CytometryML, Binary Data Standards

摘要

CytometryML is a proposed new Analytical Cytology (Cytomics) data standard, which is based on a common set of XMLschemas for encoding flow cytometry and digital microscopy text based data types (metadata). CytometryML schemasreference both DICOM (Digital Imaging and Communications in Medicine) codes and FCS keywords. Flow CytometryStandard (FCS) list-mode has been mapped to the DICOM Waveform Information Object. The separation of the largebinary data objects (list mode and image data) from the XML description of the metadata permits the metadata to bedirectly displayed, analyzed, and reported with standard commercial software packages; the direct use of XML languages;and direct interfacing with clinical information systems. The separation of the binary data into its own files simplifiesparsing because all extraneous header data has been eliminated. The storage of images as two-dimensional arrays withoutany extraneous data, such as in the Adobe® Photoshop® RAW format, facilitates the development by scientists of theirown analysis and visualization software. Adobe Photoshop provided the display infrastructure and the translation facilityto interconvert between the image data from commercial formats and RAW format. Similarly, the storage and parsing oflist mode binary data type with a group of parameters that are specified at compilation time is straight forward. Howeverwhen the user is permitted at run-time to select a subset of the parameters and/or specify results of mathematical manipulations,the development of special software was required. The use of CytometryML will permit investigators to be able tocreate their own interoperable data analysis software and to employ commercially available software to disseminate theirdata.