P R O T E X A

Step 1
Upload Your Dataset
Step 2
Differential Expression Analysis
Step 3
Proceed with Filtering Process
Step 4
Enrichment Analysis
Step 5
Create co-expression NETs
Step 6
Preform network Clustering

Step 1: Upload Expression Dataset

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.

Select Experiment Type info

Select Preprocessing Scheme info

Upload your Dataset info

Pick-up an example

Framework Actions

Step 2: Protein Expression Data Analysis

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.

Click this button to run the statistical analysis of your protein expression dataset. A series of charts will follow, that statistically evaluate the outcome of this process.

Step 3: Filtering Process

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.

Select P.Value score to work with info

Set Score Tresshold info

Up-Regulated FC threshold info

Down-Regulated FC threshold info

Sort final table by means of info

Select Sort Order info

Maximum number of rows to keep info

Use the above panel and press this button to filter the outcome derived from statistical analysis. A heat-map, a box plot and the overall expression levels will appear, that depict the filtered outcome of this process.

Step 4: Enrichment Analysis

Select pathway database info

Select score info

Set Score Threshold info

Select Sort Order info

# of pathways to keep info

Press this button to perform enrichment analysis according to your filter.

Step 4: Create Co-Expression Networks

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.

Select Network Methodology info

Select Node Type info

# top-scored proteins-genes to keep info

Select a network type and clustering options from the above panel and then press the green button to view the network.

Step 6: Clustering & filtering of co-expression network

Select Clustering Algorithm info

Select Weight Policy for Clustering

Select Edge Exclusion Method info

Select a network type and clustering options from the above panel and then press the green button to view the network.

A PROTEIN-GENE EXPRESSION DATA ANALYSIS TOOL

Reference 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.