Hi
Thanks for your input. I checked manually the counts of the lowest BCV
values (see below) And I see nothing strange. Except the fact that the
counts are all at the low side. So I think I will keep them in.
Is it correct to think that the reason they appear on 1 horizontal line is
because of the discreteness of the counts?

Greetings
Adriaan


> ids = (y.tagwise$tagwise.dispersion %in%
sort(y.tagwise$tagwise.dispersion)[1:10])
> counts.filtered[ids,]
                X1 X2 X8 X7 X14 X13 X20 X3 X4 X9 X10 X15 X16 X21 X22 X5 X6
X11 X12 X17 X18 X23 X24
ENSG00000111215 18 13 16 15  20  14  13 11 17 42  13  30  35  17  26 12 14
35  15  50  20  13  27
ENSG00000134061  7 11 11  9   8   4  14  0 11 24  21  10  23   6  31  4 11
27  17  15   9   8  30
ENSG00000162927 26 28 15 25  19  18  19 23 24 77  18  44  40  19  27 21 17
75  22  67  18  21  37
ENSG00000197748 19 14 15 28  23  25  18 12 17 75  29  46  53  21  38 10 14
75  22  86  18  18  30
ENSG00000204822  8  8  9 12  12  12  12  4  7 32   7  18  16  14  17  3  8
30  13  27   9   7  32
ENSG00000213070 16 13  6  8  16  12  14  8 12 27   6  24  22  17  24  7 12
22  11  39  10  17  33
ENSG00000228343 13 14  7  9  17  15  20  5 11 22   6  30  39  19  33  4 12
26   8  63  22  16  45
ENSG00000229359 25 20 12 14  14  19  20  9 15 32   9  19  20  10  22  8 16
28  15  33  13   9  27
ENSG00000233597 21 18 14 14  18  16  14 10 18 41  13  23  25  18  21 12 13
35  15  42  12  15  31
ENSG00000248932 27 25 19 27  15  21  19 18 18 71  19  28  29  17  31 19 13
68  24  49  12  15  30


2014-01-30 Ryan <rct@thompsonclan.org>:

>  Hi Adriaan,
>
> Perhaps the best plan is to inspect these genes and their counts directly
> to see if there are any obvious commonalities about them. You can access
> the tagwise dispersions in the DGEList object using
> dge$tagwise.dispersions. It's not clear exactly how many of the genes show
> this artifact, but try finding the 10 or so genes with the lowest
> dispersions and look at their counts to see if there is anything amiss.
>
> For what it's worth though, it doesn't look like these genes are having a
> noticeable effect on your dispersion trend, and I don't think you have much
> to worry about.
>
> -Ryan
>
>
> On 1/30/14, 10:23 AM, Adriaan Sticker wrote:
>
> Hi all,
>
> I made some BCV plots of my data after the tagewise estimation step. I
> notice sometimes that I gave genes with identical very low BCV values .It
> appears as a horizontal line below the rest of my data but it is always
> above zero. I put an example in attachement. They disapear when I higher
> the cutoff of my filter (cpm(counts)>1 to  cpm(counts)>2) but then I also
> lose a fraction of my genes.
>
> I wonder how I should interpret these values? What are they exactly. My
> guess would be that they are very low counts and due the discretness of
> count data, their bcv is zero?
> If I dont up my filter cutoff and thus leave them in the data, how harmfull
> are they? Can they influence much the estimation of BCV of the other data?
> (I use prior.df = 20) I can see the trended dispersion line moving a bit
> when I up my filter for the lower counts.
>
> In attachement the BCV plot with the artifacts (cpm(counts)>1) and a BCV
> plot without them (cpm(counts)>2)
>
> Best regards
> Adriaan Sticker
>
>
>
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