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:

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:

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

get_data(

  table = "LUAD_Academia_protein",

  action = "expression",

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

)

Arguments

table	For action = expression, use dataset$Abbre to get all tables; For action = clinic, remove _protein/_mRNA/_Phospho from dataset$Abbre.
action	"expression", "degs" or "clinic".
gene	Gene symbols, you can input one or multiple symbols.

1. get_expr_data()

Description

Get the mRNA/protein expression data in CPTAC database.

Usage

get_expr_data(

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

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

)

Arguments

datasets	Dataset names, you can input one or multiple datasets. Use 'dataset$Abbre' to get all datasets.
genes	Gene symbols, you can input one or multiple symbols.

1. Get_DEGs_result()

Description

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

Usage

get_DEGs_result(dataset = "LUAD_CPTAC_protein", method = "t.test")

Arguments

dataset	Use dataset$Abbre to get all tables.
method	"limma" or "t.test".

1. merge_clinic_data()

Description

Get clinic data and merge it with expression data.

Usage

merge_clinic_data(cohort = "LUAD_APOLLO", data_input)

Arguments

cohort	Data cohort, for example, "LUAD_APOLLO", "LUAD_CPTAC".
data_input	Expression data obtained from get_expr_data() function.

1. cor_cancer_genelist()

Description

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

Usage

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

dataset1	Dataset name. Use 'dataset$Abbre' to get all datasets.
id1	Gene symbol, you can input one gene symbols.
dataset2	Dataset name. Use 'dataset$Abbre' to get all datasets.
id2	Gene symbols, you can input one or multiple symbols.
sample_type	Sample type used for correlation analysis, default all types: c("Tumor", "Normal").
cor_method	cor_method for correlation analysis, default "pearson".

1. cor_pancancer_genelist()

Description

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

Usage

cor_pancancer_genelist(

  df,

  geneset_data,

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

  cor_method = "pearson"

)

Arguments

df	The expression data of the target gene in multiple datasets, obtained by the get_expr_data() function.
geneset_data	The expression data of a genelist in multiple datasets, obtained by the get_expr_data() function.
sample_type	Sample type used for correlation analysis, default all types: c("Tumor", "Normal").
cor_method	Method for correlation analysis, default "pearson".

1. cor_pancancer_drug()

Description

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

Usage

cor_pancancer_drug(

  df,

  cor_method = "pearson",

  Target.pathway = c("Cell cycle")

)

Arguments

df	The expression data of the target gene in multiple datasets, obtained by the get_expr_data() function.
cor_method	Method for correlation analysis, default "pearson".
Target.pathway	The signaling pathways of anti-tumor drug targets, default "Cell cycle". Use "drug_info"to get the detail infomation of these drugs.

1. cor_pancancer_TIL

Description

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

Usage

cor_pancancer_TIL(df, cor_method = "spearman", TIL_type = c("TIMER"))

Arguments

df	The expression data of the target gene in multiple datasets, obtained by the get_expr_data() function.
cor_method	Method for correlation analysis, default "pearson".
TIL_type	Algorithm for calculating immune cell infiltration, default "TIMER".

1. viz_TvsN()

Description

Visualizing the different expression of mRNA/protein expression data between Tumor and Normal tissues in CPTAC database.

Usage

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

viz_DEGs_volcano(

  df,

  p.cut = 0.05,

  logFC.cut = 1,

  show.top = FALSE,

  show.labels = NULL

)

Arguments

cohort	Data cohort, for example, "LUAD_APOLLO", "LUAD_CPTAC".
data_input	Expression data obtained from get_expr_data() function.

1. viz_cor_heatmap()

Description

Presenting correlation analysis results using heat maps based on ggplot2.

Usage

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

viz_corplot(

  data,

  a,

  b,

  method = "pearson",

  x_lab = " relative expression",

  y_lab = " relative expression"

)

Arguments

data	A gene expression dataset with at least two genes included, rows represent samples, and columns represent gene expression in the matrix.
a	Gene A
b	Gene B
method	Method for correlation analysis, "pearson" or "spearman".
x_lab	X-axis label.
y_lab	Y-axis label.

1. viz_phoso_sites()

Description

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

Usage

viz_phoso_sites(gene = "YTHDC2", phoso_infoDB = "CPTAC")

Arguments

gene	Gene/protein symbol.
phoso_infoDB	Database for extracting phosphorylation site information. only supports 'UniProt' and 'CPTAC', Default "CPTAC".