[dpryan]

If you're trying to do a comparison, the edgeR equivalent would be the "RLE" method in calcNormFactors(). estimateSizeFactors() doesn't take an option in this regard (see help(estimateSizeFactors) for what options it does have).

[sindrle]

Ah!
Always a good answer from you. Thanks!

[alisrpp]

The local fit helps you out by dipping down at the high counts, giving you lower estimates of dispersion there than by taking the mean overall. The large circles are genes whose dispersion estimates are not shrunk because they deviate too far from the fitted line. The local fit is useable as well I would say.

I presume -- because you haven't posted the output of sessionInfo() -- that you could be using a version of DESeq2 which performs automatic independent filtering to optimize the number of rejections. Check the Details section of ?results and the argument 'independentFiltering'.

Here is my sessionInfo()

> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] gplots_2.11.3           MASS_7.3-29             KernSmooth_2.23-10      caTools_1.14           
 [5] gdata_2.13.2            gtools_3.1.0            RColorBrewer_1.0-5      DESeq2_1.2.0           
 [9] RcppArmadillo_0.3.920.1 Rcpp_0.10.5             GenomicRanges_1.14.1    XVector_0.2.0          
[13] IRanges_1.20.0          BiocGenerics_0.8.0     

loaded via a namespace (and not attached):
 [1] annotate_1.40.0      AnnotationDbi_1.24.0 Biobase_2.22.0       bitops_1.0-6        
 [5] DBI_0.2-7            genefilter_1.44.0    lattice_0.20-24      locfit_1.5-9.1      
 [9] RSQLite_0.11.4       splines_3.0.2        stats4_3.0.2         survival_2.37-4     
[13] tools_3.0.2          XML_3.95-0.2         xtable_1.7-1

I'm still having some questions about the independent filtering. I am understanding that I have to perform it right after the:

dds<- estimateSizeFactors(dds)
ddsLocal <- estimateDispersions(dds, fitType="local")
ddsLocal <- nbinomWaldTest(ddsLocal)
res <- results(ddsLocal, name= "condition_MR_vs_BR") 
res <- res[order(res$padj),]

With the code:

use <- res$baseMean >= 10 & !is.na(res$pvalue)
resFilt <- res [use, ]
resFilt$padj <- p.adjust(resFilt$pvalue, method="BH")

And then continue with (for example):

#filter for upregulated and downregulated genes
resSig <- resFilt[ resFilt$padj < .1, ]
write.table(resSig[ order( resSig$log2FoldChange, -resSig$baseMean ), ]
            ,"~\\DESeq2\\BRvsMR_old_DownRegulated.txt")
write.table(resSig[ order( -resSig$log2FoldChange, -resSig$baseMean ),
                    ],"~\\DESeq2\\BRvsMR_old_UpRegulated.txt")

But i also found this code:

filterThreshold <- 2.0
keep <- rowMeans ( counts(ddsLocal, normalized=TRUE)) > filterThreshold

And now i'm really confused. What is this for? (the last code) When i have to use it? Am I doing something wrong?

Thank you again for the help.

[Michael Love]

You are using DESeq2 v1.2 and reading the documentation off the Bioc website for version v1.0.

This is why I recommended you to look up the man page for ?results.

And it's always safer to use:

browseVignettes("DESeq2")

to ensure you are reading the documentation for the version you are using.

[alisrpp]

You are using DESeq2 v1.2 and reading the documentation off the Bioc website for version v1.0.

This is why I recommended you to look up the man page for ?results.

And it's always safer to use:

browseVignettes("DESeq2")

to ensure you are reading the documentation for the version you are using.

Ups! Thanks!

By using:

browseVignettes("DESeq2")

i am obtaining

/library/DESeq2/doc/DESeq2.pdf not found

Any idea about why this is happening?

[i4u412]

Hi

I just started using DEseq2, so please correct if i am wrong.
My sample description has five time points (with 2 replicates).
(t0,t0,t1,t1,t2,t2,t3,t3,t4,t4,t5,t5)

if i set the timepoint design, i always get the differential expression through initial time point (control).

In addition to this, i also want to have differential expression with respect to the immediate time points (like t1 to t0, t2 to t1, t3 to t2, t4 to t3 and t5 to t4). Can you please tell me how to set the factor levels to get this type of comparison.

thank you

[alisrpp]

Hi,

I'm using DESeq2_1.2.0 and I am having this error:

> resSig <- res[ res$padj < .1, ]
Error in normalizeSingleBracketSubscript(i, x, byrow = TRUE, exact = FALSE) : 
  subscript contains NAs

It's really weird because this error started today and I have been using the same datasets and the same Rscript for the last 3 weeks without this error coming up.

