Welcome to Shiny Methylation Analysis Resource Tool (SMART)

An interactive web server for analyzing DNA methylation of TCGA project.

Sample Summary

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Select a probe to view the methylation across all samples






Methylation value across all samples

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ns: p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001; ****: p <= 0.0001.

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Select multiple probes to view the aggregated methylation across all samples




CpG-aggregated methylation value across all samples

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ns: p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001; ****: p <= 0.0001.

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Differential Methylation Analysis




This section allows users to apply custom thresholds on a given cancer type to analyze differentially methylated CpGs and their chromosomal distribution. The p.value is calculated using Wilcoxon rank sum test, and is adjusted using Benjamini-Hochberg method.

  • Dataset: Select a cancer type of interest. Usually, performing differential analysis using less than 10 samples would affect the performance of statistical test and provide less reliable results. Therefore, cancer types with normal sample less than 10 are excluded for differential methylation analysis.
  • |Beta-value| Cutoff: Set custom Beta-value difference threshold. Beta-value difference is defined as mean(tumor)-mean(normal), using Beta-value.
  • |M-value| Cutoff: Set custom M-value difference threshold. M-value difference is defined as mean(tumor)-mean(normal), using M-value.
  • Adj.pvalue Cutoff: Set custom adjusted p.value threshold.
  • Chromosome distribution: Select “Hyper-methylated” or “Hypo-methylated” for chromosomal distribution plots, and set custom color.
  • Display density: Plot relative density of differential methylated CpGs to get a more informative plot.



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    Differential methylated CpGs


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    Sometimes users only want to visualize specific CpGs. To address this need, SMART offers a function that allows users to flexibly visualize CpGs. First, select CpGs that you want to plot from above table, check the corresponding chromosome locations. Next, select chromosomes that you checked from the above table in the panel, click the plot button, a beautiful circular plot showing the selected CpG sites and relative density will be displayed.






    Circos plot (Select differentially methylated probes from above table)

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    Methylation Box Plot (Tumor vs. Normal)




    SMART generates box plots with jitter for comparing methylation in a given cancer type. SMART will first display all associated CpGs of the input gene, and users can select the probe(s) of interest for visualization.

  • Dataset: Select a cancer type of interest. Note that there are some cancer types which have no normal samples for comparison (see home page for sample summary).
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Tumor Color: Set the box color of tumor group.
  • Normal Color: Set the box color of normal group.
  • Gene: Input a gene of interest.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".
  • Same Y axis: When multiple CpGs are selcted, choose whether to use the same y axis scale for visualization.
  • Box Width: Set the width of box.
  • Jitter Width: Set the width of jitter across the box.
  • Download Figure Height: Set the height of the download figure.
  • Download Figure Width: Set the width of the download figure.








  • Select probe(s) to view the methylation box plot




    Comparison of methylation value between normal and tumor samples

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    Methylation Stage Plot




    This feature displays methylation box plots based on the pathological stage.

  • Gene: Input a gene of interest.
  • Dataset: Select a cancer type of interest. Note that there are some cancer types that have no pathological stage information, and these cancer types are not displayed.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".
  • Major Stage: Select pathological major stages or substages for plotting.
  • Same Y axis: When multiple CpGs are selcted, choose whether to use the same y axis scale for visualization.
  • Box Width: Set the width of box.
  • Jitter Width: Set the width of jitter across the box.
  • Download Figure Height: Set the height of the download figure.
  • Download Figure Width: Set the width of the download figure.









  • Select probe(s) to view the methylation stage plot




    Methylation value among different pathological stages

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    Methylation (Somatic Mutation)




    This feature allows users to compare methylation among tumors with or without the presence of mutation for a given gene.

  • Gene: Input a gene of interest.
  • Dataset: Select a cancer type of interest.
  • Mutation Color: Set the box color of mutation group.
  • Wild Type Color: Set the box color of wild type group.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".
  • Same Y axis: When multiple CpGs are selcted, choose whether to use the same y axis scale for visualization.
  • Box Width: Set the width of box.
  • Jitter Width: Set the width of jitter across the box.
  • Download Figure Height: Set the height of the download figure.
  • Download Figure Width: Set the width of the download figure.








  • Mutation status of the input gene

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    Select probe(s) to view the relationship between methylation and mutation




    Methylation value with mutation

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    Methylation (CNV)




    This feature allows users to compare methylation among tumors with different copy number variations for a given gene.

    Gene-level copy number variation (CNV) is estimated using the GISTIC2 threshold method.

    The estimated values -2, -1, 0, 1, 2, representing homozygous deletion, single copy deletion, diploid normal copy, low-level copy number amplification, and high-level copy number amplification.

