[Bioc-devel] R: Re: Deseq2 and differentia expression

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

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?