FLAME: Analyze your own or example data

Use the FLAME suite modules to define and characterize discrete populations in flow cytometric data. For an overview of the FLAME suite modules, and links to view the published data, see GenePattern's FLAME page

This page organizes links that open four FLAME modules and one FLAME pipeline on the public GenePattern server in two modes:

  1. With default parameters to load your own data, or
  2. Preloaded with example data and preset parameters.

The example data and its analysis parameters are described by Pyne et. al. in the 2009 PNAS article titled "Automated High-dimensional Flow Cytometric Data Analysis." Supplementary Figure 1 provides a schematic representation of the FLAME dataflow.

Steps below also provide links to detailed module documentation in PDF format.


Register and log in to the public GenePattern server at http://genepattern.broadinstitute.org/gp/

  • If you are new to GenePattern, start with the 10-minute Quick Start guide.

If you are analyzing your own data, the flow cytometric data should be compressed in a ZIP file. Individual files must be in TXT or FCS format and all files must be in the same format. The International Society for Advancement of Cytometry (http://www.isac-net.org/) provides details on flow cytometry data file format standards

  • FCS files are 2.0 or 3.0 format. 
  • TXT files contain a matrix of fluorescent intensities in all colors, where each row is data for one cell and each column is one probe. There should also be a header row containing the probe names. TXT files in GenePattern are always plain text as described in the File Formats Guide's Transforming Plain Text Files section. 
  • Example data ZIP file containing ten FCS files: SMALL_phospho.lymphgated.fcs.zip.

For the FLAMEContourViewer.Pipeline to launch the FLAMEViewer component, you must have Java 3D v1.3.1 or later version installed. See FAQ titled "How do I view the 3D visualization in the PCAViewer or FLAMEViewer?". Note that Java 3D is unsupported for Mac OS Leopard and later systems.


  1. Open FLAMEPreprocess or run with example data.‚Äč
What it does   Input format and information

Performs a series of preprocessing operations on flow cytometric data files, including column/channel selection, bi-exponential transformation, and optional live-cell gating.


  • The input file is a ZIP file containing flow files in TXT or FCS format. A set should be all TXT or all FCS and not mixed.
  • FLAMEPreprocess documentation 
  1. Open FLAMEMixtureModel  or run with example data.  
What it does   Input format and information

Clusters each preprocessed sample data file over a range of possible cluster numbers.


  • The input is the ZIP output file from FLAMEPreprocess containing preprocessed flow sample files in TXT format.
  • Depending on data size and parameters, this module may take hours to run. The skew distributions require more computation time.
    • If the populations are expected to be roughly symmetric, use t or normal distributions for faster response.
    • If more than ten samples are to be analyzed, analyze a few samples to determine the optimal density distribution and number of clusters. Then, analyze all samples using that density distribution and a small range of cluster numbers.
  • FLAMEMixtureModel documentation 
  1. Open FLAMEChooseOptimalClusterNumber or run with example data.
What it does   Input format and information

Determines the optimal number of clusters for each sample based on the range of cluster numbers provided to FLAMEMixtureModel.


  1. Open FLAMEMetacluster or run with example data
What it does   Input format and information

Takes data that have been optimally clustered into subpopulations and matches the subpopulations so that a given population can be identified uniformly across all samples. Each cluster is represented using a consistent color and position across all samples.




  • The input files are: (1) the ZIP output file from FLAMEChooseOptimalClusterNumber containing the optimal mixture modeling result of each sample, and (2) the sample class names file, a two-column text file where the first column contains sample names without .fcs or .txt filetype appended, and the second column contains the corresponding class names.
  • When samples in a dataset belong to different classes, the module performs two metaclusterings--within-class metaclustering and cross-class metaclustering. When all samples belong to a single class, the module performs only within-class metaclustering.
  • FLAMEMetacluster documentation 
  1. Open FLAMEContourViewer.Pipeline or run with example data
What it does   Input format and information

The pipeline runs two modules. First it runs the FLAMEContourDataGenerator module to generate the data required to display a 3-D contour plot of the clusters in a given sample. Then, it launches the FLAMEViewer to display a 3-D scatterplot and 3-D contour plot of the clusters in the sample. 



Tips for FLAME workflows using GenePattern pipelines

The FLAME modules are designed to be used in a sequential workflow, where the output file from one module is used as the input file to the next module. GenePattern makes this easy in two ways. One is by allowing you to send an output file from the Jobs Tab to the next module, and two is by allowing you to capture the workflow you ran, including the parameters, as a GenePattern Pipeline.

Capturing a pipeline requires that the original module runs utilize files from consecutive jobs stored on the Jobs Tab as described next. The created pipeline allows you or those you share the pipeline with to reproduce the original analysis.

Seemlessly input an output file into the next module

  1. After running a module, GenePattern's Jobs Tab lists the module and its output files.
  2. Click on the output file that you want use. A slide-out menu lists the modules that accept this output file as an input file. You may need to scroll down to find the desired module. Click to select.
  3. The file is automatically loaded into the next module with default parameters. Run with desired parameters.
  4. Repeat steps 1–3 with the new output file.

Create a pipeline from a completed sequence of module runs 

  1. Analyze data in sequential runs as described above.
    • For example, run data through FLAMEPreprocess, FLAMEMixtureModel, FLAMEChooseOptimalClusterNumber, and FLAMEMetacluster using Jobs Tab outputs. 
  2. Within the Jobs Tab, click the output ZIP file generated by the final module, e.g. FLAMEMetacluster, containing the final results. An option menu slides out.
  3. Select the Create Pipeline option. Name the pipeline to create it.