Hi,

I am using DESeq2 for DEG analysis in my RNA-Seq experiment. I have 2 bacterial strains: one is the wild type and the other is a mutant (it lacks one plasmid). When I look for differentially expressed genes, plotting the results with MAplot I obtain a strange plot, which seems not to have the "classical" symmetrical shape (like the one that is reported in the tutorial).

Here is the codes:

txi.tx <- tximport(files, type = "salmon", txOut = TRUE)

library("DESeq2")
samples <- data.frame(species = c("2011","2011","2011","delta","delta","delta")) # sample metadata 
ddsTxi <- DESeqDataSetFromTximport(txi.tx, colData = samples, design = ~species) 

res <- t(apply(counts(ddsTxi),1, function(x)by(x, samples$species, sum)))
keep <- res[,2] > 0
ddsTxi <- ddsTxi[keep,]

keep <- rowSums(counts(ddsTxi)) >= 10
ddsTxi <- ddsTxi[keep,]

ddsTxi$species<- factor(ddsTxi$species, levels = c("2011","delta"))

ddsTxi <- DESeq(ddsTxi, fitType = "local")
res <- lfcShrink(ddsTxi, coef = "species_delta_vs_2011",  type = "normal")

plotMA(res, ylim=c(-5,5))

http://i67.tinypic.com/9jhqph.png

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 8 (jessie)

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.0
LAPACK: /usr/lib/lapack/liblapack.so.3.0

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

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

other attached packages:
 [1] DESeq2_1.18.1              SummarizedExperiment_1.8.1 DelayedArray_0.4.1         matrixStats_0.54.0        
 [5] Biobase_2.38.0             GenomicRanges_1.30.3       GenomeInfoDb_1.14.0        Biostrings_2.46.0         
 [9] XVector_0.18.0             IRanges_2.12.0             S4Vectors_0.16.0           BiocGenerics_0.24.0       
[13] tximport_1.6.0             BiocInstaller_1.28.0       ggplot2_3.1.0              phyloseq_1.22.3           

loaded via a namespace (and not attached):
 [1] jsonlite_1.6           bit64_0.9-7            splines_3.4.3          foreach_1.4.4          Formula_1.2-3         
 [6] latticeExtra_0.6-28    blob_1.1.0             GenomeInfoDbData_1.0.0 pillar_1.3.0           RSQLite_2.0           
[11] backports_1.1.2        lattice_0.20-35        digest_0.6.18          RColorBrewer_1.1-2     checkmate_1.9.1       
[16] colorspace_1.3-2       htmltools_0.3.6        Matrix_1.2-12          plyr_1.8.4             pkgconfig_2.0.1       
[21] XML_3.98-1.19          genefilter_1.60.0      zlibbioc_1.24.0        xtable_1.8-3           scales_1.0.0          
[26] BiocParallel_1.12.0    htmlTable_1.13.1       tibble_1.4.2           annotate_1.56.2        mgcv_1.8-23           
[31] withr_2.1.2            nnet_7.3-12            lazyeval_0.2.1         survival_2.41-3        magrittr_1.5          
[36] crayon_1.3.4           memoise_1.1.0          nlme_3.1-131           MASS_7.3-48            foreign_0.8-69        
[41] vegan_2.5-3            tools_3.4.3            data.table_1.11.8      stringr_1.3.1          munsell_0.5.0         
[46] locfit_1.5-9.1         cluster_2.0.6          AnnotationDbi_1.40.0   ade4_1.7-13            compiler_3.4.3        
[51] rlang_0.3.0.1          rhdf5_2.22.0           grid_3.4.3             RCurl_1.95-4.11        biomformat_1.6.0      
[56] iterators_1.0.10       rstudioapi_0.8         htmlwidgets_1.3        igraph_1.2.2           bitops_1.0-6          
[61] base64enc_0.1-3        multtest_2.34.0        gtable_0.2.0           codetools_0.2-15       DBI_0.7               
[66] reshape2_1.4.3         gridExtra_2.3          knitr_1.22             bit_1.1-12             Hmisc_4.2-0           
[71] permute_0.9-4          ape_5.2                stringi_1.2.4          Rcpp_1.0.0             geneplotter_1.56.0    
[76] rpart_4.1-12           acepack_1.4.1          xfun_0.6

Thank you in advance! Alice

Can you describe your samples? How many are there? Is the sequencing depth even across samples?