Transform your your protein expression file to the simplified supported file format and upload it here. Then press the Next button at the end of this page to move to the next step. Click the Load Example button bellow to see how this tool works, or refer to the Help Page.
PROTEXA uses the LIMMA statistical analysis package that requires the dataset to be normalised and transformed to log2 scale. Please check carefully your data and the pre-processing recommendations before continuing. Please note that if your dataset includes control vs disease-state samples, then the reference class refers usualy to the control-state samples.
Use the parameters below to perform filtering on the outcome of the statistical analysis performed in previous step. The tool has been setup with the parameteres commonly used in such analyses. If you want to keep these parameters just run the filter as it is by pressing the green button. Please note that the obtained proteins-genes through this process will be used as input for either the enrichment analysis on Step4, or the creation of co-expression networks on Step5.
Press this button to perform enrichment analysis according to your filter.
The creation of co-expression networks in this tab, draws from the final number of proteins-genes obtained from the filtering process performed in Step3. If you want to change this number you must to go back to Step 3 and refine your filter. Please note that for large number of proteins-genes, the creation of co-expression network requires more time. Large networks that exceed the 1000 edges are not visualised but can be further filterd and clustered in step6 in order to explore smaller sub-clusters.
A PROTEIN-GENE EXPRESSION DATA ANALYSIS TOOL
Minadakis, George, Kleitos Sokratous, and George M. Spyrou. "ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics." Computational and Structural Biotechnology Journal 18 (2020): 1695-1703.Department of Bioinformatics, The Cyprus Institute of Neurology & Genetics. © 2021