[BioC] HTqPCR

[Heidi Dvinge]

Hi Simon,

thanks for your email, and sorry if the example files are shoddy. I'm
travelling the next couple of days, but will have a look as soon as I'm
back.

Off the top of my head, I seem to remember that for some of the BioMark
files I originally had access to, the sample names were in slightly
different formats. Therefore, as a temporary measure I just ignored sample
names in the file, in which case the sample names can be added with the
'samples' parameter to readCtData(). Or alternative, added with
sampleNames(object) <- c("",...) later. Obviously, that's not optimal
though, and I should fix that for future versions.

In the case of the example file, a (somewhat messy) workaround may be to say:

> exPath <- system.file("exData", package = "HTqPCR")
> temp	<- read.csv(file.path(exPath, "BioMark_sample.csv"), as.is=TRUE,
skip=11)
> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, format =
"BioMark", n.features = 48, n.data = 48,
samples=temp$Name[seq(1,nrow(temp), 48)])
> head(sampleNames(raw1))
[1] "no preamp"     "preamp neat"   "no preamp.1"   "preamp neat.1" "no
preamp.2"
[6] "preamp neat.2"

Apart from the missing sample names, that what exactly are the problems
you're seeing in importing your own data into a qPCRset object?

HTH
\Heidi

> Hi Heidi,
> I'm working on getting your package routinely going for for our biomark
> data, and it looks really good. However, I've been trying to use your
> example Biomark file in the package as an example, and I'm running into a
> few problems. Firstly, its pretty normal to have uneven numbers of samples
> per plate. We never do a single sample per plate! Remember, on the small
> plates, there are 2304 PCR reactions, and on the large plates, 9216. Due
> to the large volume of samples which can be run, we typically do multiple
> different samples with the same suite of genes per plate, with varying
> replicates. However, it can be tough to make things symetrical due to
> running of controls (no template controls, etc). We also strive to have
> the samples fields present in the CSV files as it makes things easier for
> our existing analysis.
>
> In the file you supply, the samples do not appear to be recognized by your
> scripts. If you do
> pData(raw1)
>
> on the newly imported Biomark file you supply, you just get a generic list
> of sample ID's for each of the 48 chambers, and the
>
> raw1 <- readCtData(files = "BioMark_sample2.csv",path = exPath, format =
> "BioMark", n.features = 48,n.data = 48)
>
> command does not appear to import the sample names associated with each of
> the genes which are as follows:
>
>
> no preamp
> preamp neat
> preamp 1:10
> preamp 1:100
> preamp 1:1000
>
> In the biomark file, there are also a number of samples which do not have
> any identifiers (blank), so I filled these in as "Blank". Further, the
> number of assays being run for each of these samples is quite variable,
> ranging form a low of 48 assays, up to 1152 assays.
>
> I'm having trouble using the examples you provided to reformat the data so
> that I can use your excellent tools. I'm also puzzled why the sample names
> within the file are not being imported along with the Ct and gene data.
>
> I'd really like to be able to try various normalizing methods, but I cant
> get there till we can import the data so it makes sense.
>
> best
>
> Simon.
>
> Simon Melov Ph.D.
> Associate Professor &
> Director of Genomics
> Buck Institute for Research on Aging
> 8001 Redwood Blvd
> Novato, CA 94945
>
> Office: 415 209 2068
> Cell: 415 827 4979
> Fax: 415 209 9920
>
>
>
>
>
>
>