Hallo BioC users,

I am using the siggenes package to analyze miRNA-Microarrays.

It all run perfectly well and I have got a list of 14 miRNA with
significantly differential regulation.

Than I wanted to check the values of these miRNA to see the true values
(after normalization), just to check the relative expression of these miRNA
in comparisons to the rest on the array.

The miRNA affy arrays are built differentially. They contain not only
drosophila miRNA, but also from other sources.
So I did the analysis as always:
1. reading the files:
rawData <- ReadAffy()
sampleNames(rawData) <- sub("^\\d\\_(.*)\\_\\(miRNA-1\\_0\\_2Xgain\\).CEL$",
"\\1", sampleNames(rawData))
sampleNames(rawData)

2.normalizing using RMA:
rma_data <- rma(rawData)

3. filtering the expressionSet only for these probesets from drosophila:
dme <- grep("dme", Probe_names)
dme_rma <- rma_data[dme,]

4. run the sam analysis with subtset:
dme_sam <- sam(dme_rma, cl, var.equal=FALSE, B=100, include.zero=FALSE,
gene.names = featureNames(dme_rma), R.fold = 1.5, na.replace=FALSE,
rand=123)

list.siggenes(dme_sam, 1.7)# gies me back a list of 14 genes.
summary(dme_sam,1.7)

SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances

Number of variables having a fold change >= 1.5 or <= 0.6667 : 26

 s0 = 0.0514  (The 0 % quantile of the s values.)

 Number of permutations: 20 (complete permutation)

 MEAN number of falsely called variables is computed.

Delta: 1.7
cutlow: -6.087
cutup: 2.896
p0: 0
Identified Genes: 14
Falsely Called Genes: 0.9
FDR: 0


Identified Genes (using Delta = 1.7):

   Row d.value  stdev    rawp q.value R.fold            Name
1  132   13.33 0.2369 0.00385       0 14.344  dme-miR-986_st
2    4    7.16 0.0514 0.00769       0  1.666 dme-miR-1001_st
3  126   -6.09 0.0920 0.01154       0  0.546  dme-miR-980_st
4   35    5.19 0.7641 0.01538       0 18.841  dme-miR-193_st
5  133    4.53 0.1673 0.01923       0  1.989  dme-miR-987_st
6    7    4.48 0.0924 0.02308       0  1.563 dme-miR-1004_st
7  100    4.38 0.3418 0.02692       0  3.295  dme-miR-954_st
8   29    4.20 0.2729 0.03077       0  2.571  dme-miR-13a_st
9   37    3.72 0.3097 0.03462       0  2.534  dme-miR-210_st
10  93    3.58 0.2528 0.03846       0  2.126   dme-miR-87_st
11 127    3.57 0.1949 0.04231       0  1.840  dme-miR-981_st
12   3    3.22 0.1529 0.05000       0  1.578 dme-miR-1000_st
13 135    3.00 0.2862 0.05385       0  2.018  dme-miR-989_st
14 118    2.90 0.1537 0.05769       0  1.510  dme-miR-972_st


As you can see I have these miRNA with significant  deregulation with a
delta value of 1.7.
Than I looked at the normalized values of some of these genes and the
results are very strange.
here is a list of three of the genes from the data set:
the first one is a downregulated miRNA and the others are the two strongest
up-regulated miRNAs.

    miRNA_id wt1 wt2 wt3 mut1 mut2 mut3 mean_wt man_mut sam results
dme-miR-980_st 10.72 10.45 10.48 9.71 9.71 9.61 10.55 9.68 0.55
dme-miR-986_st 4.86 5.43 5.24 9.06 8.71 9.28 5.18 9.02 14.34
dme-miR-193_st 1.75
1.75 2.24 4.69 6.62 7.14 1.91 6.15 18.84
For each line I calculated the mean, just to get an Idea of the two
summarized values of each one of them, but this doesn't even get close to
the R.Fold values I can see in the sam result table.

Does anyone have an explanation for such a behavior?

Do I understand it completely wrong?

I would appriciate any help you have.

Thanks

Assa

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