Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS)
CPADS is a web page for analysing drug sensitivity data from the GEO, TCGA, and GDSC databases. CPADS has four main functions: differential analysis, correlation analysis, pathway analysis and drug analysis.
More
Proportion of each database
Data summary
177
Datasets
268
DEG Files
165
Drugs
7833
DEG Files
30
Cancers
Number of cancers
Number of drugs
Updates
Ver 1.3.0
1. Answers to user concerns are provided in the "About" module.
2. Fixed some bugs.
Ver 1.2.0
1. Add the function of custom color in each module, users can choose the desired color scheme according to the demand.
2. Fixed some bugs.
Ver 1.1.0
1. We added a gene perturbation part based on GPSA and CGP data support, providing users with GSEA, ssGSEA, and various visualization methods.
2. The "Drug Analysis" module has been added to make it convenient for users to directly screen out differentially expressed genes between sensitive and resistant groups.
3. Features such as visitor recording and feedback were added, and some bugs were fixed.
Ver 1.0.0
A volcano map is drawn to quickly and intuitively identify genes with statistical significance and/or differential expression. Users can select the genes they are interested in and set the P value and the threshold value of logFC.
Differential Genes Data
The heatmap shows the correlations among genes between the two groups (control or treatment). The expression data were converted to log2 and then normalized by Z score and scale function.
Differential Genes Data
Expression Data
This bar graph shows the expression of GSM in samples of different sources, cell lines and genotypes in the GEO, and these results can be used for intragroup analysis. Only GSE datasets with source, cell line and genotype information are displayed in the options.
Expression Data
Clinical Data
GEO: The scatter plot shows the correlation between two genes at the expression level, and the results are provided by the ggstatsplot package. GDSC/TCGA: The scatter plot shows the relationship between the expression levels of genes selected by users and the IC50 values of drugs.
Expression Data
CorrHeatmap is a correlation matrix. Users can use Pearson or Spearman to calculate correlation coefficients, p values and confidence intervals. The lower left triangle correlation matrix is the polygene correlation of the control group. The upper right triangle correlation matrix is the polygene correlation of the treatment group.
Correlation Result Data
Expression Data
Clinical Data
GSEA plots visualize the distribution of the gene set and the enrichment score using the running score and preranked list, which are the traditional methods for visualizing GSEA results. Pictures are provided by the GseaVis package. Users can specify the number of terms (most significant) or selected term to display via parameters.
Data
The heat map shows the correlation between control (sensitivity) and treat (resident) ssgsea scores, which is calculated by GSVA single sample gene set enrichment analysis (ssgsea). The significance of the path name followed by a p value or adj.p.Val. P value: ns>0.05 *<0.05 * *<0.01***< 0.001; ****< 0.0001.
Expression Data
The Pathview diagram shows the upregulation/downregulation of genes in different KEGG pathways, which is provided by the Pathview package.
Differential Genes Data
This box graph shows the IC50 values of drugs selected by the user in groups stratified according to gene expression.
P value for all differently expressed genes
Expression Data
IC50 Data
This box graph shows the IC50 value grouped according to the gene expression after the treatment of the gene selected by the user.
P value for all drugs
Expression Data
IC50 Data
The GSEA of the gene perturbation module showed the enrichment of differentially expressed genes in the GPSAdb[1] (a total of 6096 gene perturbation pathways) and the CGP pathway concentration from MSigDB before and after drug treatment in GEO and GDSC. The visualization methods that can be selected include GSEA plots, dot plots, bar plots, ridge plots, and enrichment plots. Users can view detailed enrichment analysis results in 'GSEA Data'.
GSEA Data
Perturbed Gene
The ssGSEA module displays the pathway score data of GEO, GDSC, and TCGA expression data in GPSAdb and CGP. Visualization methods include heatmaps and scatter plots. In the heatmap, all data are grouped according to the type of drug selected by the user, and a U test between groups is performed. The scatter plot shows the correlation between IC50 and pathway scores.
ssGSEA Data
GEO Data introduction
GDSC Data introduction
Contact us
If you have any questions, please feel free to contact us.
