[Bioc-devel] Deseq2 and differentia expression

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
>>
>
>