Hi!

I have  a set of experiments where I try to detect the differential expression genes between the over-expression of a particular  fusion genes. One  of the gene of the chimera are differential express the other no. This is strange because we have some evidence on the wet lab,

Another think is I found  only few differential expression genes. This is strange.

out of 15361 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 21, 0.14%
LFC < 0 (down)   : 3, 0.02%
outliers [1]     : 14, 0.091%
low counts [2]   : 0, 0%
(mean count < 3)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

in some comparison I obtain only this:

out of 15530 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 2, 0.013%
LFC < 0 (down)   : 0, 0%
outliers [1]     : 100, 0.64%
low counts [2]   : 0, 0%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

I know one of the gene of the chimera are probably filter out as outliers for this reason:

Even if I use  this comand:

dds <- DESeq(dds,minReplicatesForReplace=Inf)

I obtain the same problem.

What Can I do?

R version 3.2.3 (2015-12-10)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS release 6.7 (Final)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] edgeR_3.12.0               pheatmap_1.0.8             genefilter_1.52.1          ggplot2_2.0.0             
 [5] limma_3.26.7               RColorBrewer_1.1-2         gplots_2.17.0              org.Hs.eg.db_3.2.3        
 [9] RSQLite_1.0.0              DBI_0.3.1                  annotate_1.48.0            XML_3.98-1.3              
[13] AnnotationDbi_1.32.3       DESeq2_1.10.1              RcppArmadillo_0.6.500.4.0  Rcpp_0.12.3               
[17] SummarizedExperiment_1.0.2 Biobase_2.30.0             GenomicRanges_1.22.4       GenomeInfoDb_1.6.3        
[21] IRanges_2.4.6              S4Vectors_0.8.11           BiocGenerics_0.16.1        biomaRt_2.26.1            

loaded via a namespace (and not attached):
 [1] statmod_1.4.24       gtools_3.5.0         locfit_1.5-9.1       splines_3.2.3        lattice_0.20-33     
 [6] colorspace_1.2-6     yaml_2.1.13          survival_2.38-3      foreign_0.8-66       BiocParallel_1.4.3  
[11] lambda.r_1.1.7       plyr_1.8.3           zlibbioc_1.16.0      munsell_0.4.2        gtable_0.1.2        
[16] futile.logger_1.4.1  caTools_1.17.1       labeling_0.3         latticeExtra_0.6-26  geneplotter_1.48.0  
[21] acepack_1.3-3.3      KernSmooth_2.23-15   xtable_1.8-0         scales_0.3.0         gdata_2.17.0        
[26] Hmisc_3.17-1         XVector_0.10.0       gridExtra_2.0.0      digest_0.6.9         grid_3.2.3          
[31] tools_3.2.3          bitops_1.0-6         RCurl_1.95-4.7       Formula_1.2-1        cluster_2.0.3       
[36] futile.options_1.0.0 rpart_4.1-10         nnet_7.3-12   

First thing is to see if you can convince yourself that the data tells you that your gene of interest is differentially expressed.

If you use the plotCounts function from DESeq2 to plot the expression of your gene of interest, does the data support your intuition?

Have you looked PCA or MDS plots of your data to how strong your treatment effect is? From the results you show, it seems like the effect is rather minimal.

How many replicates do you have? When effect sizes are minimal, higher replication can give you more power to detect differential expression.

You say you have "some evidence in the wet lab". Presumably you've done some qPCR on a panel of genes to look at the effect of over-expression of your fusion gene on a handful of transcripts? Does your RNA-seq show similar changes in expression as the evidence you are referring to across the same genes?

I suggest doing quality assessment / control to see whether all of the libraries are good. Garbage in → garbage out.

The DESeq2 vignette is one place to find suggestions for quality related plots, but also consider statistics such as number of reads, number of uniquely aligned reads, etc.

Thanks so much for the suggestions:

So I have 3 replicated for condition and  6 controls. In the ctrl I have no expression of my fusion gene or over-expression of  GFP. The chimera are composed by both gene. One is differentialy expressed the other are not reported. Programs for the detection of fusion like FusionCatcher   are able to detect the fusion

For the evidence of wet lab I have qpcr and western blot of the expression of this chimera and this genes. Here you have a plot counts show the value of that gene are not considered http://imgur.com/tOYJx9Y :

Here you can found a pca and show what do you suggest but I have 3 replicates. http://imgur.com/wl9rGVK 

All the samples have more that 27 ML of unique reads aligned. I did one set of experiments using 40ML of reads but no increase power was detected.

Any suggestion?