iProPhos is a user-friendly interactive web portal that provides multiple analysis modules to explore and visualize functional proteomics and phosphoproteomics across 12 cancer types.
iProPhos contains a large number of samples including 1,444 tumor samples and 746 normal samples across 12 cancer types. Transcriptome, proteome, phosphoproteome, and clinical data are obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) project (https://proteomics.cancer.gov/programs/cptac).
CPTAC | Disease Type | Proteome (Tumor+Normal) | Phosphoproteome (Tumor+Normal) | Transcriptome (Tumor only) | Publication |
---|---|---|---|---|---|
BRCA | Breast Invasive Carcinoma | 140(122+18) | 140(122+18) | 122 | PMID: 33212010 |
CCRCC | Clear Cell Renal Cell Carcinoma | 194(110+84) | 194(110+84) | 110 | PMID: 31675502 |
COAD | Colon Adenocarcinoma | 197(97+100) | 197(97+100) | 96 | PMID: 31031003 |
GBM | Glioblastoma | 109(99+10) | 109(99+10) | 99 | PMID: 33577785 |
HCC | HBV-Related Hepatocellular Carcinoma | 318(159+159) | 318(159+159) | 159 | PMID: 31585088 |
HNSCC | Head and Neck Squamous Cell Carcinoma | 171(108+63) | 171(108+63) | 108 | PMID: 33417831 |
LUAD | Lung Adenocarcinoma | 211(110+101) | 211(110+101) | 110 | PMID: 32649874 |
LSCC | Lung Squamous Cell Carcinoma | 207(108+99) | 207(108+99) | 108 | PMID: 34358469 |
OV | Ovarian Serous Cystadenocarcinoma | 103(83+20) | 103(83+20) | 82 | PMID: 32529193 |
PBT | Pediatric Brain Tumor | 218(218+0) | 218(218+0) | 188 | PMID: 33242424 |
PDA | Pancreatic Ductal Adenocarcinoma | 202(135+67) | 202(135+67) | 135 | PMID: 34534465 |
UCEC | Uterine Corpus Endometrial Carcinoma | 120(95+25) | 120(95+25) | 95 | PMID: 32059776 |
limma
algorithm.
The table below displays the results of differential analysis conducted using the limma algorithm. The data has been filtered based on your customized cutoff values. Please review the listed proteins for further input in the GO enrichment analysis. Then, click on the
"Plot"
button to generate plots.
The table below displays the results of differential analysis conducted using the limma algorithm. The data has been filtered based on your customized cutoff values. Please review the listed proteins for further input in the KEGG enrichment analysis. Then, click on the
"Plot"
button to generate plots.
The table below displays the results of differential analysis conducted using the limma algorithm. The data has been filtered based on your customized cutoff values. Please review the listed proteins for further input in the PPI analysis. Then, click on the
"Plot"
button to generate plots.
iProPhos supports the visualization of a PPI network for up to 200 differential proteins, ranked by logFC.
limma
algorithm.
limma
algorithm.
The protein list used for GSEA is ranked based on log2(fold change) from differential expression analysis using the limma algorithm.
iProPhos can perform proteomics-related and phosphoproteomics-related analyses.
This feature allows users to explore and compare the expression patterns of their interested proteins across tumor and normal samples.
iProPhos generates boxplots with jitter and allows users to customize box color, point size and statistical methods.
Dataset: Select a cancer type of interest.
Protein: Input a protein of interest. Note:
The available proteins in each dataset vary. Only 1000 proteins from the respective dataset are shown in the dropdown list, and users can also manually input proteins with auto-completion. If a protein that is not present in the selected dataset is input, it will be treated as a null value and result in an error message.
Tumor color: Set the box color in tumor samples.
Normal color: Set the box color in normal samples.
Point Size: Set the point size.
Differential Methods: Select a method for differential analysis.
t-test: two-tailed test, assuming unequal variances.
wilcox.test: Wilcoxon rank-sum test.
anova: assuming equal variances.
The t-test is appropriate when the data is normally distributed. The Wilcoxon test is suitable when the data does not meet the assumptions of normality. ANOVA is useful when assuming normality and equal variances. The choice of the appropriate test should be based on the specific characteristics of the data.
This analysis involves individual protein without multiple comparisons, so it is not corrected for multiple testing.
iProPhos generates volcano plots and allows users to set the cutoff value to define significance.
[For plot]
Upregulated and downregulated proteins in tumor samples are labeled orange and blue respectively, while gray means non-significance. Moreover, the interested protein can be magnified and highlighted with its gene symbol.
This table (ranked by |logFC|) provides a concise summary of the differential analysis results using the limma algorithm.
iProPhos allows users to evaluate protein expression correlations with scatter plots or tables.
This feature investigates the correlation between two interested proteins in the specific tumor.
Dataset: Select a cancer type of interest.
Protein A: Input a protein A of interest. [For x-axis]
Protein B: Input a protein B of interest. [For y-axis]
Color for non-imputed data: Set the point color for non-imputed data.
Color for imputed data: Set the point color for KNN imputed data.
Point Size: Set the point size.
Method: Select a method for the correlation test.
pearson: Pearson correlation assumes that the variables are normally distributed and have a linear relationship.
spearman: Spearman correlation assesses the non-linear relationship between two variables, and it does not assume normality.
kendall: Kendall correlation is a non-parametric correlation measure that assesses the strength of association between two variables without assuming linearity.
This analysis involves individual protein pair without multiple comparisons, so it is not corrected for multiple testing.
This table shows the correlation of the target protein with other proteins in the selected dataset.
Dataset: Select a cancer type of interest.
Protein: Input a protein of interest.
Method: Select a method for the correlation test.
Correlation analysis provides results both with and without imputation. The table has been ranked by the correlation coefficient, and p-values have been adjusted using the Benjamini-Hochberg (BH) method.
Using non-imputed dataset