Automated high-dimensional flow cytometric data analysis

By Saumyadipta Pyne, Xin-li Hu, Kui Wang, E. Rossin, Tsung-I Lin, L. Maier, C. Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David A. Hafler and 2 more. Proceedings of the National Academy of Sciences of the United States of America, 2009

448 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c98308e239cf785f63ed9f96a5c4cfc27fe73867

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Citations in the corpus

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia (2013, unknown): “20 ).”
Earth Mover’s Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations (2016, background): “[2] or the bipartite matching introduced by Pyne et al [9].”
Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis (2015, unknown): “Parametric methods (Pyne et al., 2009) require strong assumptions about the high-dimensional shape of cellular populations (e.g., ellipsoid, convex), which are violated in single-cell data (Amir et al., 2013).”
Cellular heterogeneity: when do differences make a difference? (2010, methodology): “Intuitively, methods such as deterministic K-means clustering or probabilistic Gaussian mixture modeling seek to identify local, high-density regions of phenotype space (Figure 1Bii; Pyne et al., 2009; Slack et al., 2008).”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “Models based on skewed and heavy-tailed distributions (115) and spanning-tree progression analysis (116) have been proposed to identify “rare” clusters in flow cytometry data, and may also be used for clustering of image-based phenotypic profiles.”
Cytobank: providing an analytics platform for community cytometry data analysis and collaboration. (2014, methodology): “For example, a large number of papers report population ﬁnding but ignore the later steps that lead to biological outcome analysis (Lo et al. 2008; Pyne et al. 2009; Walther et al. 2009a; Qian et al. 2010; Zare et al. 2010; Aghaeepour et al. 2011; Ge and Sealfon 2012).”
Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE (2011, unknown): “Gates are either manually drawn using software such as FlowJo (http://www.treestar.com/), FlowCore [4], or automatically defined by clustering algorithms [5, 6, 7, 8, 9, 10].”