[BioC] understanding siggenes results

Thu May 19 15:47:13 CEST 2011

Hi Assa,

On 5/19/2011 7:18 AM, Assa Yeroslaviz wrote:
> 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?

You are missing the fact that the fold changes that siggenes reports are 
no longer on the log scale.

 > 2^(9.68-10.55)
[1] 0.5471469
 > 2^(9.02-5.18)
[1] 14.3204
 > 2^(6.15-1.91)
[1] 18.89588

My values are slightly off because IIRC, what really happens in siggenes 
(and for sure happens in SAM) is that the data are 'unlogged' first, 
then the mean is computed, then the ratio.

Best,

Jim

>
> I would appriciate any help you have.
>
> Thanks
>
> Assa
>
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>
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-- 
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
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