Hello,

I updated today for R version 3.3.0. When I defined DESeqDataSetFromMatrix, I obtained this error which was not the case with R version 3.2.5. when I switched with R-studio from one version to another, the error is still present with the version3.3.0

Any help will be valuable, Thanks in Advance.

Here are my code

AMHdup<-read.csv(file="count_genes_8indi_RmDup.csv", sep = ";",header=TRUE,row.names=1 )

library(DESeq2)
library(HTSFilter)
coldatafile<-read.table(file="colData.txt",sep="\t",header=TRUE,row.names=1)
dds<-DESeqDataSetFromMatrix(countData = AMHdup,
                            colData = coldatafile,
                            design = ~ Treatment)

Error in FUN(X[[i]], ...) : 
  assay colnames() must be NULL or equal colData rownames()

R version 3.3.0 (2016-05-03)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

locale:
[1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252    LC_MONETARY=French_France.1252 LC_NUMERIC=C                  
[5] LC_TIME=French_France.1252    

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] HTSFilter_1.12.0           DESeq2_1.12.3              SummarizedExperiment_1.2.3 Biobase_2.32.0            
[5] GenomicRanges_1.24.1       GenomeInfoDb_1.8.2         IRanges_2.6.0              S4Vectors_0.10.1          
[9] BiocGenerics_0.18.0       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.5          RColorBrewer_1.1-2   plyr_1.8.4           XVector_0.12.0       tools_3.3.0          zlibbioc_1.18.0     
 [7] rpart_4.1-10         RSQLite_1.0.0        annotate_1.50.0      gtable_0.2.0         lattice_0.20-33      Matrix_1.2-6        
[13] DBI_0.4-1            DESeq_1.24.0         gridExtra_2.2.1      genefilter_1.54.2    cluster_2.0.4        locfit_1.5-9.1      
[19] grid_3.3.0           nnet_7.3-12          data.table_1.9.6     AnnotationDbi_1.34.3 XML_3.98-1.4         survival_2.39-4     
[25] BiocParallel_1.6.2   foreign_0.8-66       limma_3.28.6         latticeExtra_0.6-28  Formula_1.2-1        edgeR_3.14.0        
[31] geneplotter_1.50.0   ggplot2_2.1.0        Hmisc_3.17-4         scales_0.4.0         splines_3.3.0        colorspace_1.2-6    
[37] xtable_1.8-2         acepack_1.3-3.3      munsell_0.4.3        chron_2.3-47        

This is a new check from SummarizedExperiment (a dependency of DESeq2)

I'd first check the colnames and rownames to make sure they are in a consistent order:

colnames(AMHdup)
rownames(coldatafile)

If these are as you expected but just not equal, then assign NULL to the colnames of the assay and continue.

colnames(AMHdup) <- NULL

ok it works. Thank

> dds<-DESeqDataSetFromMatrix(countData = AMHdup,
+ colData = coldatafile,
+ design = ~ Treatment)
Error in FUN(X[[i]], ...) :
  assay colnames() must be NULL or equal colData rownames()
> colnames(AMHdup)
[1] "X6Sum"  "X8Sum"  "X9Sum"  "X10Sum" "X14Sum" "X15Sum" "X16Sum" "X17Sum"
> rownames(coldatafile)
[1] "6Sum"  "8Sum"  "9Sum"  "10Sum" "14Sum" "15Sum" "16Sum" "17Sum"
> colnames(AMHdup) <- NULL
> dds
Error: object 'dds' not found
> dds<-DESeqDataSetFromMatrix(countData = AMHdup,
+                             colData = coldatafile,
+                             design = ~ Treatment)
> dds$Treatment<- relevel(dds$Treatment, ref ="CTRL")
> dds

class: DESeqDataSet 
dim: 27054 8 
metadata(1): version
assays(1): counts
rownames(27054): ENSOARG00000000001 ENSOARG00000000002 ... ENSOARG00000027056 ENSOARG00000027057
rowData names(0):
colnames(8): 6Sum 8Sum ... 16Sum 17Sum
colData names(1): Treatment
> dds1<-dds
> dds1
class: DESeqDataSet 
dim: 27054 8 
metadata(1): version
assays(1): counts
rownames(27054): ENSOARG00000000001 ENSOARG00000000002 ... ENSOARG00000027056 ENSOARG00000027057
rowData names(0):
colnames(8): 6Sum 8Sum ... 16Sum 17Sum
colData names(1): Treatment
> dds1<-DESeq(dds1) # on lance l'analyse différentielle
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
> filterHTS<-HTSFilter(dds1,s.len=500, plot=TRUE)$filteredData # on obtient un seuil de 23.889
> class(filterHTS) # on a bien un objet DESEQ2
[1] "DESeqDataSet"
attr(,"package")
[1] "DESeq2"
> dim(filterHTS) # on réduit à 12700
[1] 12700     8