Just in case, here is my code:

#Count matrix input
Cele_SPvsLR_old = read.csv (file.choose(), header=TRUE, row.names=1)
CeleDesign <- data.frame(
  row.names = colnames(Cele_SPvsLR_old),
  condition = factor(c("SP", "SP", "LR", "LR")))
dds <- DESeqDataSetFromMatrix(countData = Cele_SPvsLR_old,
                              colData = CeleDesign,
                              design = ~ condition)
dds

#Est size factor = normalize for library size
dds<- estimateSizeFactors(dds)
ddsLocal <- estimateDispersions(dds, fitType="local")
ddsLocal <- nbinomWaldTest(ddsLocal)
plotDispEsts(ddsLocal)

#Differential expression analysis
resultsNames(ddsLocal)
res <- results(ddsLocal, name= "condition_SP_vs_LR") 
res <- res[order(res$padj),]
head(res)
plotMA(ddsLocal, ylim=c(-2,2), main="DESeq2")
sum(res$padj < .1, na.rm=TRUE)

#filter for significant genes 
resSig <- res[ res$padj < 0.1, ]

And my sessionInfo()

> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] DESeq2_1.2.0            RcppArmadillo_0.3.920.1 Rcpp_0.10.5             GenomicRanges_1.14.1   
[5] XVector_0.2.0           IRanges_1.20.0          BiocGenerics_0.8.0     

loaded via a namespace (and not attached):
 [1] annotate_1.40.0      AnnotationDbi_1.24.0 Biobase_2.22.0       DBI_0.2-7           
 [5] genefilter_1.44.0    grid_3.0.2           lattice_0.20-24      locfit_1.5-9.1      
 [9] RColorBrewer_1.0-5   RSQLite_0.11.4       splines_3.0.2        stats4_3.0.2        
[13] survival_2.37-4      tools_3.0.2          XML_3.95-0.2         xtable_1.7-1

Can anyone give me an explanation about why am I having this error now but not in the past using exactly the same Rscript and datasets? Does anyone know how to fix it?

Thanks!

[Simon Anders]

Please use

resSig <- res[ which( res$padj < .1 ), ]

to get rid of the NAs.

[sindrle]

Can I ask why you set it to .1 ?

[David [R]]

contrast argument

Hi,

I have started using DESeq2 on a multi-factor design. Everything goes fine until I try to use the "contrast" argument on the "results" function in order to get somparisons other than the ones concerning the control condition.

This is what I get:
> res.R24.OE1=results(dds.gtype,contrast=c("gtype","R24","OE1"))
Error in results(dds.gtype, contrast = c("gtype", "R24", "OE1")) :
unused argument (contrast = c("gtype", "R24", "OE1"))

"gtype" being my factor and "R24" and "OE1" my two conditions.

I would be very grateful if you could help me out on this one.

Thanks!

[fpesce]

Hi,
I am using DESeq2 version 1.2.5 and when I use this design:

> design(dds) <- formula(~ AGE + SEX + CONDITION)

Defining the variables:
> colData(dds)$CONDITION <- relevel(colData(dds)$CONDITION, "R")
> colData(dds)$SEX <- relevel(colData(dds)$SEX, "M")
Whereas AGE is a continuous variable

I get this error:
> dds <- DESeq(dds)
using pre-existing size factors
estimating dispersions
you had estimated dispersions, replacing these
gene-wise dispersion estimates
error: inv(): matrix appears to be singular

On the other hand, there are no problems with these combinations:
> design(dds) <- formula(~ AGE)
> design(dds) <- formula(~ CONDITION)
> design(dds) <- formula(~ SEX + CONDITION)

While I get the same error with:
> design(dds) <- formula(~ AGE + CONDITION)

Any clue what's going on here?
Thank you so much in advance for your help

[dpryan]

Could "AGE" have been converted to a factor at some point?

[fpesce]

Could "AGE" have been converted to a factor at some point?

No it has not. I checked:

> is(colData(dds)$AGE)
[1] "numeric" "vector" "atomic" "vectorORfactor"

Thanks for the suggestion

[Michael Love]

Can you please post colData(dds) (or some scrubbed version of it)? This helps me figure out what the design matrix looks like, and therefore why some designs are causing errors.

[fpesce]

Can you please post colData(dds) (or some scrubbed version of it)? This helps me figure out what the design matrix looks like, and therefore why some designs are causing errors.

Here it is:
> colData(dds)
DataFrame with 122 rows and 4 columns
CONDITION KEEP AGE SEX
<factor> <integer> <numeric> <factor>
ID1 A 1 47.06913 F
ID2 A 1 45.33333 M
ID3 A 1 59.63039 F
ID4 A 1 49.00753 M
ID5 A 1 34.94319 M
... ... ... ... ...
ID118 B 1 30.00684 M
ID119 B 1 41.90828 F
ID120 B 1 39.04449 F
ID121 B 1 39.64682 M
ID122 B 1 16.70363 M