### 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.

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

### Data matrix

### Meta data

### Step 3. Process data matrix before analysis

#### NOTE

Note that this pre-process is optional, and please do it before downstream analysis if necessary, instead of afterProcessing, please wait ...

### Step 4. Go to analysis

### Step 4. Analyze Differential Expressions

Plotting, please wait ...

### Volcano plot showing smooth curve threshold

### Volcano plot showing right-angle threshold

### 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".

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".