  • Gene: Input a gene of interest.
  • Dataset: Select a cancer type of interest.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".
  • Same Y axis: When multiple CpGs are selcted, choose whether to use the same y axis scale for visualization.
  • Box Width: Set the width of box.
  • Jitter Width: Set the width of jitter across the box.
  • Download Figure Height: Set the height of the download figure.
  • Download Figure Width: Set the width of the download figure.








  • Select probe(s) to view the relationship between methylation and CNV




    Methylation value with CNV

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    Correlation (Gene-level)




    This feature performs pair-wise correlation analysis to explore the correlation between the expression and DNA methylation, using methods including Pearson, Spearman and Kendall. All samples, including normal samples (if available), with both gene expression and methylation data are included for correlation analysis.

    SMART uses the log2-scaled(TPM+1) value (gene) and Beta-value or M-value (probe) for calculation.

  • Gene: Input a gene of interest.
  • Dataset: Select a cancer type of interest.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Correlation Coefficient: The method for calculating the correlation coefficient.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".








  • Select probe(s) to view the correlation between methylation and gene expression (Multiple selection supported)





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    Correlation (Transcript-level)




    This feature performs pair-wise correlation analysis to explore the correlation between the expression (transcript-level) and DNA methylation, using methods including Pearson, Spearman and Kendall.

    SMART uses the log2-scaled(TPM+1) value (gene) and Beta-value or M-value (probe) for calculation.

  • Gene: Input a gene of interest.
  • Dataset: Select a cancer type of interest.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Correlation Coefficient: The method for calculating the correlation coefficient.
  • Aggregation Method: Select a statistical method for CpG aggregation (Mean or Median). This will calculate the mean/median methylation for all the individual CpGs that users selected. The result is displayed as the box plot titled "Aggregation".








  • Select a transcript to view the distribution of the transcript and CpG sites




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    Select probes (Multiple selection supported)



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    Univariate Cox Regression Analysis




    This feature performs overall survival (OS) or disease free interval (DFI, also known as recurrent free survival,DFS) related univariate Cox regression analysis. OS, 1 for death from any cause, 0 for alive. DFS (DFI), 1 for patient having new tumor event whether it is a local recurrence, distant metastasis, new primary tumor of the cancer.

  • Dataset: Select a cancer type of interest.
  • Method: Select OS or DFS for analysis.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Probe List: Copy and paste a list of probes into the box.








  • View methylation and clinical data

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    Check results

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    Multivariate Cox Regression Analysis




    This feature performs overall survival (OS) or disease free interval (DFI, also known as recurrent free survival,DFS) related Multivariate Cox regression analysis. OS, 1 for death from any cause, 0 for alive. DFS (DFI), 1 for patient having new tumor event whether it is a local recurrence, distant metastasis, new primary tumor of the cancer.

  • Dataset: Select a cancer type of interest.
  • Method: Select OS or DFS for analysis.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Probe List: Copy and paste a list of probes into the box.
  • Clinical Factor: Available for multivariate Cox analysis, including Age, Gender, Race and Stage.









  • View methylation and clinical data

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    Check results

    
                            
                              
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    Survival Curve




    This feature draws overall survival (OS) or disease free interval (DFI, also known as recurrent free survival, DFS) related survival curves, using Log-rank test for hypothesis test. Users can select a suitable threshold for splitting the high-methylation and low-methylation groups. The hazard ratio and the 95% confidence interval information based on the group cutoff will also be included in the survival plot. OS, 1 for death from any cause, 0 for alive. DFS (DFI), 1 for patient having new tumor event whether it is a local recurrence, distant metastasis, new primary tumor of the cancer.

  • Dataset: Select a cancer type of interest.
  • Method: Select OS or DFS for analysis.
  • Methylation Value: Select Beta-value or M-value for analysis.
  • Group Cutoff: Select a threshold for splitting the high-methylation and low-methylation groups.
  • Cutoff-High(%): Samples with methylation level higher than this cutoff are considered as the high-methylation group.
  • Cutoff-Low(%): Samples with methylation level lower than this cutoff are considered the low-methylation group.
  • High Group Color: Set the color of high-methylation group.
  • Low Group Color: Set the color of lwo-methylation group.
  • Unit: Days or Months for plotting.
  • Probe: Input a probe of interest.









  • About


    The SMART (Shiny Methylation Analysis Resource Tool) App is a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project.


    Data


    The data used in the SMART App are directly pulled down from UCSC Xena public data hubs (https://xenabrowser.net) upon users’ request. Some minor processing is performed to ensure the consistency of the sample names.

