Thanks very much Simon !!!

Indeed, you are right, the experiment depth is very low in all the samples,
they are going for a new sequencing round with higher depth. I hope results
get better then. Anyway, your recommendations will help for future
situations like this.

best regards.
osvaldo


On Fri, Sep 13, 2013 at 11:32 AM, Simon Anders <anders@embl.de> wrote:

> Hi Osvaldo
>
> Looking through the excerpt of your result list, it seems that your read
> counts are very low. Most miRNAs seem to have less than 50 reads, so it
> might well that you simply have not sequenced deeply enough.
>
> Have you enriched your library for short RNA molecules, or is this
> standard RNA-Seq? For how many miRNA genes do you have counts, and how many
> are above 50?
>
> For now, independent filtering might help: Exclude all the genes with less
> than, say, 20 or 40 counts (according to baseMean), and run the
> Benjamini-Hochberg adjustment only on the remaining ones. I think we have a
> chapter on the vignette about this.
>
> Also double-check teh normalization with MA plots. With so small counts,
> the default location measure (the median) might work subobtimal; maybe try
> the shorth. (See ?estimateSizeFactors).
>
> Also consider switching to DESeq2, which has better inferential power due
> to an improved dispersion estimation scheme.
>
>   Simon
>
>
>
> On 13/09/13 10:15, Osvaldo Graña wrote:
>
>> hi there !!
>> I am analyzing miRNA-seq data with DESeq, and I am getting no significant
>> results, and I cannot see why is so.
>>
>> My experiment has two conditions and two replicates per condition:
>>
>> I execute it in R as follows:
>> *countTable = read.table (
>>
>> "/mnt/TB3/ograna/Ozge_Uluckan/**miRNA-seq/Analysis/tables_for_**
>> DESeq/cOB1_vs_tOB100"
>> , header=TRUE, row.names=1)
>> experiment_design = data.frame(
>> row.names = colnames(countTable),
>> condition=c("OB1","OB1","**OB100","OB100"),
>> libType=c("single-end","**single-end","single-end","**single-end")
>> )
>> library("DESeq")
>> condition=factor(c("OB1","OB1"**,"OB100","OB100"))
>> cds = newCountDataSet( countTable, condition )
>> cds = estimateSizeFactors( cds )
>> normalizedReadCounts = counts( cds, normalized=TRUE )
>> write.csv( normalizedReadCounts,
>> file="cOB1_vs_tOB100.DESeq_**normalizedReadCounts.csv" )
>> cds = estimateDispersions( cds )
>> res = nbinomTest( cds, "OB1", "OB100" )
>> write.csv( res, file="cOB1_vs_tOB100.DESeq_**diffExp.csv" )*
>>
>>
>>
>> I am getting the following size factors:
>>
>>> sizeFactors(cds)
>>>
>>     OB.1OU    OB.2OU OB100.1OU OB100.2OU
>>          1         1         1         1
>>
>>
>> the differential expression table sorted by 'padj' is as follows (I am
>> showing just a small set):
>>
>> number    id    baseMean    baseMeanA    baseMeanB    foldChange
>> log2FoldChange    pval    padj
>> *340    mmu-miR-204-5p    31.5    12.5    50.5    4.04    2.014355293
>>
>> 6.466284630369E-005    0.122859408
>> 69    mmu-miR-1247-5p    57.5    32.5    82.5    2.5384615385
>> 1.3439544012    0.0010710548    1
>> 170    mmu-miR-15b-3p    99.25    135.5    63    0.4649446494
>> -1.1048691179    0.0024355731    1
>> 363    mmu-miR-214-3p    591.25    767    415.5    0.5417209909
>> -0.8843781007    0.0040302141    1
>> 1122    mmu-miR-664-3p    55.5    76    35    0.4605263158
>> -1.1186444965    0.0065510324    1
>> 630    mmu-miR-335-3p    9.5    15.5    3.5    0.2258064516
>> -2.1468413883    0.0093262341    1
>> 997    mmu-miR-574-3p    282    364.5    199.5    0.5473251029
>> -0.8695300681    0.0101138488    1
>> 176    mmu-miR-17-3p    29    41    17    0.4146341463    -1.2700891634
>> 0.0109905451    1
>> 1898    mmu-miR-99a-5p    1254.5    1568.5    940.5    0.5996174689
>> -0.7378856803    0.0140500521    1
>> 846    mmu-miR-467a-5p    11    17    5    0.2941176471    -1.7655347464
>> 0.019028208    1
>> 1900    mmu-miR-99b-5p    6246    7702.5    4789.5    0.6218111003
>> -0.6854517236    0.0204136071    1*
>>
>>
>>
>> There is no even one significant miRNA. Is it that I am missing something
>> or doing something wrong? What does it mean that all the 'padj' values are
>> '1' with the exception of the first one '0.122' ?
>> Is there a way to change the method used to correct the p-values?
>>
>> thanks very much in advance !!!
>> regards.
>>
>>
>>
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>>
>>
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>



-- 
Osvaldo Graña
Bionformatics Unit, Structural Biology and Biocomputing Programme
Spanish National Cancer Research Centre  (CNIO)
Melchor Fernández Almagro, 3 - 28029 Madrid
+34 91 732 8000 (ext 3062)
http://bioinfo.cnio.es (www.cnio.es)
<ograna@cnio.es>

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