An R‐Derived FlowSOM Process to Analyze Unsupervised Clustering of Normal and Malignant Human Bone Marrow Classical Flow Cytometry Data

Abstract

Multiparameter flow cytometry (MFC) is a powerful and versatile tool to accurately analyze cell subsets, notably to explore normal and pathological hematopoiesis. Yet, mostly supervised subjective strategies are used to identify cell subsets in this complex tissue. In the past few years, the implementation of mass cytometry and the big data generated have led to a blossoming of new software solutions. Their application to classical MFC in hematology is however still seldom reported. Here, we show how one of these new tools, the FlowSOM R solution, can be applied, together with the Kaluza ® software, to a new delineation of hematopoietic subsets in normal human bone marrow (BM). We thus combined the unsupervised discrimination of cell subsets provided by FlowSOM and their expert-driven node-by-node assignment to known or new hematopoietic subsets. We also show how this new tool could modify the MFC exploration of hematological malignancies both at diagnosis (Dg) and follow-up (FU). This can be achieved by direct comparison of merged listmodes of reference normal BM, Dg, and FU samples of a representative acute myeloblastic case tested with the same immunophenotyping panel. This provides an immediate unsupervised evaluation of minimal residual disease.

Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia

“…Simultaneously, the ELN mentioned that the use of new software, based on non-supervised analysis, should be considered in order to reduce interpretation subjectivity. Initially used for mass-cytometry data, such new algorithms are based on a reduction of the number of dimensions (principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), visualizing data using t-SNE (viSNE)) or on clustering methods (Spanning-tree Progression Analysis of Density-normalized Events (SPADE), Citrus, PhenoGraph, Flow-Self Organizing Maps (FlowSOM)) [11][12][13][14][15][16][17][18][19]. Although most of them can be adapted to treat classical MFC data, these different solutions are not well adapted to MRD analyses: the number of events to process needs to be quite high to obtain a good sensitivity and software-based analysis time can be very long, up to several hours and thus incompatible with daily routine.…”

“…We have previously explored and published [13,15] how FlowSOM can be integrated with such classical analysis software as Kaluza ® . In this work, we aimed at appreciating the performance of the FlowSOM solution together with Kaluza ® in real-life for MRD assessment.…”

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

“…Moreover, computational methods have been used on existing clinical flow cytometric data to improve diagnostic accuracy to distinguish mantle cell lymphoma from small lymphocytic lymphoma [80], or discriminate various subpopulations of blood cells in the context of B-chronic lymphocytic leukemia [81]. Complex data sets generated by multi-parametric flow cytometry have been analyzed with automatic tools for characterizing myeloid and lymphoid cells in steady state [82][83][84], during the differentiation process [85,86], and in pathological conditions [87][88][89][90][91][92][93].…”

Flow Cytometric Analyses of Lymphocyte Markers in Immune Oncology: A Comprehensive Guidance for Validation Practice According to Laws and Standards

“…This includes the requirement for samples, the fact that cell characterization requires multiple parameters which can be evaluated in different combination and the high number of interacting variables in each experiment. This will become even more complicated in future when high-parameter research methods such as clustering become routine (26). There are many different clustering algorithms for evaluation of cytometry results.…”