    DNA methylation (Methylation450K) (n=9,639) Pan-Cancer Atlas Hub

    TOIL RSEM tpm (n=10,535) UCSC Toil RNAseq Recompute (Gene expression RNAseq)

    TOIL RSEM tpm (n=10,535) UCSC Toil RNAseq Recompute (Transcript expression RNAseq)

    Curated clinical data (n=12,591) Pan-Cancer Atlas Hub

    Gene-level copy number (GISTIC2 thresholded) (n=10,845) TCGA hub

    Gene level non-silent mutation (n=9,104) Pan-Cancer Atlas Hub


    Getting Started


    This is the SMART App start screen:



    The home page displays the number of DNA methylation samples available from TCGA project and provides a quick search interface. Users can enter a gene symbol (e.g. TRIM58) into the ‘Quick Start’ box to search for a gene of interest.

    By clicking the “Go” button, a circular plot showing the chromosomal distribution of all associated CpGs of the input gene will be displayed. To help users gain more useful information about the CpGs and their genomic locations along with transcripts, a detailed segment plot highlighting the transcripts, exons, UTR, CDS, CpG island regions, shelves and shores is displayed by clicking the "Click to view the detailed chromosomal distribution". The name and the type of each transcript is given. The genomic length is shown below. By default, the distance between any adjacent two lines stands for 1k. Users can set the distance scale. The yellow arrow at the top stands for the strand direction, that is, towards right, +, towards left, -. The coverage of the CpGs islands are displayed as the red regions.

    Clicking the "Click to check probe-level methylation" will display a table showing the detailed information these probes, and users can select one of these probes to view its pan-cancer methylation profile and identify dys-methylated sites for further analysis. Clicking the "Click to check gene-level methylation" will display the gene-level pan-cancer methylation profile.

    Differential analysis:



    The SMART app allows users to set custom cut-off values for a given cancer type to dynamically obtain differentially methylated CpGs and their chromosomal distribution. When all parameters are set, the chromosomal distribution plot and the list of differentially methylated CpGs based on input parameters will be displayed.


    The SMART App offers a function that allows users to flexibly visualize CpGs. Users can select CpGs that you want to plot from the table, check the corresponding chromosome locations, select chromosomes that you checked from the above table in the panel, and click the plot button, a beautiful circular plot showing the selected CpG sites and relative density will be displayed. If no chromosome is selected, the SMART App will automatically display all chromosomes.

    Methylation DIY:



    This module provides functions for users to comprehensively analyze DNA methylation taking other omics data and clinical stage into consideration. Users can select multiple probes at the same time for easier visibility and interpretation. The returned box plots will display all the selected probes plus an aggregation box plot showing the mean/median methylation of all the selected probes.

    The first panel generates custom box plots for users for compare CpGs of genes between normal and tumor samples in a given cancer type.

    The second panel plots methylation by pathological stages based on the TCGA clinical data. Two options are available, namely, major stage and sub-stage. For example, if users choose major stage for plotting, stage IIA/IIB will be included into the stage II group.

    The third panel offers a function for plotting box plots comparing methylation between mutation and wild type groups.

    The fourth panel provides a function for researchers to study the possible association between CNV and DNA methylation.

    Correlation:



    For correlation analysis, one can choose to analyze the correlation at gene-level or transcript-level. When analyzing the correlation at transcript-level, a segment plot highlighting the genomic locations of the transcript and CpGs will be displayed and the distances of each probe to TSS will also be shown in the table below for users to locate the ones at the promoter region .




    Users can select probes from the table to visualize the correlation between expression and methylation. The results are displayed as scatter and distribution plots. In distribution plot, each bar represents a sample, the names of the gene/transcript and CpGs are shown on the right, the methylation and expression values are shown on the left. The samples are reorders according to the expression value.

    Survival:




    For Survival analysis, the SMART App offers univariate Cox regression analysis. Users can copy and paste a list of CpGs into the box, select the cancer type of interest to conduct Cox regression analysis. The hazard ratio, 95% confidence interval, z-score and p-value will be given. Once users have identified the significant variables, they can use SMART app to draw survival curves. The thresholds for high/low methylation level cohorts can be adjusted by users. .


    Results Download


    All data in the SMART App are accessible. Figures are rendered as Portable Document Format (PDF), which can be edited by Adobe Illustrator.


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    Development

    The development of the SMART App is ongoing and we intend to add more functions and promote results visualization. Comments and suggestions from users for the future development of the application are greatly appreciated.

    Download SMART App



    The SMART App can be launched on any computer that has R installed. Once you have downloaded the SMART App, unzip the file, open the Manual.html, and it will help you install SMART App locally.

    Methylation Manifest File