Hi all,
I'm fairly new to the HTqPCR package and to R.
I wanted to analyze my Ct values that I get from the BioMark output in
form
of a .cvs file. I managed to read my file in with:
> raw<-readCtData(files="test2.csv",format="BioMark",n.features=47,
+ n.data=3,samples=samples)
Warning message:
In readCtData(files = "test2.csv", format = "BioMark", n.features =
47, :
Not enough sample names provided; using Sample1, Sample2, Instead
> show(raw)
An object of class "qPCRset"
Size: 47 features, 3 samples
Feature types:
Feature names: HSP90AB1 CD13 COL1A1 ...
Feature classes:
Feature categories: OK
Sample names: Sample1 Sample2 Sample3 ...
I ran 3 samples (three different cell types), each in 6 biological and
2
technical replicates (12 total) but I don't know how to modify or edit
my
qPCRset object in order to visualize them or perform downstream
analysis
(PCA, clustering etc.). I read through the PDF "HTqPCR -
highthroughput
qPCR analysis in R and Bioconductor" by Heidi Dvinge but couldn't find
an
answer to my specific problem (mainly because I'm fairly new to the
language
R I think).
If anyone could help out I would greatly appreciate it. Let me know if
I
need to provide additional information about the input .cvs file)
Thanks!
Jens
:: Jens Durruthy-Durruthy ::
:: Research Scholar ::
:: Reijo Pera Lab ::
:: Stanford University School of Medicine ::
:: Institute for Stem Cell Biology & Regenerative Medicine ::
:: Lorry Lokey Stem Cell Research Building ::
:: 265 Campus Drive, Rm 3015 ::
:: Stanford, CA 94305 United States ::
:: Mail: durruthy@stanford.edu ::
:: Phone: +1-650-498-7303 ::
:: Fax: 650-725-6910 ::
This e-mail may contain confidential and/or privileged
i...{{dropped:12}}
Jens,
If your qPCRset recognizes that there are 47 features and 3 samples,
then
you should be able to perform downstream analyses. If you are having
trouble modifying sample names, I have been storing sample names in a
file, ordered as the actual samples are in the Fluidigm output file,
then
assigning sample names. That is, something like:
Temp <- read.csv("sample.names.csv")
sampleNames(raw) <- Temp
Best,
Kipper
On 11/11/12 3:27 AM, "Jens Durruthy-Durruthy" <jensdd at="" stanford.edu="">
wrote:
>Hi all,
>
>I'm fairly new to the HTqPCR package and to R.
>I wanted to analyze my Ct values that I get from the BioMark output
in
>form
>of a .cvs file. I managed to read my file in with:
>
>> raw<-readCtData(files="test2.csv",format="BioMark",n.features=47,
>+ n.data=3,samples=samples)
>Warning message:
>In readCtData(files = "test2.csv", format = "BioMark", n.features =
47, :
> Not enough sample names provided; using Sample1, Sample2, ? Instead
>
>> show(raw)
>An object of class "qPCRset"
>Size: 47 features, 3 samples
>Feature types:
>Feature names: HSP90AB1 CD13 COL1A1 ...
>Feature classes:
>Feature categories: OK
>Sample names: Sample1 Sample2 Sample3 ...
>
>I ran 3 samples (three different cell types), each in 6 biological
and 2
>technical replicates (12 total) but I don't know how to modify or
edit my
>qPCRset object in order to visualize them or perform downstream
analysis
>(PCA, clustering etc.). I read through the PDF "HTqPCR -
high?throughput
>qPCR analysis in R and Bioconductor" by Heidi Dvinge but couldn't
find an
>answer to my specific problem (mainly because I'm fairly new to the
>language
>R I think).
>
>If anyone could help out I would greatly appreciate it. Let me know
if I
>need to provide additional information about the input .cvs file)
>
>Thanks!
>Jens
>
>:: Jens Durruthy-Durruthy ::
>:: Research Scholar ::
>:: Reijo Pera Lab ::
>
>:: Stanford University School of Medicine ::
>:: Institute for Stem Cell Biology & Regenerative Medicine ::
>:: Lorry Lokey Stem Cell Research Building ::
>:: 265 Campus Drive, Rm 3015 ::
>:: Stanford, CA 94305 ? United States ::
>:: Mail: durruthy at stanford.edu ::
>:: Phone: +1-650-498-7303 ::
>:: Fax: 650-725-6910 ::
>
>
>This e-mail may contain confidential and/or privileged
i...{{dropped:12}}
>
