Possible(validated) methodology implemented in R for integrating and analyzing clinical data with gene expression data in R
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svlachavas ▴ 780
Last seen 1 day ago
Germany/Heidelberg/German Cancer Resear…

Dear Bioconductor community,

i have posted in the past a more general question about data integration. This time, i would like to adress a specific task i would like to adress. I have clinical data accompaning my gene expression microarray data(affymetrix colorectal cancer datasets-60 samples:paired cancer and control samples). In detail, the clinical data represent Positron Emission Tomography(PET) measurements on each sample(of each patient, both cancer and adjucent control), such as SUV(Standardized Uptake Value), Fractal Dimension(FD) and other kinetic parameters[in total 8 "variables"-parameters with measurements(numbers with units). A very small subset of these clinical data just for illustration are presented below(with some of the variables with bolt):

Parameter         Unit                 Sample_1       Sample_2    Sample_3

SUV                                           8.085            10.255           3.2744

VB                                             0.00595          0.063967       0.032291

FD                                              1.3546            1.3923          1.2349

K1            ml/ml Tiss/min            0.6953             0.4653          0.3942........


Thus, my main question is if there is an appropriate methodology implemented in any package in R, in order to perform appropriate integration and subsequent analysis of my gene expression data with the correspoding PET data, in order to search for any interesting correlations or patterns produced  (regarding the specific patients of my data)? And also to be able to perform any necessay transformation to the above data(maybe scaling or normalization of the above continuous variables, and also removal of any samples with a lot of missing values) . The only package i have noticed is the FactoMineR R package, but as i have no experience in any similar kind of analysis i dont know if could be used for my specific purposes.

Any suggestions, comments or help would be beneficial !!


data integration clinicaldata gene expression data PET • 828 views

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