Dear all,
I did a RNA-seq analysis two years ago (2011), using edgeR package (I do
not remember the version). I was asked to analyze the data again, and now I
am using edgeR_3.2.4.

The comparison is between two groups. The "backbone" of the code was very
similar (it is pasted bellow), and I just changed it to adapt the package
modifications. It turned out that the number of differentially expressed
genes (FDR<0.05) in the second analysis (78) was a smaller than the number
of DEG obtained in the first analysis (119).

I understand that two years is a lot of time in such fast evolving field,
and I suppose that the newer package version has more reliable analysis
than older version. Nonetheless, has anyone experienced something similar?
Is there a clear reason for this (other than I am making a mistake)?

I also would appreciate if someone told me the mistakes in the code, if
there are mistakes.

Best regards,
Fernando
_____________________________________________
Genomics Research Associate
Dept Bioengineering, University of California San Diego
_____________________________________________



the design is the following:

             (Intercept) groupAB
7552_34_C           1       0
7828_34_C           1       0
8153_34_C           1       0
8140_34_C           1       0
8035_34_C           1       0
E002_34_C           1       1
E003_34_C           1       1
B967_34_C           1       1
B987_34_C           1       1
O621_34_C           1       1
O604_34_C           1       1
O470_34_C           1       1
O143_34_C           1       1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"


*from 2011*

read_count_data_b_TMM <- calcNormFactors(read_count_data_b, method="TMM")
design <- model.matrix(~ group , data=targets)
read_count_data_c_TMM <-estimateGLMCommonDisp(read_count_data_b_TMM,
design, method="CoxReid")
read_count_data_c_TMM <-estimateGLMTrendedDisp(read_count_data_c_TMM,
design, method="bin.spline")
read_count_data_c_TMM <- estimateGLMTagwiseDisp(read_count_data_c_TMM,
design, method="trend")
glmfit_read_count_data_c_TMM <- glmFit(read_count_data_c_TMM, design,
dispersion=read_count_data_c_TMM$tagwise.dispersion)
lrt_glmfit_read_count_data_c_TMM.group <- glmLRT(read_count_data_c_TMM
,glmfit_read_count_data_c_TMM, coef=2)
decideTests_read_count_data_c_TMM.group  <-
decideTestsDGE(lrt_glmfit_read_count_data_c_TMM.group)
summary(decideTests_read_count_data_c_TMM.group)
     [,1]
-1      11
0  13022
1      108


*From 2013*

read_count_data_b_TMM <- calcNormFactors(read_count_data_b, method="TMM")
design <- model.matrix(~ group , data=targets )
read_count_data_c_TMM <-estimateGLMCommonDisp(read_count_data_b_TMM,
design, method="CoxReid")
read_count_data_c_TMM <-estimateGLMTrendedDisp(read_count_data_c_TMM,
design, method="bin.spline")
read_count_data_c_TMM <- estimateGLMTagwiseDisp(read_count_data_c_TMM,
design, trend=TRUE)
glmfit_read_count_data_c_TMM <- glmFit(read_count_data_c_TMM, design,
dispersion=read_count_data_c_TMM$tagwise.dispersion)
lrt_glmfit_read_count_data_c_TMM.group <-
glmLRT(glmfit_read_count_data_c_TMM , coef=2)
decideTests_read_count_data_c_TMM.group  <-
decideTestsDGE(lrt_glmfit_read_count_data_c_TMM.group)
summary(decideTests_read_count_data_c_TMM.group)
      [,1]
-1      11
0  13063
1       67



the design is the following:

             (Intercept) groupAB
7552_34_C           1       0
7828_34_C           1       0
8153_34_C           1       0
8140_34_C           1       0
8035_34_C           1       0
E002_34_C           1       1
E003_34_C           1       1
B967_34_C           1       1
B987_34_C           1       1
O621_34_C           1       1
O604_34_C           1       1
O470_34_C           1       1
O143_34_C           1       1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"




The current sessionInfo() is the following:
> sessionInfo()
R version 3.0.1 (2013-05-16)
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] splines   grid      parallel  stats     graphics  grDevices utils
datasets  methods   base

other attached packages:
[1] baySeq_1.14.1      VennDiagram_1.6.4  DESeq_1.12.0
lattice_0.20-15    locfit_1.5-9.1     Biobase_2.20.1     BiocGenerics_0.6.0
edgeR_3.2.4
[9] limma_3.16.6

loaded via a namespace (and not attached):
 [1] annotate_1.38.0      AnnotationDbi_1.22.6 DBI_0.2-7
genefilter_1.42.0    geneplotter_1.38.0   IRanges_1.18.2
RColorBrewer_1.0-5
 [8] RSQLite_0.11.4       stats4_3.0.1         survival_2.37-4
tools_3.0.1          XML_3.95-0.2         xtable_1.7-1

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