[Bioc-devel] Deseq2 and differentia expression

[jarod_v6 at libero.it]

Hi there!!!

I have did this code:
SampleTable <-data.frame(SampleName=metadata$ID_CLINICO,fileName=metadata$NOME,
condition=metadata$CONDITION,prim=metadata$CDT)
ddHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable=SampleTable,directory="
Count/", design= ~condition) # effetto dello mutazione
ddHTSeq$condition <- relevel(ddHTSeq$condition, "NVI")# quindi verso non 
viscerali
dds <- DESeq(ddHTSeq)
res <-results(dds)

resOrdered <- res[order(res$padj),]
head(resOrdered)
ResSig <- res[ which(res$padj < 0.1 ), ]


I want to select some data. How can I do? which is the good cut-off on FDR 
values? 
All the data have a FDR less thank 0.1 . :
Is it right this comand? 
res[ which(res$padj < 0.1 ), ]

How many significant genes are with FDR less than 0.1 and  have an absolute 
value of  foldchange  more of 1 ? I  have and error on this. I have many NA 
values.

If I try this code I have the follow errors
> significant.genes = res[(res$padj < .05 & abs(res$log2FoldChange) >= 1 ),] # 
Set thethreshold for the log2 fold change.
Error in normalizeSingleBracketSubscript(i, x, byrow = TRUE, exact = FALSE) : 
  subscript contains NAs

How can I resolve this problenms?
thanks in advance for the help



R version 3.1.0 (2014-04-10)
Platform: i686-pc-linux-gnu (32-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] annotate_1.40.1         RColorBrewer_1.0-5      gplots_2.14.1          
 [4] org.Hs.eg.db_2.10.1     ReportingTools_2.4.0    AnnotationDbi_1.24.0   
 [7] RSQLite_0.11.4          DBI_0.2-7               knitr_1.6              
[10] biomaRt_2.18.0          DESeq2_1.4.5            RcppArmadillo_0.4.320.0
[13] Rcpp_0.11.2             GenomicRanges_1.14.4    XVector_0.2.0          
[16] IRanges_1.20.7          affy_1.40.0             NOISeq_2.6.0           
[19] Biobase_2.22.0          BiocGenerics_0.8.0     

loaded via a namespace (and not attached):
 [1] affyio_1.30.0            AnnotationForge_1.4.4    BiocInstaller_1.
12.1    
 [4] Biostrings_2.30.1        biovizBase_1.10.8        bitops_1.0-
6            
 [7] BSgenome_1.30.0          Category_2.28.0          caTools_1.
17            
[10] cluster_1.15.2           colorspace_1.2-4         dichromat_2.0-
0         
[13] digest_0.6.4             edgeR_3.4.2              evaluate_0.
5.5          
[16] formatR_0.10             Formula_1.1-1            gdata_2.
13.3            
[19] genefilter_1.44.0        geneplotter_1.40.0       GenomicFeatures_1.
14.5  
[22] ggbio_1.10.16            ggplot2_1.0.0            GO.db_2.
10.1            
[25] GOstats_2.28.0           graph_1.40.1             grid_3.
1.0              
[28] gridExtra_0.9.1          GSEABase_1.24.0          gtable_0.
1.2            
[31] gtools_3.4.1             Hmisc_3.14-4             hwriter_1.
3             
[34] KernSmooth_2.23-12       lattice_0.20-29          latticeExtra_0.6-
26     
[37] limma_3.18.13            locfit_1.5-9.1           MASS_7.3-
33             
[40] Matrix_1.1-4             munsell_0.4.2            PFAM.db_2.
10.1          
[43] plyr_1.8.1               preprocessCore_1.24.0    proto_0.3-
10            
[46] RBGL_1.38.0              RCurl_1.95-4.1           reshape2_1.
4            
[49] R.methodsS3_1.6.1        R.oo_1.18.0              Rsamtools_1.
14.3        
[52] rtracklayer_1.22.7       R.utils_1.32.4           scales_0.
2.4            
[55] stats4_3.1.0             stringr_0.6.2            survival_2.37-
7         
[58] tools_3.1.0              VariantAnnotation_1.8.13 XML_3.98-
1.1            
[61] xtable_1.7-3             zlibbioc_1.8.0