A Survey of Flow Cytometry Data Analysis Methods <s> Results <s> A pattern recognition system based on Neural Network Analysis, a form of artificial intelligence, was used to search DNA flow cytometry histograms for features that correlated with breast cancer patients' risk of relapse. DNA flow cytometry histograms and clinical followup information from 796 breast cancer patients were used to train a Neural Network to predict the clinical outcome of patients in a separate independent set of 794 patients. Median follow-up in this patient data base was short, 23 months. Neural Network Analysis resulted in a model that evaluated DNA flow cytometry histograms differently than conventional analysis, which categorizes the histograms by ploidy and S-phase fraction. Neural Network Analysis appeared to identify low risk and high risk subsets of patients as accurately as conventional analysis. Neural Network Analysis placed heavy emphasis on the region to the right of the diploid G2/M peak, where a subpopulation of nuclei with high DNA content is seen even in many histograms scored as diploid by conventional techniques. The number of nuclei in this region was found to be a powerful predictor of patient outcome, and multivariate analysis showed that the number of nuclei in this region and the S-phase fraction both were independently predictive of relapse. This pilot study suggests that conventional analysis (based on a mechanistic interpretation of regions in flow cytometry histograms) might be used in conjunction with and improved by pattern recognition systems or insights derived from them. © 1993 Wiley-Liss, Inc. <s> BIB001 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Results <s> Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems. <s> BIB002 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Results <s> Acute graft-versus-host disease (GVHD) is diagnosed by clinical and histologic criteria that are often nonspecific and typically apparent only after the disease is well established. Because GvHD is mediated by donor T cells and other immune effector cells, we sought to determine whether changes within a wide array of peripheral blood lymphocyte populations could predict the development of GvHD. Peripheral blood samples from 31 patients undergoing allogeneic blood and marrow transplant were analyzed for the proportion of 121 different subpopulations defined by 4-color combinations of lymphocyte phenotypic and activation markers at progressive time points posttransplant. Samples were processed using a newly developed high content flow cytometry technique and subjected to a spline- and functional linear discriminant analysis (FLDA)-based temporal analysis technique. This strategy identified a consistent posttransplant increase in the proportion and extent of fluctuation of CD3+CD4+CD8beta+ cells in patients who developed GVHD compared to those that did not. Although larger prospective clinical studies will be necessary to validate these results, this study demonstrates that high-content flow cytometry coupled with temporal analysis is a powerful approach for developing new diagnostic tools, and may be useful for developing a sensitive and specific predictive test for GVHD. <s> BIB003 </s> A Survey of Flow Cytometry Data Analysis Methods <s> Results <s> BACKGROUND ::: Clinical flow cytometry typically involves the sequential interpretation of two-dimensional histograms, usually culled from six or more cellular characteristics, following initial selection (gating) of cell populations based on a different subset of these characteristics. We examined the feasibility of instead treating gated n-parameter clinical flow cytometry data as objects embedded in n-dimensional space using principles of information geometry via a recently described method known as Fisher Information Non-parametric Embedding (FINE). ::: ::: ::: METHODS ::: After initial selection of relevant cell populations through an iterative gating strategy, we converted four color (six-parameter) clinical flow cytometry datasets into six-dimensional probability density functions, and calculated differences among these distributions using the Kullback-Leibler divergence (a measurement of relative distributional entropy shown to be an appropriate approximation of Fisher information distance in certain types of statistical manifolds). Neighborhood maps based on Kullback-Leibler divergences were projected onto two dimensional displays for comparison. ::: ::: ::: RESULTS ::: These methods resulted in the effective unsupervised clustering of cases of acute lymphoblastic leukemia from cases of expansion of physiologic B-cell precursors (hematogones) within a set of 54 patient samples. ::: ::: ::: CONCLUSIONS ::: The treatment of flow cytometry datasets as objects embedded in high-dimensional space (as opposed to sequential two-dimensional analyses) harbors the potential for use as a decision-support tool in clinical practice or as a means for context-based archiving and searching of clinical flow cytometry data based on high-dimensional distribution patterns contained within stored list mode data. Additional studies will be needed to further test the effectiveness of this approach in clinical practice. <s> BIB004
In Table 1 , we report the data analysis components of each paper according to the framework presented in Section 3. For the papers that reported multiple designs, multiple classifications were recorded. The designs were categorized based only on what was implemented and reported in each paper. Each column in Table 1 reports the details of each of the components of the FCM data analysis framework, including the following details of each automated gating algorithm (i) capability of supporting multidimensional gating, (ii) capability of the algorithm to determine the number of cell populations (gates) automatically, (iii) whether or not the algorithm belongs to the category of supervised or unsupervised learning techniques. All the studies covered in this review (except BIB001 BIB004 ) use percentages of cells within the identified gates and/or median fluorescent intensities of cell populations as the properties (features) of the identified gates for further analysis. Furthermore, a few studies address quality assessment  BIB003 BIB002 and normalization BIB002 of FCM data. Therefore, for effective use of space, Table 1 does not report the quality assessment, normalization and feature extraction components of the framework for each study. The entries that contain "E" refer to the term "embedded" meaning that either the cluster labelling, determining the number of cell populations, or outlier detection is embedded in the automated gating algorithm. Studies that did not implement a specific data processing component or do not have a specific capability (e.g., handling multidimensional data) have a "-" entry.