OmicsData

Matlab library of methods for analyzing high-throughput data. See also the OmicsData Wiki.

Installation

• Clone or download repository
  • If you want to include the submodule 'Rcall': "git clone --recurse-submodules https://github.com/kreutz-lab/OmicsData"
• Add the repository file path to your Matlab search path (e.g. by addpath.m)

Examples

An OmicsData object is created by O = OmicsData(file); where .txt, .xls, .xlsx, .csv and .mat files as well as a numeric input are accepted, e.g. the MaxQuant output tables can serve as file inputs here. Example data can be found in the folder TestData.

OmicsInit
O = OmicsData('proteinGroups.txt');
O = log2(O);
image(O)

OmicsInit
O = OmicsData('proteinGroups.txt');
O = OmicsFilter(O,0.8,'log2');
O = DIMA(O);

OmicsInit
O = OmicsData('YEAST-Data-NonNormalized.csv',[],[],'yeast');     % PXD002099
O = DIMA(O,'fast');

Methods

Some methods published from our group can be applied via:

• O = DIMA(O); Egert J, Brombacher E, Warscheid B, and Kreutz C. DIMA: Data-driven Selection of an Imputation Algorithm. J Proteome Res, 2021. doi: 10.1021/acs.jproteome.1c00119. DIMA Wiki
• O = OmicsMbqnMatlab(O); Brombacher E, Schad A, Kreutz C. Tail-Robust Quantile Normalization. Proteomics. 2020;20(24). doi: 10.1002/pmic.202000068.
• O = gsri(isnan(O)); Gehring J, Kreutz C, Bartholomé K and Timmer J. Introduction to the GSRI package : Estimating Regulatory Effects utilizing the Gene Set Regulation Index. 2013.

Rcall

The OmicsData library includes an R interface 'Rcall' which executes R commands via command line. If R functunationalities like O = limma(O); are used in the OmicsData tools, include the submodule:

git submodule init
git submodule update

For further information see the Rcall Wiki.

Author

Clemens Kreutz and Janine Egert

Institute of Medical Biometry and Statistics,

Faculty of Medicine and Medical Center – University of Freiburg, Germany

https://www.uniklinik-freiburg.de/imbi-en/msb.html

ckreutz at imbi.uni-freiburg.de

egert at imbi.uni-freiburg.de