Step 1. Upload Data Matrix

1) Upload the data matrix file:


2) Upload the meta file:


Try our sample data:

The sample data set contains a data matrix, and a meta file.
For data matrix, columns are samples and rows are features.
For meta file, first column is samples and second column is the group information.
(click 'Load sample data' above to load, you can try pre-process sample by clicking 'Process data before analysis' switch, or directly click 'Go to differential expression analysis' to continue)

Step 3. Process data

If your data needs processing before analysis:
Tips for pre-processing: please be sure that you aware of what you are doing in order to obtain correct statistical results. Negative and NA values can lead to a failure of the analysis.

Skip to Step 4. Go to analysis

If your data is ready to go:

Step 2. Double-check Data Matrix

Step 3. Process data matrix before analysis


Note that this pre-process is optional, and please do it before downstream analysis if necessary, instead of after
Q vaule is the presence percentage threshold. For example, 0,75 means 75% of presence across all samples
This function centers and scales numeric matrix. Center means the data(row or column)'s mean is going to be 0. Scale is done after centering. Scale is done by divding each value by their standard deviation if center is TRUE, and the root mean square otherwise. This function is called z-score some elsewhere.

Processing, please wait ...

Step 4. Go to analysis

Step 4. Analyze Differential Expressions

Plotting, please wait ...

About Differential Protein Analyzer

Welcome to the beta version of our Differential Protein Analyzer!
In this app, we perform parametric/non-parametric hypothesis tests, calculate fold changes and visualize the results using volcano plot
Besides classical right-angle cut-off, we introduced smooth curve cut-off, which was inspired by the article by Keilhauer et al. (Mol Cell Proteomics. 2015 Jan; 14(1): 120-135.) The smooth curve is defined by the following equation: y > curvature / |x-"Log2FoldChangeCutOff"| + "-Log10pValueCutOff"
Parametric test is performed using function t.test(), Non-parametric test is performed using function wilcox.test(), p values are adjusted using function p.adjust(). Result is visualized using R packages "ggplot2" and "ggplotly".