Gating-ML

Functions to import gates, transformations and compensations of cytometry data.

An object of the gatingML class is a set of compensations, transformations, and gates as defined by a Gating-ML file.

To create a gatingML object by specifying the Gating-ML file, use the gatingML constructor. To associate the gatingML object with a particular fcs file, use the load_fcs_file method. To apply gates to the loaded fcs file, use the apply_gate method.

gatingML requires two functions from the the Mathworks File Exchange: the XMLNode function (#34711) and the fca_readfcs function (#9608). Versions of them that are compatible with this implementation of gatingML are included here.

If you will be using the logicle and/or hyper log transformations, you will need to download and install the logicleTransform (#45022) and/or hyperlogTransform (#45034).

A gatingML object has the following properties:

xml: An XMLNode object created from the Gating-ML file
transforms: A structure array of transformations included in the Gating-ML file. A function to apply a transformation with name trans_name is found in obj.transforms.(trans_name).fun
comps: A structure array of compensations included in the Gating-ML file, each including the compensation matrix and relevant parameters
gates: A structure array of gates included in the Gating-ML file, with fields listing original gate name, parent gate, and gate type. The method apply_gate adds the field inGate, which is a logical vector indicating each cell's inclusion in the gate.

fcsData: A structure array of compensated data matrices, which are created by gatingML.load_fcsfile. The original data is assumed to have the channel-to-scale transformations already applied. To load this data from an fcs file, use the third output (fcsdatscaled) from the function fca_readfcs. The method load_fcs_file then creates a matrix and list of relevant parameters for each compensation.

fcsHdr: An fcs file header created from fca_readfcs.m, which is created by the method load_fcs_file

An example can be found in the file gatingML_example.m

References:

J. Spidlen, International Society for the Advancement of Cytometry Data Standards Task Force, and Brinkman, R.R. (2013). Gating-ML 2.0 -- International Society for Advancement of Cytometry (ISAC) stanford for representing gating descriptions in flow cytometry. Retrieved from http://flowcyt.sourceforge.net/gating/20130122.pdf

J. Spidlen and N. Gopalakrishnan. gatingMLData: Data and XML files for Gating-ML Test suite. R package version 2.1.4.