Dear All,

I am having some strange experience with the function decideTests. When I compare between the total number of genes between my three groups and the the sum of the corresponding up and down genes the results are different.

The sum is either larger than or smaller than the total.

Am I doing something wrong, or is there an explanation for this discrepancy?

Below is code and output for two different comparisons using the same fit (two channel array with common reference) and examples highlighted in yellow:

d.lm <- modelMatrix(targets, ref = "reference")
fit.94.g <- lmFit(data94.g, design = d.lm, weights = arrayWeights(data94.g, design = d.lm))
fit.94.g_e <- eBayes(fit.94.g)

data94_results <- decideTests(fit.94.g_e, method="separate", adjust.method="fdr", p.value=0.05)

> vennCounts(data94_results)
  MES RTK1 RTK2 Counts
1   0    0    0   4107
2   0    0    1   2626
3   0    1    0    158
4   0    1    1    176
5   1    0    0    506
6   1    0    1   4520
7   1    1    0     79
8   1    1    1   8180
attr(,"class")
[1] "VennCounts"

> vennCounts(data94_results, "up")
  MES RTK1 RTK2 Counts
1   0    0    0  11428
2   0    0    1   1585
3   0    1    0     48
4   0    1    1     88
5   1    0    0    334
6   1    0    1   2871
7   1    1    0     23
8   1    1    1   3975
attr(,"class")
[1] "VennCounts"

> vennCounts(data94_results, "down")
  MES RTK1 RTK2 Counts
1   0    0    0  13024
2   0    0    1   1044
3   0    1    0    117
4   0    1    1     85
5   1    0    0    176
6   1    0    1   1649
7   1    1    0     52
8   1    1    1   4205
attr(,"class")
[1] "VennCounts"


> data94_results1 <- decideTests(fit.94.g_e, method="global", adjust.method="fdr", p.value=0.05)

> vennCounts(data94_results1)
  MES RTK1 RTK2 Counts
1   0    0    0   4247
2   0    0    1   2504
3   0    1    0    191
4   0    1    1    209
5   1    0    0    510
6   1    0    1   3975
7   1    1    0     92
8   1    1    1   8624
attr(,"class")
[1] "VennCounts"

> vennCounts(data94_results1, "up")
  MES RTK1 RTK2 Counts
1   0    0    0  11529
2   0    0    1   1514
3   0    1    0     58
4   0    1    1    108
5   1    0    0    347
6   1    0    1   2564
7   1    1    0     28
8   1    1    1   4204
attr(,"class")
[1] "VennCounts"

> vennCounts(data94_results1, "down")
  MES RTK1 RTK2 Counts
1   0    0    0  13062
2   0    0    1    993
3   0    1    0    141
4   0    1    1     98
5   1    0    0    168
6   1    0    1   1411
7   1    1    0     59
8   1    1    1   4420
attr(,"class")
[1] "VennCounts"

I would appreciate your help on this, because I am stuck and have been scratching my head for hours now.

Thanks,

Ahmed

With include="up", downregulated genes are ignored, i.e., they might as well be non-DE genes. So, if you have a gene that is upregulated in one contrast but downregulated in the two other contrasts, it would be:

> vennCounts(cbind(1,-1,-1), include="both")
  Group 1 Group 2 Group 3 Counts
1       0       0       0      0
2       0       0       1      0
3       0       1       0      0
4       0       1       1      0
5       1       0       0      0
6       1       0       1      0
7       1       1       0      0
8       1       1       1      1
attr(,"class")
[1] "VennCounts"

... without considering the direction of the change, but this would switch to:

> vennCounts(cbind(1,-1,-1), include="up")
  Group 1 Group 2 Group 3 Counts
1       0       0       0      0
2       0       0       1      0
3       0       1       0      0
4       0       1       1      0
5       1       0       0      1
6       1       0       1      0
7       1       1       0      0
8       1       1       1      0
attr(,"class")
[1] "VennCounts"

... when you only treat upregulated genes as being of interest. In short, the genes will move around the different categories depending on whether you consider genes changing in one direction or the other, or both. Thus, the numbers needn't add up per category.

If you want to examine the number of DE genes from decideTests(), then use

summary(data94_results)
summary(data94_results1)