* using log directory 'd:/Rcompile/CRANpkg/local/2.13/NanoStringNorm.Rcheck' * using R version 2.13.2 (2011-09-30) * using platform: i386-pc-mingw32 (32-bit) * using session charset: ISO8859-1 * checking for file 'NanoStringNorm/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'NanoStringNorm' version '1.1.3' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking whether package 'NanoStringNorm' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking for portable file names ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... 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OK * checking sizes of PDF files under inst/doc ... OK * checking examples ... ERROR Running examples in 'NanoStringNorm-Ex.R' failed The error most likely occurred in: > ### Name: other.normalization > ### Title: other.normalization > ### Aliases: other.normalization > ### Keywords: NanoString Normalization mRNA miRNA > > ### ** Examples > > > # load the NanoString.mRNA dataset > data(NanoString); > > # specify housekeeping genes in annotation > NanoString.mRNA[NanoString.mRNA$Name %in% + c('Eef1a1','Gapdh','Hprt1','Ppia','Sdha'),'Code.Class'] <- 'Housekeeping'; > > # z-value transformation. scale each sample to have a mean 0 and sd > # by default all the other normalization methods are 'none' > # you cannot apply a log because there are negative values > # good for meta-analysis and cross platform comparison abstraction of effect size > NanoString.mRNA.norm <- NanoStringNorm( + x = NanoString.mRNA, + otherNorm = 'zscore', + return.matrix.of.endogenous.probes = TRUE + ); ############################## ### NanoStringNorm v1.1.3 ### ############################## There are 25 samples and 49 Endogenous genes OtherNorm.zscore: The following genes will not be processed: Code.Class Name 1 Positive POS_A(128) 2 Positive POS_B(32) 3 Positive POS_C(8) 4 Positive POS_D(2) 5 Positive POS_E(0.5) 6 Positive POS_F(0.125) 7 Negative NEG_A(0) 8 Negative NEG_B(0) 9 Negative NEG_C(0) 10 Negative NEG_D(0) 11 Negative NEG_E(0) 12 Negative NEG_F(0) 13 Negative NEG_G(0) 14 Negative NEG_H(0) 27 Housekeeping Eef1a1 32 Housekeeping Gapdh 37 Housekeeping Hprt1 54 Housekeeping Ppia 62 Housekeeping Sdha > > > # inverse normal transformation. use quantiles to transform each sample to the normal distribution > NanoString.mRNA.norm <- NanoStringNorm( + x = NanoString.mRNA, + otherNorm = 'rank.normal', + return.matrix.of.endogenous.probes = TRUE + ); ############################## ### NanoStringNorm v1.1.3 ### ############################## There are 25 samples and 49 Endogenous genes OtherNorm.rank.normal: The following genes will not be processed: Code.Class Name 1 Positive POS_A(128) 2 Positive POS_B(32) 3 Positive POS_C(8) 4 Positive POS_D(2) 5 Positive POS_E(0.5) 6 Positive POS_F(0.125) 7 Negative NEG_A(0) 8 Negative NEG_B(0) 9 Negative NEG_C(0) 10 Negative NEG_D(0) 11 Negative NEG_E(0) 12 Negative NEG_F(0) 13 Negative NEG_G(0) 14 Negative NEG_H(0) 27 Housekeeping Eef1a1 32 Housekeeping Gapdh 37 Housekeeping Hprt1 54 Housekeeping Ppia 62 Housekeeping Sdha > > # quantile normalization. create an empirical distribution based on the median gene counts at the same rank across sample. then transform each sample to the empirical distribution. > NanoString.mRNA.norm <- NanoStringNorm( + x = NanoString.mRNA, + otherNorm = 'quantile', + return.matrix.of.endogenous.probes = FALSE + ); ############################## ### NanoStringNorm v1.1.3 ### ############################## There are 25 samples and 49 Endogenous genes OtherNorm.quantile: The following genes will not be processed: Code.Class Name 1 Positive POS_A(128) 2 Positive POS_B(32) 3 Positive POS_C(8) 4 Positive POS_D(2) 5 Positive POS_E(0.5) 6 Positive POS_F(0.125) 7 Negative NEG_A(0) 8 Negative NEG_B(0) 9 Negative NEG_C(0) 10 Negative NEG_D(0) 11 Negative NEG_E(0) 12 Negative NEG_F(0) 13 Negative NEG_G(0) 14 Negative NEG_H(0) 27 Housekeeping Eef1a1 32 Housekeeping Gapdh 37 Housekeeping Hprt1 54 Housekeeping Ppia 62 Housekeeping Sdha > > # vsn. apply a variance stabilizing normalization. > # fit and predict the model using 'all' genes i.e. 'controls' and 'endogenous'. this is the default > # note this is just a wrapper for the vsn package > # you could even add strata for the controls vs the endogenous to review systematic differences > NanoString.mRNA.norm <- NanoStringNorm( + x = NanoString.mRNA, + otherNorm = 'vsn', + return.matrix.of.endogenous.probes = FALSE, + genes.to.fit = 'all', + genes.to.predict = 'all' + ); ############################## ### NanoStringNorm v1.1.3 ### ############################## There are 25 samples and 49 Endogenous genes Loading required package: vsn Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material. To view, type 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object(s) are masked from 'package:gdata': combine > > # vsn. this time generate the parameters (fit the model) on the 'controls' and apply (predict) on the endogenous > # alternatively you may want to use the 'controls' for both fitting and predicting. > NanoString.mRNA.norm <- NanoStringNorm( + x = NanoString.mRNA, + otherNorm = 'vsn', + return.matrix.of.endogenous.probes = FALSE, + genes.to.fit = 'controls', + genes.to.predict = 'endogenous' + ); ############################## ### NanoStringNorm v1.1.3 ### ############################## There are 25 samples and 49 Endogenous genes Error: isSmall(rsv@mu - hmean) is not TRUE Execution halted