[BioC] HTqPCR : readCtData problem?

[Heidi Dvinge]

> Dear Heidi,
>
> In HTqPCR I am getting a strange toptable where it looks like it is
> treating probes in biological replicates as separate
> probes.
>

Dear Steve,

just to clear my mind here, exactly what type of replicates are we talking
about?

1) The same miRNA is present three times on the qPCR card for each sample?
Per default, this isn't taken into account by limmaCtData. You'll need to
resort your qPCRset by featureNames(), so that all triplicates are next to
each other, and then call limmaCtData(..., ndups=3, spacing=1) to indicate
the number of duplications (3) and the spacing between them (1, since they
are adjacent in the resorted qPCRset). As I recall, there's an example of
this in ?limmaCtData with duplicate spots.

2) The same miRNA is present three times on the qPCR card, but in
different "lanes" belonging to different samples? In that case the qPCRset
will ahve to be reformatted after readCtData, to reflect that there are
multiple samples on each 384 well card. changeCtLayout () should give some
examples on this, although I admit that the vignettes could use some more
examples on this.

I'm asking since in the example you present below, you say you're reading
in a file with 3 samples (which BTW looks fine), but according to you
design matrix you have 8 different samples.

(more below)

> For example:
>
> 68	hsa-miR-629-4395547	N14	-2.462024092	0.018610579	0.707201997	-4.648018479	25.07223112	35.08434984	39.73236831
> Undetermined	Undetermined
> 144	hsa-miR-629-4395547	N14	-2.462024092	0.018610579	0.707201997	-4.648018479	25.07223112	35.08434984	39.73236831
> Undetermined	Undetermined
> 220	hsa-miR-629-4395547	N14	-2.462024092	0.018610579	0.707201997	-4.648018479	25.07223112	35.08434984	39.73236831
> Undetermined	Undetermined
> 5	hsa-miR-107-4373154	C13	-1.917877218	0.062890552	0.794930138	-4.173391821	18.0433063	33.48123219	37.65462402	OK
> Undetermined
> 81	hsa-miR-107-4373154	C13	-1.917877218	0.062890552	0.794930138	-4.173391821	18.0433063	33.48123219	37.65462402	OK
> Undetermined
> 157	hsa-miR-107-4373154	C13	-1.917877218	0.062890552	0.794930138	-4.173391821	18.0433063	33.48123219	37.65462402	OK
> Undetermined
>
>
> I am using:
>
> raw<-readCtData(files = files$File,
> path=".",SDS=TRUE,n.data=3,samples=samples)
>
> There are 3 replicates per file and the header looks like:
>
> SDS 2.4 RQ Results      1.2
> Filename        7.10.10 8A.2.sdm
> Assay Type      RQ Study
> EmbeddedFile    8A 13.9.10 A
> Run DateTime    Mon Sep 13 17:08:13 BST 2010
> Operator
> ThermalCycleParams
> EmbeddedFile    8B 14.9.10 A
> Run DateTime    Tue Sep 14 15:02:13 BST 2010
> Operator
> ThermalCycleParams
> EmbeddedFile    8C 15.9.10 A
> Run DateTime    Wed Sep 15 14:45:18 BST 2010
> Operator
> ThermalCycleParams
>
> #       Plate   Pos     Flag    Sample  Detector        Task    Ct
> Delta Ct        Avg Delta Ct    ?Ct SE  Delta
> Delta Ct SDRQ       RQ Min  RQ Max  Omit    HMD     FOS     LME     NAW
>  EW      BPR     HRN     HNS     EAF     BAF
>       TAF     CAF     HS
> D
>
>
> Would readCtData parse this type of file correctly?
>
This should be okay. Per default, readCtData reads the beginning of the
file and discards everything prior to the line beginning with "#".
Everything following that is considered the actual data, and
n.features*n.data lines are read in, in your case 384*3, and this is split
into 3 different samples.

HTH
\Heidi


>
> Here are my design and contrast matrices, which I think look fine.
>
>
>   > design
>      SAMP1 SAMP2 SAMP3 SAMP4 SAMP5 SAMP6 SAMP7 SAMP8
> 1      1     0     0     0     0     0     0     0
> 2      1     0     0     0     0     0     0     0
> 3      1     0     0     0     0     0     0     0
> 4      0     1     0     0     0     0     0     0
> 5      0     1     0     0     0     0     0     0
> 6      0     1     0     0     0     0     0     0
> 7      0     0     1     0     0     0     0     0
> 8      0     0     1     0     0     0     0     0
> 9      0     0     1     0     0     0     0     0
> 10     0     0     0     1     0     0     0     0
> 11     0     0     0     1     0     0     0     0
> 12     0     0     0     1     0     0     0     0
> 13     0     0     0     0     1     0     0     0
> 14     0     0     0     0     1     0     0     0
> 15     0     0     0     0     1     0     0     0
> 16     0     0     0     0     0     1     0     0
> 17     0     0     0     0     0     1     0     0
> 18     0     0     0     0     0     1     0     0
> 19     0     0     0     0     0     0     1     0
> 20     0     0     0     0     0     0     1     0
> 21     0     0     0     0     0     0     1     0
> 22     0     0     0     0     0     0     0     1
> 23     0     0     0     0     0     0     0     1
> 24     0     0     0     0     0     0     0     1
> attr(,"assign")
> [1] 1 1 1 1 1 1 1 1
> attr(,"contrasts")
> attr(,"contrasts")$SAMP
> [1] "contr.treatment"
>
> cont.matrix
>          Contrasts
> Levels  C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12
>     SAMP1 -1  0  0  0  1  0  0  0  0  -1   0   0
>     SAMP2  1  0  0  0  0  0 -1  0 -1   0   0   0
>     SAMP3  0 -1  0  0 -1  0  0  0  0   0   0  -1
>     SAMP4  0  1  0  0  0  0  1  0  0   0  -1   0
>     SAMP5  0  0 -1  0  0  1  0  0  0   1   0   0
>     SAMP6  0  0  1  0  0  0  0 -1  1   0   0   0
>     SAMP7  0  0  0 -1  0 -1  0  0  0   0   0   1
>     SAMP8  0  0  0  1  0  0  0  1  0   0   1   0
>   >
>
> I am finally running the command:
>
> qDE.limma<-limmaCtData(sr.norm,design=design,contrasts=cont.matrix)
>
> Let me know if you need more information.
>
> Kind regards and thanks,
>
> Steve
> ============================================
> Head of Computational Biology Research Group
> Weatherall Institute of Molecular Medicine
> University of Oxford
> John Radcliffe Hospital
> Headington
> Oxford OX3 9DS
> +44 1865 222640
>