During non-specific filtering, I am using parameters for filtering probes (require.entrez=TRUE, remove.dupEntrez=TRUE,feature.exclude="^AFFX) in addition to the filters of intensity and variance. Independently, both filters works fine, but when I try to use them together, I am getting an error written below: Error in apply(expr, 1, flist) : dim(X) must have a positive length Please help me with this. I have pasted the code below. #1.Getting the data source("http://bioconductor.org/biocLite.R") biocLite("GEOquery") biocLite("affycoretools") library(GEOquery) setwd("/home/vinay/R/R-3.0.2") getGEOSuppFiles("GSE6631") setwd("/home/vinay/R/R-3.0.2/GSE6631") system("tar -xvf GSE6631_RAW.tar") cels <- list.files( pattern = "[gz]") sapply(cels, gunzip) #2.Loading and normalising the data using GC-RMA # You may need to copy your phenodata.txt file into the GSE6631 folder library(affy) library(affycoretools) data <- ReadAffy() pData(data)<-read.table("phenodata.txt", header=T,row.names=1, sep="\t") pData(data) eset <- gcrma(data) eset dim(eset) pData(eset) write.exprs(eset, file="Expression_values_GCRMA_normalize.xls") eset2<-eset[,pData(eset)[,"Condition"]%in%c("Normal","Cancer")] #3. Non-specific Filtering data library(genefilter) celfiles_filtered <- nsFilter(eset2, require.entrez=TRUE, remove.dupEntrez=TRUE,feature.exclude="^AFFX") f1<-pOverA(0.10,log2(100)) #intensity filter-the intensity of a gene should be above log2(100) in at least 25 percent of the samples f2<-function(x)(IQR(x)>0.5) #variance filter-the interquartile range of log2???intensities should be at least 0.5 ff<-filterfun(f1,f2) selected<-genefilter(celfiles_filtered,ff) -- output of sessionInfo(): R version 3.0.2 (2013-09-25) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_IN LC_NUMERIC=C LC_TIME=en_IN [4] LC_COLLATE=en_IN LC_MONETARY=en_IN LC_MESSAGES=en_IN [7] LC_PAPER=en_IN LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=en_IN LC_IDENTIFICATION=C attached base packages: [1] parallel stats graphics grDevices utils datasets methods [8] base other attached packages: [1] hgu95av2.db_2.10.1 org.Hs.eg.db_2.10.1 [3] arrayQualityMetrics_3.18.0 affyPLM_1.38.0 [5] preprocessCore_1.24.0 RColorBrewer_1.0-5 [7] hgu95av2probe_2.13.0 affycoretools_1.34.0 [9] KEGG.db_2.10.1 GO.db_2.10.1 [11] RSQLite_0.11.4 DBI_0.2-7 [13] limma_3.18.12 hgu95av2cdf_2.13.0 [15] AnnotationDbi_1.24.0 simpleaffy_2.38.0 [17] genefilter_1.44.0 gcrma_2.34.0 [19] affy_1.40.0 GEOquery_2.28.0 [21] Biobase_2.22.0 BiocGenerics_0.8.0 [23] BiocInstaller_1.12.0 loaded via a namespace (and not attached): [1] affyio_1.30.0 annaffy_1.34.0 annotate_1.40.0 [4] AnnotationForge_1.4.4 beadarray_2.12.0 BeadDataPackR_1.14.0 [7] biomaRt_2.18.0 Biostrings_2.30.1 biovizBase_1.10.7 [10] bit_1.1-11 bitops_1.0-6 BSgenome_1.30.0 [13] Cairo_1.5-5 Category_2.28.0 caTools_1.16 [16] cluster_1.14.4 codetools_0.2-8 colorspace_1.2-4 [19] DESeq2_1.2.10 dichromat_2.0-0 digest_0.6.4 [22] edgeR_3.4.2 ff_2.2-12 foreach_1.4.1 [25] Formula_1.1-1 gdata_2.13.2 GenomicFeatures_1.14.2 [28] GenomicRanges_1.14.4 ggbio_1.10.11 ggplot2_0.9.3.1 [31] GOstats_2.28.0 gplots_2.12.1 graph_1.40.1 [34] grid_3.0.2 gridExtra_0.9.1 GSEABase_1.24.0 [37] gtable_0.1.2 gtools_3.3.0 Hmisc_3.14-0 [40] hwriter_1.3 IRanges_1.20.6 iterators_1.0.6 [43] KernSmooth_2.23-10 labeling_0.2 lattice_0.20-24 [46] latticeExtra_0.6-26 locfit_1.5-9.1 MASS_7.3-29 [49] Matrix_1.1-2 munsell_0.4.2 oligoClasses_1.24.0 [52] PFAM.db_2.10.1 plyr_1.8 proto_0.3-10 [55] R2HTML_2.2.1 RBGL_1.38.0 Rcpp_0.11.0 [58] RcppArmadillo_0.4.000.2 RCurl_1.95-4.1 ReportingTools_2.2.0 [61] reshape2_1.2.2 R.methodsS3_1.6.1 R.oo_1.17.0 [64] Rsamtools_1.14.3 rtracklayer_1.22.3 R.utils_1.29.8 [67] scales_0.2.3 setRNG_2011.11-2 splines_3.0.2 [70] stats4_3.0.2 stringr_0.6.2 survival_2.37-7 [73] SVGAnnotation_0.93-1 tcltk_3.0.2 tools_3.0.2 [76] VariantAnnotation_1.8.12 vsn_3.30.0 XML_3.98-1.1 [79] xtable_1.7-1 XVector_0.2.0 zlibbioc_1.8.0 > -- Sent via the guest posting facility at bioconductor.org.

Hi Vinay a look in the man page of ?nsFilter? indicates that its output is a list, one of whose elements is ? eset?, the filtered ExpressionSet. You could try (I haven?t checked) with selected<-genefilter(celfiles_filtered$est, ff) But I also wonder why you would want to do this? DId you explore the ' var.cutoff?, ?filterByQuantile? arguments of ?nsFilter?? Wolfgang On 18 Feb 2014, at 05:07, Vinay Randhawa [guest] <guest at="" bioconductor.org=""> wrote: > > During non-specific filtering, I am using parameters for filtering probes (require.entrez=TRUE, remove.dupEntrez=TRUE,feature.exclude="^AFFX) in addition to the filters of intensity and variance. Independently, both filters works fine, but when I try to use them together, I am getting an error written below: > Error in apply(expr, 1, flist) : dim(X) must have a positive length > > > Please help me with this. > > > I have pasted the code below. > > #1.Getting the data > source("http://bioconductor.org/biocLite.R") > biocLite("GEOquery") > biocLite("affycoretools") > library(GEOquery) > setwd("/home/vinay/R/R-3.0.2") > getGEOSuppFiles("GSE6631") > setwd("/home/vinay/R/R-3.0.2/GSE6631") > > system("tar -xvf GSE6631_RAW.tar") > cels <- list.files( pattern = "[gz]") > sapply(cels, gunzip) > > #2.Loading and normalising the data using GC-RMA > # You may need to copy your phenodata.txt file into the GSE6631 folder > library(affy) > library(affycoretools) > data <- ReadAffy() > pData(data)<-read.table("phenodata.txt", header=T,row.names=1, sep="\t") > pData(data) > eset <- gcrma(data) > eset > dim(eset) > pData(eset) > write.exprs(eset, file="Expression_values_GCRMA_normalize.xls") > eset2<-eset[,pData(eset)[,"Condition"]%in%c("Normal","Cancer")] > > > #3. Non-specific Filtering data > library(genefilter) > celfiles_filtered <- nsFilter(eset2, require.entrez=TRUE, remove.dupEntrez=TRUE,feature.exclude="^AFFX") > f1<-pOverA(0.10,log2(100)) #intensity filter-the intensity of a gene should be above log2(100) in at least 25 percent of the samples > f2<-function(x)(IQR(x)>0.5) #variance filter-the interquartile range of log2???intensities should be at least 0.5 > ff<-filterfun(f1,f2) > selected<-genefilter(celfiles_filtered,ff) > > > > > > > -- output of sessionInfo(): > > R version 3.0.2 (2013-09-25) > Platform: x86_64-unknown-linux-gnu (64-bit) > > locale: > [1] LC_CTYPE=en_IN LC_NUMERIC=C LC_TIME=en_IN > [4] LC_COLLATE=en_IN LC_MONETARY=en_IN LC_MESSAGES=en_IN > [7] LC_PAPER=en_IN LC_NAME=C LC_ADDRESS=C > [10] LC_TELEPHONE=C LC_MEASUREMENT=en_IN LC_IDENTIFICATION=C > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods > [8] base > > other attached packages: > [1] hgu95av2.db_2.10.1 org.Hs.eg.db_2.10.1 > [3] arrayQualityMetrics_3.18.0 affyPLM_1.38.0 > [5] preprocessCore_1.24.0 RColorBrewer_1.0-5 > [7] hgu95av2probe_2.13.0 affycoretools_1.34.0 > [9] KEGG.db_2.10.1 GO.db_2.10.1 > [11] RSQLite_0.11.4 DBI_0.2-7 > [13] limma_3.18.12 hgu95av2cdf_2.13.0 > [15] AnnotationDbi_1.24.0 simpleaffy_2.38.0 > [17] genefilter_1.44.0 gcrma_2.34.0 > [19] affy_1.40.0 GEOquery_2.28.0 > [21] Biobase_2.22.0 BiocGenerics_0.8.0 > [23] BiocInstaller_1.12.0 > > loaded via a namespace (and not attached): > [1] affyio_1.30.0 annaffy_1.34.0 annotate_1.40.0 > [4] AnnotationForge_1.4.4 beadarray_2.12.0 BeadDataPackR_1.14.0 > [7] biomaRt_2.18.0 Biostrings_2.30.1 biovizBase_1.10.7 > [10] bit_1.1-11 bitops_1.0-6 BSgenome_1.30.0 > [13] Cairo_1.5-5 Category_2.28.0 caTools_1.16 > [16] cluster_1.14.4 codetools_0.2-8 colorspace_1.2-4 > [19] DESeq2_1.2.10 dichromat_2.0-0 digest_0.6.4 > [22] edgeR_3.4.2 ff_2.2-12 foreach_1.4.1 > [25] Formula_1.1-1 gdata_2.13.2 GenomicFeatures_1.14.2 > [28] GenomicRanges_1.14.4 ggbio_1.10.11 ggplot2_0.9.3.1 > [31] GOstats_2.28.0 gplots_2.12.1 graph_1.40.1 > [34] grid_3.0.2 gridExtra_0.9.1 GSEABase_1.24.0 > [37] gtable_0.1.2 gtools_3.3.0 Hmisc_3.14-0 > [40] hwriter_1.3 IRanges_1.20.6 iterators_1.0.6 > [43] KernSmooth_2.23-10 labeling_0.2 lattice_0.20-24 > [46] latticeExtra_0.6-26 locfit_1.5-9.1 MASS_7.3-29 > [49] Matrix_1.1-2 munsell_0.4.2 oligoClasses_1.24.0 > [52] PFAM.db_2.10.1 plyr_1.8 proto_0.3-10 > [55] R2HTML_2.2.1 RBGL_1.38.0 Rcpp_0.11.0 > [58] RcppArmadillo_0.4.000.2 RCurl_1.95-4.1 ReportingTools_2.2.0 > [61] reshape2_1.2.2 R.methodsS3_1.6.1 R.oo_1.17.0 > [64] Rsamtools_1.14.3 rtracklayer_1.22.3 R.utils_1.29.8 > [67] scales_0.2.3 setRNG_2011.11-2 splines_3.0.2 > [70] stats4_3.0.2 stringr_0.6.2 survival_2.37-7 > [73] SVGAnnotation_0.93-1 tcltk_3.0.2 tools_3.0.2 > [76] VariantAnnotation_1.8.12 vsn_3.30.0 XML_3.98-1.1 > [79] xtable_1.7-1 XVector_0.2.0 zlibbioc_1.8.0 >> > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor