Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high‐dimensional objects

Finn, William G.; Carter, Kevin M.; Raich, Raviv; Stoolman, Lloyd M.; Hero, Alfred O.

doi:10.1002/cyto.b.20435

Cited by 41 publications

(40 citation statements)

References 18 publications

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“…Analyzing and visualizing multidimensional data are certainly not a new endeavor, and there have been a number of innovative approaches to this problem (22)(23)(24)(25)(26)(27)(28)(29)(30). Most all these methods are intent on mapping multidimensional populations into two-or three-dimensional space after some type of clustering algorithm.…”

Section: Discussionmentioning

confidence: 99%

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Probability state modeling theory

Bagwell¹,

Hunsberger²,

Herbert³

et al. 2015

Cytometry Pt A

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As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. V C 2015 International Society for Advancement of Cytometry

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Section: Discussionmentioning

confidence: 99%

“…(24) is that this analysis procedure results in summation matrices and vectors that are also described by the mean and Poisson distributions shown in Eqs. (17).…”

Section: Original Articlementioning

confidence: 99%

Probability state modeling theory

Bagwell¹,

Hunsberger²,

Herbert³

et al. 2015

Cytometry Pt A

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“…The differences in the nature of the high-dimensional distributions created by each n-parameter FCM dataset are measured using computational estimates of the Fisher information distance (a measure of the differences among probability density functions embedded on statistical manifolds). The underlying principles of FINE and information geometry are described in detail in earlier studies (7,8), and a schematic representation of the method is provided in Figure 1.…”

Section: Fisher Information Nonparametric Embedding Analysismentioning

confidence: 99%

“…N-parameter event data from flow cytometry list mode files are converted into probability density functions (PDFs); the PDFs are embedded as points on a high-dimensional virtual construct known as a statistical manifold, and the differences in information contained within the PDFs are represented as distances along the statistical manifold (Fisher information distance) using a computational estimate; finally, the high-dimensional neighborhood map on the statistical manifold is reduced to a lower dimensional plot (two or three dimensions) for visualization. Details of the FINE method are provided in references (7,8).…”

Section: Fig 1 Schematic Overview Of Fisher Information Nonparametrmentioning

confidence: 99%

“…This method, known as Fisher information nonparametric embedding (FINE), uses the principles of information geometry to determine information distances between individual high-dimensional FCM list mode files, with each file treated as a single high-dimensional distribution or object, rather than as a series of two-dimensional dotplots (7,8). A related method, information preserving component analysis (IPCA), allows direct visualization of an information-optimized two-dimensional histogram for each case, derived from all measured FCM markers based on the differences in information among different cases (9).…”

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Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling

Finn

Harrington

Carter

et al. 2011

Cytometry Part B Clinical

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Background: The role of flow cytometry (FCM) in diagnosing myelodysplastic syndromes (MDS)remains controversial, because analysis of myeloid maturation may involve subjective interpretation of sometimes subtle patterns on multiparameter FCM.Methods: Using six-parameter marker combinations known to be useful in evaluating the myeloid compartment in MDS, we measured objective immunophenotypic differences between non-neoplastic (n 5 25) and dysplastic (n 5 17) granulopoiesis using a novel method, called Fisher information nonparametric embedding (FINE), that measures information distances among FCM datasets modeled as individual high-dimensional probability density functions, rather than as sets of two-dimensional histograms. Information-preserving component analysis (IPCA) was used to create information-optimized ''rotated'' twodimensional histograms for visualizing myelopoietic immunophenotypes for each individual sample.Results: There was a consistent trend of segregation of higher-grade MDS (RAEB and RCMD) from benign by FINE analysis. This difference was accentuated in cases with morphologic dysgranulopoiesis and in cases with clonal cytogenetic abnormalities. However, lower grades of MDS or cases that lacked morphologic dysgranulopoiesis showed much greater overlap with non-neoplastic cases. Two cases of reactive left shift were consistently embedded within the higher-grade MDS group. IPCA yielded twodimensional histogram projections for each individual case by relative weighting of measured cellular characteristics, optimized for preserving information distances derived through FINE.Conclusions: Objective analysis by information geometry supports the conclusions of previous studies that there are immunophenotypic differences in the maturation patterns of benign granulopoiesis and high grade MDS, but also reinforces the known pitfalls of overlap between low-grade MDS and benign granulopoiesis and overlap between reactive granulocytic left shifts and dysplastic granulopoiesis.

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An automated analysis of highly complex flow cytometry‐based proteomic data

et al. 2011

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The combination of color-coded microspheres as carriers and flow cytometry as a detection platform provides new opportunities for multiplexed measurement of biomolecules. Here, we developed a software tool capable of automated gating of color-coded microspheres, automatic extraction of statistics from all subsets and validation, normalization, and cross-sample analysis. The approach presented in this article enabled us to harness the power of high-content cellular proteomics. In size exclusion chromatography-resolved microsphere-based affinity proteomics (Size-MAP), antibody-coupled microspheres are used to measure biotinylated proteins that have been separated by size exclusion chromatography. The captured proteins are labeled with streptavidin phycoerythrin and detected by multicolor flow cytometry. When the results from multiple size exclusion chromatography fractions are combined, binding is detected as discrete reactivity peaks (entities). The information obtained might be approximated to a multiplexed western blot. We used a microsphere set with [1,000 subsets, presenting an approach to extract biologically relevant information. The R-project environment was used to sequentially recognize subsets in two-dimensional space and gate them. The aim was to extract the median streptavidin phycoerythrin fluorescence intensity for all 1,0001 microsphere subsets from a series of 96 measured samples. The resulting text files were subjected to algorithms that identified entities across the 24 fractions. Thus, the original 24 data points for each antibody were compressed to 1-4 integrated values representing the areas of individual antibody reactivity peaks. Finally, we provide experimental data on cellular protein changes induced by treatment of leukemia cells with imatinib mesylate. The approach presented here exemplifies how large-scale flow cytometry data analysis can be efficiently processed to employ flow cytometry as a high-content proteomics method. ' 2011 International Society for Advancement of Cytometry

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Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high‐dimensional objects

Cited by 41 publications

References 18 publications

Probability state modeling theory

Probability state modeling theory

Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling

An automated analysis of highly complex flow cytometry‐based proteomic data

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