Hello,

I am trying to analyse a dataset that has data from three different sequencing runs, two cell types (one is a homozygous knockout of the other) and four stimuli (plus control). I know from previous analyses of this data that there are batch effects, so I am using a design formula that looks like the following:

design = ~ run + celltype + stimulus + celltype:stimulus

When I attempt to run DESeq2 using this design formula, I get the error "1650 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest"

I don't get the error when I combine celltype and stimulus into a single variable "condition".

I've searched for this error in the manual and online, but cannot figure out what it means. Could somebody please explain it?

Thanks!

Erin

output of sessionInfo(): 

R version 3.1.2 (2014-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
[1] DESeq2_1.6.3            RcppArmadillo_0.4.600.0 Rcpp_0.11.3            
[4] GenomicRanges_1.18.4    GenomeInfoDb_1.2.4      IRanges_2.0.1          
[7] S4Vectors_0.4.0         BiocGenerics_0.12.1    

loaded via a namespace (and not attached):
 [1] acepack_1.3-3.3      annotate_1.44.0      AnnotationDbi_1.28.1
 [4] base64enc_0.1-2      BatchJobs_1.5        BBmisc_1.8          
 [7] Biobase_2.26.0       BiocParallel_1.0.0   brew_1.0-6          
[10] checkmate_1.5.1      cluster_2.0.1        codetools_0.2-10    
[13] colorspace_1.2-4     DBI_0.3.1            digest_0.6.8        
[16] fail_1.2             foreach_1.4.2        foreign_0.8-62      
[19] Formula_1.2-0        genefilter_1.48.1    geneplotter_1.44.0  
[22] ggplot2_1.0.0        grid_3.1.2           gtable_0.1.2        
[25] Hmisc_3.14-6         iterators_1.0.7      lattice_0.20-29     
[28] latticeExtra_0.6-26  locfit_1.5-9.1       MASS_7.3-37         
[31] munsell_0.4.2        nnet_7.3-9           plyr_1.8.1          
[34] proto_0.3-10         RColorBrewer_1.1-2   reshape2_1.4.1      
[37] rpart_4.1-9          RSQLite_1.0.0        scales_0.2.4        
[40] sendmailR_1.2-1      splines_3.1.2        stringr_0.6.2       
[43] survival_2.37-7      tools_3.1.2          XML_3.98-1.1        
[46] xtable_1.7-4         XVector_0.6.0  

hi Erin,

Can you show what the column data looks like? It might be that there is some near-collinearity which poses a problem for the fitting algorithm. Also can you provide all the code you are using for construction and running DESeq2?

The code that I'm using is as follows:

#start R

R

#read in new sample sheet 
samples = read.csv("samples.csv",header=TRUE)

#read in count matrix
counts = read.delim("sumtechreps.csv", col.names=samples$techreps)

#attach the column data to from the sample sheet to the variable coldata
coldata = with(samples,data.frame(shortname = I(shortname), celltype = celltype, run = run, stimulus = stimulus))

#attach the count data to the variable countdata
countdata = counts

#start DESeq2
library("DESeq2")

#construct your DESeq2 data set, making sure to specify the design matrix here
dds <- DESeqDataSetFromMatrix(countData = countdata,
colData = coldata,
design = ~ run + celltype + stimulus + celltype:stimulus)

#relevel controls
dds$celltype <- relevel(dds$celltype, "E")
dds$stimulus <- relevel(dds$stimulus, "C4")

#run DESeq2 on the dataset
dds <- DESeq(dds)

Here is what coldata looks like:

      shortname celltype run stimulus
1  2_E14-Co....        E  r2       C4
2     2_E14-CpG        E  r2      CpG
3  2_E14-TRIDAP        E  r2        T
4     2_E14-LPS        E  r2        L
5  2_E14-In....        E  r2      I24
6  2_IRAK1-....        I  r2       C4
7   2_IRAK1-CpG        I  r2      CpG
8  2_IRAK1-....        I  r2        T
9  2_IRAK1-....        I  r2      I24
10  2_IRAK1-LPS        I  r2        L
11    3_E14-CpG        E  r3      CpG
12 3_E14-TRIDAP        E  r3        T
13 3_E14-Co....        E  r3       C4
14    3_E14-LPS        E  r3        L
15 3_E14-In....        E  r3      I24
16  3_IRAK1-CpG        I  r3      CpG
17 3_IRAK1-....        I  r3        T
18 3_IRAK1-....        I  r3       C4
19  3_IRAK1-LPS        I  r3        L
20 3_IRAK1-....        I  r3      I24
21 1_E14-Co....        E  r1       C4
22    1_E14-LPS        E  r1        L
23    1_E14-CpG        E  r1      CpG
24 1_E14-TRIDAP        E  r1        T
25 1_Influe....        E  r1      I24
26  1_IRAK1-LPS        I  r1        L
27  1_IRAK1-CpG        I  r1      CpG
28 1_IRAK1-....        I  r1        T
29 1_IRAK1-....        I  r1       C4
30 1_IRAK1-....        I  r1      I24