[BioC] HTqPCR problems

Simon Melov smelov at buckinstitute.org
Wed Jun 27 23:59:24 CEST 2012 

Hi Heidi,
you are correct, yes 48.48. 
The example you provide below is exactly what I needed for clarification for groups. I was trying to reverse engineer what you had done with the original expression set package for microarrays, but from below, I can get this to work now.

Just to be clear, I have 5 48.48 plates. Should I normalize each individually prior to combining, or should I reformat to a 2304x1 each, combine, then normalize (not sure if you can do that or not)

thanks again for your prompt responses!

best

s

On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote:

> 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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