[BioC] HTqPCR problems
Heidi Dvinge
heidi at ebi.ac.uk
Wed Jun 27 23:26:52 CEST 2012
Hi Simon,
> Thanks for the help Heidi,
> but I'm still having troubles, your comments on the plotting helped me
> solve the outputs. But if I want to just display some groups (for example
> the LO group in the example below), how do I associate a group with
> multiple samples (ie biological reps)?
>
> Currently I'm associating genes with samples by reading in the file as
> below
> plate6=read.delim("plate6Sample.txt", header=FALSE)
> #this is a file to associate sample ID with the genes in the biomark data,
> as currently HTqPCR does not seem to associate the sample info in the
> Biomark output to the gene IDs
>
Erm, no, it doesn't :-/
> samples=as.vector(t(plate6))
> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48,
> n.data=48, samples=samples)
> #now I have samples and genes similar to your example in the guide, but I
> want to associate samples to groups now. In the guide, you have an example
> where you have entire files as distinct samples, but in our runs, we never
> have that situation. I have a file which associates samples to groups,
> which I read in...
>
> groupID=read.csv("plate6key.csv")
>
> but how do I associate the samples with their appropriate groups for
> biological replicates with any of the functions in HtQPCR?
I'm afraid I'm slightly confused here (sorry, long day). Exactly how is
your data formatted? I.e. are the columns either individual samples, or
from files containing multiple samples? So for example for a single 48.48
arrays, is your qPCRset object 2304 x 1 or 48 x 48?
>From your readCtData command I'm guessing you have 48 x 48, i.e. all 48
samples from your 1 array are in columns. In that case the 'groups'
parameter in plotCtOverview will need to be a vector of length 48,
indicating how you want the 48 columns in your qPCRset object to be
grouped together.
Below is an example of (admittedly ugly) plots. I don't know if that's
similar to your situation at all.
\Heidi
> # Reading in data
> exPath <- system.file("exData", package = "HTqPCR")
> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, format =
"BioMark", n.features = 48, n.data = 48)
> # Check sample names
> head(sampleNames(raw1))
[1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6"
> # Associate samples with (randomly chosen) groups
> anno <- data.frame(sampleID=sampleNames(raw1), Group=rep(c("A", "B",
"C", "D"), times=c(4,24,5,15)))
> head(anno)
sampleID Group
1 Sample1 A
2 Sample2 A
3 Sample3 A
4 Sample4 A
5 Sample5 B
6 Sample6 B
> # Plot the first gene - for each sample individually
> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE,
col=1:nrow(anno))
> # Plot the first gene - for each group
> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group,
legend=FALSE, col=1:length(unique(anno$Group)))
> # Plot the first gene, using group "A" as a control
> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group,
legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A")
> You recommend below using a vector, but I dont see how that helps me
> associate the samples in the Expression set.
>
> thanks again!
>
> s
>
> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote:
>
>>> Hi,
>>> I'm having some troubles selectively sub-setting, and graphing up QPCR
>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 plates).
>>> I'd
>>> like to be able to visualize specific genes, with specific groups we
>>> run
>>> routinely on our Biomark system. Typical runs are across multiple
>>> plates,
>>> and have multiple biological replicates, and usually 2 or more
>>> technical
>>> replicates (although we are moving away from technical reps, as the CVs
>>> are so tight).
>>>
>>> Can anyone help with this? Heidi?
>>>
>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48,
>>> n.data=48, samples=samples)
>>> #Ive read the samples in from a separate file, as when you read it in,
>>> it
>>> doesnt take the sample names supplied in the biomark output#
>>> #Next, I want to plot genes of interest, with samples of interest, and
>>> I'm
>>> having trouble getting an appropriate output#
>>>
>>> g=featureNames(raw6)[1:2]
>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, col=rainbow(5))
>>>
>>> #This plots 1 gene across all 48 samples#
>>> #but the legend doesnt behave, its placed on top of the plot, and I
>>> cant
>>> get it to display in a non-overlapping fashion#
>>> #I've tried all sorts of things in par, but nothing seems to shift the
>>> legend's position#
>>>
>> As the old saying goes, whenever you want a job done well, you'll have
>> to
>> do it yourself ;). In this case, the easiest thing is probably to use
>> legend=FALSE in plotCtOverview, and then afterwards add it yourself in
>> the
>> desired location using legend(). That way, if you have a lot of
>> different
>> features or groups to display, you can also use the ncol parameter in
>> legend to make several columns within the legend, such as 3x4 instead of
>> the default 12x1.
>>
>> Alternatively, you can use either xlim or ylim in plotCtOverview to add
>> some empty space on the side where there's then room for the legend.
>>
>>> #I now want to plot a subset of the samples for specific genes#
>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment==
>>>> "LFY")
>>>> LOY
>>> Sample Treatment
>>> 2 L20 LFY
>>> 5 L30 LFY
>>> 7 L45 LO
>>> 20 L40 LO
>>> 27 L43 LO
>>> 33 L29 LFY
>>> 36 L38 LO
>>> 40 L39 LO
>>> 43 L23 LFY
>>>
>>>
>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5))
>>> Warning messages:
>>> 1: In split.default(t(x), sample.split) :
>>> data length is not a multiple of split variable
>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced
>>>>
>>
>> Does it make sense if you split by groups=LOY$Treatment? It looks like
>> the
>> object LOY itself is a data frame, rather than the expected vector.
>>
>> Also, you may have to 'repeat' the col=rainbow() argument to fit your
>> number of features.
>>
>>>
>>> #it displays the two groups defined by treatment, but doesnt do so
>>> nicely,
>>> very skinny bars, and the legend doesnt reflect what its displaying#
>>> #again, I've tried monkeying around with par, but not sure what HTqPCR
>>> is
>>> calling to make the plots#
>>>
>> If the bars are very skinny, it's probably because you're displaying a
>> lot
>> of features. Nothing much to do about that, except increasing the width
>> or
>> your plot :(.
>>
>> \Heidi
>>
>>> please help!
>>>
>>> thanks
>>>
>>> Simon.
>>>
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>>
>>
>
>
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