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The ProteoCancer Analysis Suite (PCAS) utilizes the CPTAC database to integrate proteomics, phosphoproteomics, and transcriptomics for cancer research. It simplifies data analysis across multiple cancer types, enhancing tumor microenvironment under-standing and aiding the development of targeted therapies.

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Citation: Wang J, Song X, Wei M, Qin L, Zhu Q, Wang S, Liang T, Hu W, Zhu X, Li J. PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data. International Journal of Molecular Sciences. 2024; 25(12):6690. https://doi.org/10.3390/ijms25126690

Users can directly access PCAS Shinyapp using the following link: <https://jingle.shinyapps.io/PCAS/>

You can also install the R package:

```R
remotes::install_github("WangJin93/PCAS")
```
Enter the PCAS\_app() function to run the PCAS app, which can be used according to the article (link) of this tool:

![](https://raw.githubusercontent.com/WangJin93/PCAS/main/PCAS.png)

The main functions of the PCAS package are:

1. **get\_data()：**

**Description**

Get the CPTAC data by using the api. All results saved in MySQL database.

**Usage**
```R
get_data(

table = "LUAD_Academia_protein",

action = "expression",

genes = c("GAPDH", "TNS1")

)
```
**Arguments**

**Description**

Get the mRNA/protein expression data in CPTAC database.

**Usage**
```R
get_expr_data(

datasets = c("LUAD_CPTAC_protein", "LSCC_CPTAC_protein"),

genes = c("TP53", "TNS1")

)

```
**Arguments**

1. **Get\_DEGs\_result()**

**Description**

Get the results of different expression analysis between tumor and normal samples in CPTAC datasets.

**Usage**
```R
get_DEGs_result(dataset = "LUAD_CPTAC_protein", method = "t.test")

```
**Arguments**

1. **merge\_clinic\_data()**

**Description**

Get clinic data and merge it with expression data.

**Usage**
```R
merge_clinic_data(cohort = "LUAD_APOLLO", data_input)

```
**Arguments**

1. **cor\_cancer\_genelist()**

**Description**

Perform correlation analysis of the mRNA/protein expression data in CPTAC database.

**Usage**
```R
cor_cancer_genelist(

dataset1 = "LUAD_CPTAC_protein",

id1 = "STAT3",

dataset2 = "LUAD_CPTAC_mRNA",

id2 = c("TNS1", "TP53"),

sample_type = c("Tumor", "Normal"),

cor_method = "pearson"

)

```
**Arguments**

1. **cor\_pancancer\_genelist()**

**Description**

Perform correlation analysis of the mRNA/protein expression data in CPTAC database.

**Usage**
```R
cor_pancancer_genelist(

df,

geneset_data,

sample_type = c("Tumor", "Normal"),

cor_method = "pearson"

)

```
**Arguments**

1. **cor\_pancancer\_drug()**

**Description**

Calculate the correlation between target gene expression and anti-tumor drug sensitivity in multiple datasets.

**Usage**
```R
cor_pancancer_drug(

df,

cor_method = "pearson",

Target.pathway = c("Cell cycle")

)

```
**Arguments**

1. **cor\_pancancer\_TIL**

**Description**

Calculate the correlation between target gene expression and immune cells infiltration in multiple datasets.

**Usage**
```R
cor_pancancer_TIL(df, cor_method = "spearman", TIL_type = c("TIMER"))

```
**Arguments**

1. **viz\_TvsN()**
### **Description**
Visualizing the different expression of mRNA/protein expression data between Tumor and Normal tissues in CPTAC database.
### **Usage**
```R
viz_TvsN(

df,

df_type = c("single", "multi_gene", "multi_set"),

Show.P.value = TRUE,

Show.P.label = TRUE,

Method = "t.test",

values = c("#00AFBB", "#FC4E07"),

Show.n = TRUE,

Show.n.location = "default"

)
```
1. **viz\_DEGs\_volcano()**

**Description**

Plotting volcano plot for DEGs between tumor and normal samples in CPTAC datasets.

**Usage**
```R
viz_DEGs_volcano(

df,

p.cut = 0.05,

logFC.cut = 1,

show.top = FALSE,

show.labels = NULL

)

```
**Arguments**

1. **viz\_cor\_heatmap()**

**Description**

Presenting correlation analysis results using heat maps based on ggplot2.

**Usage**
```R
viz_cor_heatmap(r, p)

```
**Arguments**

|r|The correlation coefficient matrix r of the correlation analysis results obtained from the functions cor\_pancancer\_genelist(), cor\_pancancer\_TIL(), and cor\_pancancer\_drug().|
| :- | :- |
|p|The P-value matrix p of the correlation analysis results obtained from the functions cor\_pancancer\_genelist(), cor\_pancancer\_TIL(), and cor\_pancancer\_drug().|

1. **viz\_corplot()**

**Description**

Scatter plot with sample size (n), correlation coefficient (r) and p value (p.value).

**Usage**
```R
viz_corplot(

data,

a,

b,

method = "pearson",

x_lab = " relative expression",

y_lab = " relative expression"

)

```
**Arguments**

1. **viz\_phoso\_sites()**

**Description**

Query phosphorylation site information of target proteins based on CPTAC database phosphorylation proteomics data or UniProt database.

**Usage**
```R
viz_phoso_sites(gene = "YTHDC2", phoso_infoDB = "CPTAC")

```
**Arguments**

PCAS

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