This interactive web application: NOt Just Another Heatmap (NOJAH) is developed in R with Shiny to


1) Perform genome wide analysis of cancer genomic data sets
2) Provide visualization, analysis and download of MMRF CoMMpass Expression, Variant and CNV data with Cluster of Cluster Analysis
3) Perform significance of cluster analysis using a robust bootstrap approach.

The goal of this tool is to provide a one stop shop to analyze Genome Wide data or CoMMpass data or perform genomic analysis on their own data.






























Input GW Data














Data Subsetting

Download subset data

Download Subset data

Subset HeatMap Input



Consensus Clustering Input



Download Consensus Cluster Results

Download Consensus Cluster Results
Download may take a while. Once complete, the result pdf file will automatically open.

Silhouette Input




Download Silhouette Core Samples

Download Silhouette Core Samples

Core Sample based Downloads


Download Subset data

Download HM

A Genome-Wide Heatmap can be very dense. Given the limitation with the computational power required to construct a genome wide heatmap, NOJAH showcases a Genome-Wide Dendrogram.


Genome-Wide Heatmap Analysis workflow is divided into four main subparts:

1. Identify the Most Variable Features

2. Construct a HeatMap for the Most Variable Features

3. Identify Number of Clusters and Assess Cluster Stability

4. Identify Core Samples

Heatmap is updated based on the Consensus Core Samples.

However each of these components are not dependent on each other and can be used independently.




Genome-Wide Dendrogram

Measures of Spread

Identify Most Variable Features

To see the position of your 'gene of interest', use the 'Choose Option' drop down to the right
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Construct a Heatmap : Visualization of Selected Subset

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Number of clusters and Assessing cluster stability

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Core samples

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Updated HeatMap based on Consensus Core Samples

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Genome-Wide Dendrogram Options
























Heat Map Options

Clustering Measures

Heat Map colors

Consensus Clustering Options

Choose Optimal Number of clusters

Silhouette Options

Heat Map Options

Clustering Measures

Heat Map colors

Input file

Clustering Options




Select atleast two platforms to run CoC analysis

Clinical markers

Download clinical ds Example

Download Results

Consensus Clustering

Download Expression clusters
Download may take a while. Once complete, the result pdf file will automatically open.
Download Variant clusters
Download may take a while. Once complete, the result pdf file will automatically open.
Download CNV clusters
Download may take a while. Once complete, the result pdf file will automatically open.
Download CoC analysis
Download may take a while. Once complete, the result pdf file will automatically open.

Sample Clusters

Download CoC Sample clusters

CoC HeatMap

Download CoC Analysis HM

Cluster Interpretation

Download Cluster Interpretation

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Choose Optimal Number of clusters





























Clustering Measures

Heat Map Options

Input Data to test significance of clusters

Input Settings

Select first row and column where numeric data starts

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Heat Map Options

Clustering Measures

Heat Map colors

Click the button to start sampling using bootstrap method for estimating the p-value. A progress indicator will appear shortly (~approx 10 seconds), on top of page indicating the status. Once complete, the p-value will be displayed in the main panel.

Click the button to start sampling using bootstrap method for estimating the p-value. A progress indicator will appear shortly (~approx 10 seconds), on top of page indicating the status. Once complete, the p-value will be displayed in the main panel.

The National Cancer Institute (NCI) requires that publications acknowledge the Winship Cancer Institute CCSG support, and they are tracking compliance. When using this tool to report results in your publication, please include the following statement in the acknowledgment section of your publication(s):

Research reported in this publication was supported in part by the Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Authors- Manali Rupji, dual M.S., Bhakti Dwivedi Ph.D. & Jeanne Kowalski Ph.D.
Maintainer- Manali Rupji 'manali(dot)rupji(at)emory(dot)edu'

The Biostatistics and Bioinformatics Shared Resource at Winship Cancer Institute of Emory University

https://bbisr.winship.emory.edu/
This App is developed and maintained by Manali Rupji at the Biostatistics and Bioinformatics core, Winship Cancer Institute, Emory University.

As a Biostatistics and Bioinformatics core, we are actively improving and expanding our NGS analysis services and analysis products. For any questions, comments, or suggestions, please email the developer at mrupji@emory.edu.