A Survey of Flow Cytometry Data Analysis Methods <s> Methods of Survey <s> Success in proteomics depends upon careful study design and high-quality biological samples. Advanced information technologies, and also an ability to use existing knowledge to the full, will be crucial in making sense of the data. Despite its genome-scale potential, proteome analysis is at a much earlier stage of development than genomics and gene expression (microarray) studies. Fundamental issues involving biological variability, pre-analytic factors and analytical reproducibility remain to be resolved. Consequently, the analysis of proteomics data is currently informal and relies heavily on expert opinion. Databases and software tools developed for the analysis of molecular sequences and microarrays are helpful, but are limited owing to the unique attributes of proteomics data and differing research goals. <s> BIB001 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Methods of Survey <s> The evolution of flow cytometry from a research tool to a pivotal technology for clinical diagnostic purposes has required significant efforts to standardize methods. The great advantage of flow cytometry is that it's applications are highly amenable to standardization. Here, we review the efforts that have been made for flow cytometric applications in four major fields of clinical cell analysis: CD4+ T-cell enumeration, CD34+ hematopoietic stem and progenitor cell enumeration, screening for the HLA-B27 antigen and leukemia/lymphoma immunophenotyping. These standardization efforts have been parallelled by the establishment of external quality assessment (EQA) schemes in many countries worldwide. The goal of these EQA exercises has been primarily educa-tional, but their results will increasingly serve as a basis for laboratory accreditation. This important development requires that the EQA schemes, in particular the quality of the distributed samples and the procedures for evaluating the results, meet the highest standards. <s> BIB002
FCM data analysis designs selected for this review include papers that met the following criteria. (1) The keyword "flow cytometry" and one or more of the keywords "automated analysis", "automated gating", and "automated clustering" appeared in its title, abstract, or body using Google Scholar search engine. (2) The work described one or more automated/semiautomated data analysis components. Papers that presented tutorials were not included. We use the framework presented in Section 3.1 to report components involved in FCM data analysis. Figure 2 depicts an FCM data analysis framework in which an FCM data file is analyzed through a series of analysis components. This framework has evolved from the study of FCM literature covered in this article and work in related fields, including statistics and computer science. This framework is constructed to report details of FCM data analysis studies in a systematic way to facilitate reporting and review process. The framework does not incorporate the hardware and software components used for FCM data collection. (1) Quality Assessment. Artifacts from sample preparation, handling, variations in instrument parameters, or other factors may confound experimental measurements and lead to erroneous conclusions. Therefore, quality assessment is a crucial step in the use of high-throughput flow cytometry and its associated information services  BIB001 BIB002 . The aim of data quality assessment could include detecting whether intersample variability measurements of samples are not likely to be biologically motivated. Such samples should be identified, investigated, and potentially removed from any further analyses.