- Peng Luo (Leader) luopeng@smu.edu.cn
- Kexin Li (Key Developer) likexin_lwl@i.smu.edu.cn
- Hong Yang (Counselor) smuyanghong@i.smu.edu.cn
(1) These differentially expressed genes are not enriched in the pathway concentration.
(2) The species in the dataset are different from those in the gene set; for example, the GEO gene set for a mouse species cannot be enriched in a GPSAdb pathway set of human genes.
The data of GPSAdb in the gene perturbation module are sourced from GPSAdb (https://www.gpsadb.com/). This includes RNAseq data from 3048 human cell line gene knockdowns. The naming in CPADS refers to the naming method of GPSAdb, and users can access GPSAdb to obtain detailed data information.
Notably, although incomplete image display may be observed on the webpage, it does not actually affect the information in the images. You can try the following methods to obtain better viewing images:
(1) If you want to browse on a webpage, you can reduce the browser display ratio, such as adjusting it to 90%.
(2) If you want to use pictures in the article, CPADS provides vector pictures, which you can download and adjust in other editors.
This module contains GSE of sample tissue source, cell line, genotype and other information. Through these groups, CPADS shows the difference in gene expression under different groups. Therefore, only some GEO data of tissue origin, cell line and genotype can support the analysis of this module.
The "Drug analysis" module displays the correlation between gene expression levels and IC50 in the GDSC and TCGA datasets. The "By gene" module allows users to group specific gene expression levels and view the IC50 levels and differences of different drugs; the "By drug" module allows users to view the levels and differences of different gene expression levels based on the IC50 scores of specific drugs.
At the same time, users can also choose whether to batch calculate the differences in the expression of all genes for all drugs for batch screening genes related to drug sensitivity.
The delineation of sensitive versus resistant groups for data from GEO was based on the groupings in the clinical information form provided by the data uploader, and the delineation of data from GDSC and TCGA was based on the median of the IC50 predicted by the GDSC or using the pRRophetic R package.
LUAD:Lung Adenocarcinoma
SCLC:Small Cell Lung Cancer
BRCA:Breast Cancer
SKCM:Melanoma
COREAD:Colon and Rectal Cancer
HNSC:Head and Neck Cance
ESCA:Esophageal Cancer
GBM:Glioblastoma
OV:Ovarian Cancer
DLBC:Large B-cell Lymphoma
PAAD:Pancreatic adenocarcinoma
NB:Neuroblastoma
ALL:Acute Lymphoblastic Leukemia
LAML:Acute Myeloid Leukemia
STAD:Stomach Cancer
MESO:Mesothelioma
BLCA:Bladder Cancer
MM:Multiple Myeloma
LIHC:Liver Cancer
THCA:Thyroid Cancer
LUSC:Lung Squamous Cell Carcinoma
CESC:Cervical Cancer
LGG:Lower Grade Glioma
LCML:Chronic Myeloid Leukaemia
PRAD:Prostate Cancer
MB:Medulloblastoma
CLL:Lymphoid neoplasm
ACC:Adrenocortical Cancer
CESC:Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL:Cholangiocarcinoma
COAD:Colon adenocarcinoma
KICH:Kidney Chromophobe
LUSC:Lung squamous cell carcinoma
PAAD:Pancreatic adenocarcinoma
PCPG:Pheochromocytoma and Paraganglioma
READ:Rectum adenocarcinoma
SARC:Sarcoma
STAD:Stomach adenocarcinoma
TGCT:Testicular Germ Cell Tumors
THYM:Thymoma
UCS:Uterine Carcinosarcoma
UVM:Uveal Melanoma
Requests for future functions/error reports/questions about CPADS are all welcome. We truly appreciate your feedback.