Automated high-dimensional flow cytometric data analysis

Abstract

Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.finite mixture model ͉ flow cytometry ͉ multivariate skew distribution F low cytometry transformed clinical immunology and hematology over 2 decades ago by allowing the rapid interrogation of cell surface determinants and, more recently, by enabling the analysis of intracellular events using fluorophore-conjugated antibodies or markers. Although flow cytometry initially allowed the investigation of only a single fluorophore, recent advances allow close to 20 parallel channels for monitoring different determinants (1-4). These advances have now surpassed our ability to interpret manually the resulting high-dimensional data and have led to growing interest and recent activity in the development of new computational tools and approaches (5-8).The difficulty in data analysis arises from the traditional technique of identifying discrete cell populations by manual gating, which is a labor-intensive process and varies by user experience. The initial computational packages for flow cytometric analyses focused largely on different preprocessing tasks such as data acquisition, normalization, and live cell gating. Besides visualization and transformation of flow cytometric data, useful tools such as Flowjo (www.flowjo.com) and the packages in BioConductor (www.bioconductor.org) (such as prada, flowCore, flowViz, flowUtils, and rflowcyt) allow some form of software-assisted gating and extraction of populations of interest. The operator subjectively demarcates a cell population while moving through successive 2-or 3-dimensional projections of the data. This process limits the reproducibility of data processing. A more fundamental problem is that this lower dimensional visualization hinders the identification of higher-dimensional features. Furthermore, current methods extract only a limited number of sample parameters, such as the mean fluorescence intensity of a cell population, which can lead to loss of critical information in defining the properties of a cell population....

Competitive SWIFT cluster templates enhance detection of aging changes

“…In response to the increasing complexity and dimensionality of flow cytometry data, several algorithms have recently been developed to automate the identification and/or quantification of cell populations in flow cytometry data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. Some algorithms function by automating the traditional manual gating procedure for detecting and isolating specific subpopulations of interest, others attempt to completely resolve cells within a sample into clusters that ideally correspond to biologically meaningful subpopulations.…”

Mapping cell populations in flow cytometry data for cross‐sample comparison using the Friedman–Rafsky test statistic as a distance measure

“…In recent years, computational gating methods have made significant advances in identifying cell populations at the individual sample level 5. Model‐based computational gating approaches, such as Gaussian and multivariate skew‐ t mixture model fitting 6, 7, 8, 9, employ statistical assumptions on the shape and location of cell population distributions. Non‐model‐based methods, such as grid‐based density clustering 10 and spectral clustering 11 algorithms, group cells into homogeneous populations based on unsupervised data clustering.…”

“…Small intersample variations in cell population locations are associated with low population misclassification rates. Other existing approaches, including FLAME 7, HDPGMM 6, JCM 9, and flowMatch 14, bundle the cell population identification method and cross‐sample mapping function together, with the mapping component operating under the principle of global template finding. In FLAME 7, mapping cell populations across samples is the last step of their computational gating method.…”

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

“…Microsphere array suspension samples were acquired from a 96-well plate using a high throughput sampler module attached to a modified BD LSR 2 instrument (BD Biosciences, San Jose, CA 40,000 microspheres were acquired at each well, and data were exported in FCS 3.0 format from FACS Diva software (BD Biosciences) and further processed in the R-project environment as described below. Sample of the FCS data can be found at www.bioinformin.net/sample.php.…”