Introduction

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.

Document

Data Source

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

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Volcano plot


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Differential Analysis Results


Differential analysis is conducted employing the limma algorithm.
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Using non-imputed dataset


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Using imputed dataset


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Proteins for GO Enrichment Input


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.

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GO graph




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Proteins for KEGG Enrichment Input


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.

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KEGG graph



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Proteins for PPI Input


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.

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PPI graph



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[For plot]

Volcano plot


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Differential Analysis Results


Differential analysis is conducted employing the limma algorithm.
Download table
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Download
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Using non-imputed dataset


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It may take a while to analyze, thank you for your patience

Using imputed dataset


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It may take a while to analyze, thank you for your patience


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File upload

View example
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The Example data could be downloaded here.


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Volcano plot


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Differential Analysis Results


Differential analysis is conducted employing the limma algorithm.
Download table
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GO enrichment



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It may take a while to analyze, thank you for your patience

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It may take a while to analyze, thank you for your patience

KEGG enrichment


GSEA

The protein list used for GSEA is ranked based on log2(fold change) from differential expression analysis using the limma algorithm.

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PPI

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Please upload your phosphoproteomics data and sample group information.

Overview

iProPhos can perform proteomics-related and phosphoproteomics-related analyses.

Proteome Analysis

Differential analysis

This feature allows users to explore and compare the expression patterns of their interested proteins across tumor and normal samples.

Boxplot

iProPhos generates boxplots with jitter and allows users to customize box color, point size and statistical methods.

Parameters

  • 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.

Results

pro_boxplot.png

Volcano plot

iProPhos generates volcano plots and allows users to set the cutoff value to define significance.

Parameters

  • Dataset: Select a cancer type of interest.

[For plot]

  • Protein: Input a protein of interest.
  • FDR cutoff: Input the adjusted p-value cutoff.
  • |log2FC| cutoff: Input the |log2(fold change)| cutoff. This value should be greater than 0.

Results

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.

pro_volcano.png

Table

This table (ranked by |logFC|) provides a concise summary of the differential analysis results using the limma algorithm.

pro_volcano_table.png

Correlation analysis

iProPhos allows users to evaluate protein expression correlations with scatter plots or tables.

Correlation plot

This feature investigates the correlation between two interested proteins in the specific tumor.

Parameters

  • 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.

Results

pro_correlation.png

Correlation table

This table shows the correlation of the target protein with other proteins in the selected dataset.

Parameters

  • Dataset: Select a cancer type of interest.

  • Protein: Input a protein of interest.

  • Method: Select a method for the correlation test.

Results

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