A Survey of Flow Cytometry Data Analysis Methods <s> Discussion <s> Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods. <s> BIB001 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Discussion <s> In spite of recent advances in flow cytometry technology, most cytometry data is still analyzed manually which is labor-intensive for large datasets and prone to bias and inconsistency. We designed an automatic processing tool (APT) to rapidly and consistently define and describe cell populations across large datasets. Image processing, smoothing, and clustering algorithms were used to generate an expert system that automatically reproduces the functionality of commercial manual cytometry processing tools. The algorithms were developed using a dataset collected from CMV-infected infants and combined within a graphical user interface, to create the APT. The APT was used to identify regulatory T-cells in HIV-infected adults, based on expression of FOXP3. Results from the APT were compared directly with the manual analyses of five immunologists and showed close agreement, with a concordance correlation coefficient of 0.96 (95% CI 0.91-0.98). The APT was well accepted by users and able to process around 100 data files per hour. By applying consistent criteria to all data generated by a study, the APT can provide a level of objectivity that is difficult to match using conventional manual analysis. <s> BIB002
Although a consensus among researchers for the need of a framework to describe FCM data analysis is not well documented, we feel that it can be a useful tool to facilitate research in this field. A common framework provides a reference, not only for researcher-to-researcher interaction but also for communication to persons in related fields and professions. It will also facilitate technology crossfertilization, that is, the ability to recognize and integrate significant technological advancements made by others into one's own work. Therefore, during the course of reviewing FCM data analysis literature, we created a framework to report FCM data analysis approaches in a structured way, which facilitates the reporting and review process in the future. Our approach was to create an intuitive framework for organizing and documenting the key data analysis components described in a study and also provide a means to identify the data analysis components that have not been reported. Moreover, the use of this framework makes it easier to understand the differences between different data analysis pipelines. Table 1 provides a summary of the survey, making it a quick reference to review the results. For example, a quick look at the first row in Table 1 shows the design components used by Jeffries et al. BIB002 in their analysis of FCM data. Moreover, if somebody is interested in designing or using automated gating approaches, he/she can quickly identify the studies that address automated gating of the FCM data by referring to the third column of Table 1 . The proposed framework is flexible enough to encompass the range of data analysis approaches covered in this paper. However, Figure 3 : Percentages of studies that address different data analysis components according to the proposed framework. Note that cluster labeling approaches that are embedded in gating stage are counted in the "Cluster Labeling" entry. refining or expanding it might be necessary in the future. For example, even though a feature selection component was not needed to describe current FCM data analysis studies, addition of this component might be necessary in future. Feature selection is specifically important as it can discard the uninformative and also redundant features, facilitate data visualization and data understanding, reduce the measurement and storage requirements, reduce training and utilization times, and defy the curse of dimensionality to improve prediction performance BIB001 . Figure 3 shows the percentages of the studies that have addressed each of the data analysis components according to the proposed framework. As shown in Figure 3 , most of the studies (more than 70%) focus on automated gating of FCM data from which 65% use unsupervised techniques and 35% use supervised techniques. However, only few studies focus on quality control and normalization of FCM data, suggesting that more work might still be needed in the future. In the rest of this section we specifically discuss the FCM data analysis methods that have been used in the context of the framework introduced in Section 3.1.