for eb method. Parameter for nordi. pvalue is a cutoff for determining normality of a distribution using the Grubbs statistic. Parameter for nordi. alpha is the area of the combined left and right tails on the normal distribution used for discretization. Parameter for quantile discretization (qd). b is the number of quantiles to use. If skip.match is FALSE, rows of platform1.data and platform2.data will be matched using gene names. This process uses R s built-in intercept function, which can be quite inefficient. If the rows of platform1.data and platform2.data already match, time can be saved by setting skip.match to TRUE. For dwd only. Can be set to TRUE or FALSE. Determines whether dwd uses sparse matrices (via the Matrix package) for its internal calculations. Sparse matrix calculations are more efficient for large problems, but will not affect the final output. For xpn only, gene.cluster specifies the gene clustering method to be used. Options are "kmeans", "pam", and "flexclust". Only "kmeans" is practical for large numbers or genes. For xpn and distran only, assay.cluster specifies the assay clustering method to be used. Options are "classic", "kmeans", "pam", and "flexclust". For xpn and distran only, corr is the type of correlation to use as a distance measure for sample or gene clustering. Ignored for the "kmeans" and "classic" clustering options, for which only Pearson s correlation is available. For xpn only, iterations gives the number of iterations of the XPN algorithm to perform. For xpn only, K is the number of gene clusters to use. Must be an integer. If a vector of more than one integer is given, the best value will be selected by the silhouette based method of the pamk function from the fpc package. This can be extremely slow for large numbers of genes.

4 4 CONOR L For xpn and distran only, L is the number of assay clusters to use. Must be an integer. If a vector of more than one integer is given, the best value will be selected by a silhouette based method using the pamk function from the fpc package. Value x Normalized data from platform 1. y Normalized data from platform 2. p1.adjust p2.adjust Author(s) Jason Rudy and Faramarz Valafar References For dwd only, vector of platform effects removed from the platform 1 data. For dwd only, vector of platform effects removed from the platform 2 data. Benito et al. Adjustment of systematic microarray data biases. Bioinformatics (2004) vol. 20 (1) pp. 105 Bolstad et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics (2003) vol. 19 (2) pp. 185 Jiang et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC bioinformatics (2004) vol. 5 pp. 81 Martinez et al. GenMiner: mining informative association rules from genomic data. Proceeding of the IEEE International Conference on Binformatics and Biomedicine. (2007) pp Shabalin et al. Merging two gene-expression studies via cross-platform normalization. Bioinformatics (2008) vol. 24 (9) pp Shi et al. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nature biotechnology (2006) vol. 24 (9) pp Walker et al. Empirical Bayes accomodation of batch-effects in microarray data using identical replicate reference samples: application to RNA expression profiling of blood... BMC bioinformatics (2008) Warnat et al. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC bioinformatics (2005) vol. 6 pp. 265 Examples ## Not run: #Load the CONORData package library(conordata) #Load the Affymetrix data from the Microarray Quality Control Project (MAQC, Shi et al, 2006) data(maqc.afx)

5 CONOR 5 #Load the Illumina data from MAQC data(maqc.ilm) #Perform Distance Weighted Discrimination (Takes 2-3 minutes, Benito et al, 2004) dwd.output <- dwd(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=true) #Plot the AFX sample A data against the ILM sample A data plot(rowmeans(dwd.output$x[regexpr("_a",colnames(dwd.output$x))!= -1]), rowmeans(dwd.output$x[regexpr("_a",colnames(dwd.output$y))!= -1]), xlab="afx", ylab="ilm") #Other methods - should work if uncommented: #Perform XPN xpn.output = xpn(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false, iterations=1) #Perform eb eb.output = eb(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform distran distran.output = distran(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform gq gq.output = gq(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform mrs mrs.output = mrs(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform nordi nordi.output = nordi(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform qd qd.output = qd(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) #Perform qn qn.output = qn(platform1.data=maqc.afx, platform2.data=maqc.ilm, skip.match=false) ## End(Not run)

6 Index Topic cross-platform Topic expression Topic gene Topic inter-platform Topic microarray Topic normalization distran (CONOR), 2 dwd (CONOR), 2 eb (CONOR), 2 gq (CONOR), 2 mrs (CONOR), 2 nordi (CONOR), 2 qd (CONOR), 2 qn (CONOR), 2 xpn (CONOR), 2 6