• If the file contains three biological replicates, all the first, second, and third labelled biological replicates of the samples should drag and drop into the corresponding containers in the test sample section. Similarly, drag and drop the control samples also.

• Click on the next button after grouping the samples in batches.

• Users can delete the contaminants by selecting ‘remove contaminants’ and can see the list of contaminants by downloading it.

• The user can replace the missing values present in the input file with any value, and the default is zero If the file does not contain a Gene symbol, ProteoArk provides an option for conversion of protein accession to gene symbol for further analysis (Those accessions which cannot convert into Gene symbol will remain with protein accession only)

• Click next.

Step 7: Based on the user’s choice of normalization method, data will be normalized and represented using PCA analysis and Box plot. If the experiment is a biological replicate user has the option to perform the batch correction. ProteoArk provides two methods for batch correction (Combat and Limma). Batch-corrected data will be represented using a box plot.

• Due to some errors in the mass-spec, some proteins may show the abundance value only in one replicate (not in the other 2 replicates in the case of triplicates). ProteoArk will consider this an error and remove these proteins from the analysis. Users can download the deleted proteins from the result page.

• LIMMA (R package) implemented by DOCKER for differential expression analysis.

Or

• By choosing different methods for p-value (Welch’s t-test, Two-Sample t-Test, One-way ANOVA, and Two-way ANOVA) and fold-change calculation.

• The significant cut-off value for fold change and p-value can be given by the user, and it is used to identify the differentially expressed (DE) proteins in the analysis.

• The result page provides a bar chart to represent the proteins that are upregulated and downregulated in every sample.

• The users can download the full result that includes normalization and differential expression calculation and the file which contains only the differentially expressed proteins separately (in .csv format)

• Based on the normalization methods, Volcano plots will be generated for each test-control comparison in the analysis. The significant cut-off value used to plot the volcano is considered based on the value given by the user on the previous page. But the user can reset the significant cut-off value on the result page also. The upregulated proteins and downregulated proteins are represented in red and green in color respectively. The insignificant proteins are in black color

• Heatmap represents the fold-change of statistically significant proteins that are differentially expressed in one or more samples

• ProteoArk provides two methods for clustering, ‘Hierarchical clustering’ or ‘k-means’. For k-means, the number of clusters in the dataset (i.e., k) needs to be pre-determined, which can be done using the elbow method provided on the same page. It works by finding WCSS (Within-Cluster Sum of Square) i.e., the sum of the square distance between points in a cluster and the cluster centroid

• The K- value will be the point where the elbow gets created (Elbow point). Users can not go for K-means if the number of differentially expressed proteins is less than 8 (Note: 8 is the total number of DE proteins in all datasets)

• For hierarchical clustering, the users need not specify the number of clusters

• The color bar of the heatmap is customized in such a way that the upregulated proteins will be in red and downregulated proteins in green. If the protein does not have differential expression in a sample, it will be in black

Step 9: User can perform functional enrichment analysis on the differentially expressed proteins in ProteoArk. Users can select the p-value threshold and species name for the same. If the user needs results without any filter, they can provide ‘1’ as a significant p-value cut-off. Gene ontology results will be represented using a circular bar plot and can be downloaded as SVG format. Each bar in the plot is labelled by the GO number. Users can also download the entire result for the same in .csv format

•User can view all the KEGG pathways associated with the differentially expressed genes from the provided list. The genes will be highlighted in red (upregulated) or green (downregulated) color in the corresponding pathway mapUser can view all the KEGG pathways associated with the differentially expressed genes from the provided list. The genes will be highlighted in red (upregulated) or green (downregulated) color in the corresponding pathway map

• Users can download pathway images in PNG format

• Using Stringdb API ProteoArk will display the protein-protein interaction for differentially expressed genes. The STRING interactions can be downladed as an image and the resulting data in CSV format.