GenePattern Flow Cytometry Suite  Print-icon

Overview

The GenePattern flow cytometry suite was developed by the Brinkman lab at the British Columbia Cancer Research Center (BCCRC), who collaborated with the GenePattern team to make these modules publicly available and easily accessible in GenePattern.  The suite consists of 34 modules in 3 general categories, discussed below. 

The workflow associated with these modules (as described in the forthcoming article) is outlined here:

Note: The ImmPort modules are designed to work together to process data files and  perform density-based clustering for automated population identification of multi-dimensional flow cytometry data based on the FLOCK (FLOw cytometry Clustering without K) algorithm. For more information about these modules, read their associated documentation.  In addition, there are two pipelines to facilitate use of the ImmPort modules.

Categories Within the Suite

There are three categories of modules within the suite, covering data preprocessing, quality assessment, and gating and clustering.  The modules in these suites may span multiple steps of the flow cytometry flowchart.

Data Preprocessing

Flow cytometry Data Preprocessing suite includes various utilities for FCS data preview and transformations, conversion between spreadsheets (i.e., CSV files) FCS files, merging and sub-sampling data, editing keywords in FCS files and other support tools.

Quality Assessment

Flow cytometry Quality Assessment suite includes modules implementing various approaches to automatically assess the quality of flow cytometry data. Such an assessment represents an important part of any data analysis, and quality control tests should be included at the beginning of data analysis and often at other steps of an analytical pipeline to identify differences in samples originating from changes in conditions that are probably not biologically motivated. Generally, these methods establish a quality control criterion to give special consideration to abnormal samples or even exclude these from further analysis.

Gating and Clustering

Flow cytometry Gating and Clustering suite includes modules for the application of manually created gates (saved in Gating-ML) on FCS data files and various clustering algorithms developed for the use with flow cytometry data.

References

This is the complete list of references for all the modules in the suite.

Updated on December 10, 2012 16:20