Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE

By Peng Qiu, Erin F. Simonds, Sean Bendall, Kenneth D. Gibbs, Robert V. Bruggner, Michael D. Linderman, K. Sachs, Garry P. Nolan, Sylvia K. Plevritis. Nature Biotechnology, 2011

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The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/edea67fb446168191489913ca732e8cab12a7fbf

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Citations in the corpus

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.”
Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data (2015, background): “The rarity of Ag-specific subsets, (i.e., 0.1% of total T cells), renders typical dimension reduction and visualization tools ineffective (14,15).”
Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B cell Development (2014, background): “Previously, it has been shown that the transitional co-occurrence of an extended suite of phenotypic markers, measured simultaneously in individual cells, can be used to roughly order cells along a developmental hierarchy (Bendall et al., 2011; Qiu et al., 2011; Amir et al., 2013).”
Multivariate Computational Analysis of Gamma Delta T Cell Inhibitory Receptor Signatures Reveals the Divergence of Healthy and ART-Suppressed HIV+ Aging (2018, background): “(64) and γδ T cell IR median expression was assessed (LAG3 not shown due to negligible results), confirming our manual gating findings (data not shown) and allowing visualization of IR expression from individual subjects (Figure 2D).”
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016, methodology): “Several computational methods have been proposed to analyze single-cell genomic data such as single-cell mass cytometry data (13–15) and single-cell gene expression data (8,16–19).”
A pipeline for multidimensional confocal analysis of mitochondrial morphology, function and dynamics in pancreatic β-cells (2019, methodology): “Unsupervised Categorization of Mitochondrial Morphology using SPADE Spanning-tree Progression Analysis of Density-normalized Events (SPADE v3.”
Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data (2016, neutral): “…FLOCK, and Rclusterpp performed well for the data sets with multiple cell populations of interest; and X-shift, FlowSOM, FlowSOM_pre, Rclusterpp, immunoClust, SWIFT, and PhenoGraph performed well for the data sets with a single rare cell population of interest (see Table 3 for full results).”
Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment (2020, neutral): “For semi-automated gating of populations of interest, high-resolution SPADE clustering was conducted on all indicated asinh-transformed markers (Table S2) with 400 target nodes (Qiu et al., 2011).”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “Spade [11], StemID 2 [13], Eclair [12], TSCAN [32] and Mpath [33] use different clustering algorithms such as k-means, k-medoids, hierarchical clustering or DBSCAN in a dimensionality-reduced space.”
Revisiting Cardiac Cellular Composition (2015, methodology): “To better define cell populations represented by this antibody panel (Figure 2B), we conducted high-dimensional analysis of our flow cytometry data using the SPADE algorithm.(27,28) SPADE uses agglomerative clustering to identify groups of cells (referred to as nodes) that are phenotypically similar”
Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer. (2018, methodology): “We evaluated the redundancy between the different markers by repeating the CytoSPADE analysis in absence of each of the six markers (Figure S1A).”