[Bioc-devel] Deseq2 and differentia expression
Michael Love
michaelisaiahlove at gmail.com
Fri Jul 11 15:15:53 CEST 2014
hi Jarod,
This is more of a main Bioc mailing list question, so you can address
future questions there.
On Fri, Jul 11, 2014 at 6:05 AM, jarod_v6 at libero.it <jarod_v6 at libero.it> wrote:
> 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?
the second and third results() commands below give you this.
> 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!
>
DESeq2 returns the results for all the genes in the same order as the
original object. You need to specify a threshold on adjusted p-value.
table(res$padj < 0.1)
You can use subset(res, padj < 0.1) to filter the DataFrame.
>
>>----Messaggio originale----
>>Da: michaelisaiahlove at gmail.com
>>Data: 10/07/2014 14.46
>>A: "jarod_v6 at libero.it"<jarod_v6 at libero.it>
>>Cc: "bioc-devel at r-project.org"<bioc-devel at r-project.org>
>>Ogg: Re: [Bioc-devel] Deseq2 and differentia expression
>>
>>hi Jarod,
>>
>>On Thu, Jul 10, 2014 at 7:59 AM, jarod_v6 at libero.it <jarod_v6 at libero.it>
> wrote:
>>> Hi there!!!
>>>
>>> I have did this code:
>>> SampleTable <-data.frame(SampleName=metadata$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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