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Galaxy Tools for Building NG-CHMs

Galaxy is a popular open source platform for performing bioinformatics analysis in a documented, repeatable process.

NG-CHM tools are available for creating and viewing heat maps on the Galaxy platform.

Tutorials

Tutorial videos:

Text-based tutorials:

Installing the NG-CHM Galaxy Tools:

The NG-CHM Galaxy tools are available in the Galaxy tool shed. Two components need to be installed to fully utilize the NG-CHM heat map tools in Galaxy: the heat map generator tool for creating heat maps and the NG-CHM Viewer plug-in for visualizing and dynamically exploring heat maps. To install the NG-CHM tools in an existing Galaxy server, please follow the instructions on our Galaxy tool shed page. Search for ‘ng-chm’ in the toolshed or use this link


Creating an NG-CHM using the NG-CHM Galaxy Tools:

The NG-CHM Generator tool is used to create an NG-CHM heat map. The options for heat map generation are described below.



Configuration Parameter Description
Input Matrix Select a data matrix that is already loaded in your current history using a Get Data tool. The data matrix needs to be a tab delimited text file with column identifiers in the first row and row identifiers in the first column. For example, column identifiers could be patient IDs and row identifiers could be gene symbols. The other cells of the matrix should be numeric values. It is often advisable to perform normalization on data prior to clustering and several other Galaxy tools are available to perform data matrix normalization.
Heat Map Name A short user-defined name for the heat map that will be displayed as its title.
Heat Map Description A longer user-defined description for the heat map that will be available in the viewer via mouse over of the name.
Data Summarization Method For large data matrices, it is necessary to summarize multiple values into a single pixel in the summary NG-CHM heat map panel. The possible summarization methods include: average, sample, and mode. Average will compute an average value to represent several data values. Sample will just draw every Nth value. Mode will use the most prevalent value as the summary value. In general, average works best to summarize the data in an intuitive fashion. In the case of categorical data, Mode will be a better summary value.
Row / Column Ordering Method Generally user are interested in clustering both rows and columns but for some analysis it may be useful to cluster just rows or columns. Use the Ordering Method selections to indicate which axes you wish to cluster and for those not being clustered whether you would like original data order or random order.
Row / Column Distance Metric If hierarchical clustering is selected for ordering method, a technique for measuring distance between rows/columns must be selected. The available distance measures are provided by R and include: Euclidean, Binary, Manhattan, Maximum, Canberra, Minkowski, and Correlation.
Row / Column Clustering Method If hierarchical clustering is selected for ordering method, an algorithm for clustering must be selected. The algorithms are provided by the R hclust function and include: Complete Linkage, Single Linkage, Ward, Mcquitty, Median, and Centroid. (Note: The Galaxy tool supports all of these; however the online GUI builder does not support Median or Centroid). If you are not familiar with these algorithms, Ward is often a good initial choice.
Row / Column Tree Cut Covariate NG-CHMs can show annotation data associated with rows / columns as covariate bars. The tool can automatically generate a category covariate bar using the clustering information by ‘cutting’ the data using the N largest clusters in the dendrogram. Set this option to something other than 0 to get a category classification bar with the number of categories (between 2 and 10).
Row / Column Linkout Data Type NG-CHM supports linkout functions for the rows or columns of some heat maps. If your data identifiers (row / column headers) are of a type for which NG-CHM provides linkouts, select the data type to activate linkouts in the viewer. For example, if your rows are gene symbols select the ‘Gene HUGO symbol’ to activate many link out options in the Heat Map Viewer.
Covariate Bars As mentioned above, NG-CHMs can show annotation data associated with rows / columns as covariate bars. For example, if your columns are patients and you want to display smoker status in the heat map, the smoking status of the patients can be loaded as a covariate bar. Any number of covariate bars can be added. Select the + Insert Covariate Bars to add one.
Axis Covariate Name User-defined name for the covariate that will be displayed in the heat map.
Covariate File A tab-delimited text file with covariate data. The first column of the file must contain row or column identifiers that match the row / column identifiers in the data matrix file. The second column must contain the covariate data. Covariate data can be categories (e.g. smoker, non-smoker) or continuous data (21, 34, 22, 41).
Axis Covariate Type Select whether the covariate bar is a row or column bar and whether it is categorical data or continuous data.



After the NG-CHM Generator tool completes running, you can view and explore the resulting heat map by clicking on the heat map item in the Galaxy history. Once the history item is open, there is a visualization button on the bottom of the history item (circled in red above). Select the NG-CHM Heat Map Viewer after clicking the visualize icon. Note: you must be logged on in Galaxy to see the visualization icon.

Heat maps can be explored in galaxy using zoom, pan, selection, link outs, and customization as described in the sections above. Also, you can click on the download icon in the Galaxy history (disk icon) to get a local copy of the heat map that can be shared and viewed with the stand-alone NG-CHM viewer outside of Galaxy.

Open-Source Tool Repository On Github

The source code for the Galaxy tools and additional documentation can also be found in our Github Repository


NG-CHM Docker Container

Docker is a container system that runs on Windows, Apple, and Unix systems. Docker makes it fast and easy to run software packages because a Docker image includes all of the software and system dependencies needed to run the application. We have created a Docker image that has a full implementation of Galaxy with the NG-CHM tools pre-installed. The NG-CHM Galaxy Docker image is available on Docker Hub.


Please see our Docker Hub page:

NG-CHM Docker Image

The Docker Hub page provides instructions on installing and running the NG-CHM Galaxy image.

Also, see our tutorial video for an overview of running a Docker NG-CHM Galaxy container and creating/viewing heat maps.



NG-CHM Builder Options

To see all your options for building NG-CHMs, see our NG-CHM Builders page.