[Bioc-devel] R: Re: Deseq2 and differentia expression
jarod_v6 at libero.it
jarod_v6 at libero.it
Fri Jul 11 12:05:03 CEST 2014
Dear Dr,
Thanks so much for clarification!!!
So I try the test of log fold change but I'm bit confusion on the results:
If I interested in the genes that have a foldchange more than 0.5 and 2 I need
to use this comand is it right?
ddsNoPrior <- DESeq(ddHTSeq, betaPrior=FALSE) #only for lessABs
resGA <- results(ddsNoPrior, lfcThreshold=.5, altHypothesis="lessAbs")
#greater tdi
resGA2 <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs") #greater
tdi
resGA3 <- results(dds, lfcThreshold=2, altHypothesis="greaterAbs") #greater
tdi
dim(resGA)
[1] 62893 6
> dim(resGA2)
[1] 62893 6
> dim(resGA3)
[1] 62893 6
The number of gene select it is always the same.. Where is my mistake!
thanks in advance!
>----Messaggio originale----
>Da: michaelisaiahlove at gmail.com
>Data: 10/07/2014 14.46
>A: "jarod_v6 at libero.it"<jarod_v6 at libero.it>
>Cc: "bioc-devel at r-project.org"<bioc-devel at r-project.org>
>Ogg: Re: [Bioc-devel] Deseq2 and differentia expression
>
>hi Jarod,
>
>On Thu, Jul 10, 2014 at 7:59 AM, jarod_v6 at libero.it <jarod_v6 at libero.it>
wrote:
>> 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?
>
>The code above does the selection on adjusted p-value. The right FDR
>cutoff is up to you, what percent of false discoveries is tolerable in
>the final list of genes? The considerations are: the cost of
>validation or following up on a false discovery, versus the cost of a
>missed discovery. These are hard to quantify even if you know all the
>details of an experiment.
>
>> All the data have a FDR less thank 0.1 . :
>> Is it right this comand?
>> res[ which(res$padj < 0.1 ), ]
>>
>
>yes. The which() is necessary because some of the res$padj have NA. If
>you have a logical vector with NA, you cannot directly index a
>DataFrame, but you can index after calling which(), which will return
>the numeric index of the TRUE's. You could also subset with:
>subset(res, padj < 0.1).
>
>The reason for the NAs is explained in the vignette: "Note that some
>values in the results table can be set to NA, for either one of the
>following reasons:..."
>
>
>> 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
>>
>
>This is not the recommended way to filter on large log fold changes.
>We have implemented a test specifically for this, check the vignette
>section on "Tests of log2 fold change above or below a threshold"
>
>Mike
>
>> 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
>>
>> _______________________________________________
>> Bioc-devel at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/bioc-devel
>
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