Abstract
BackgroundThe rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.ResultsHere, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.ConclusionsCytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.
Highlights
The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge
In accordance with well-established standards and practices [10, 12,13,14, 18,19,20,21], we present CytoTree, a trajectory inference, pseudotime estimation and visualization toolkit for flow and mass cytometry data
Overview of functionalities in CytoTree The CytoTree package was developed as an analysis and visualization software for flow and mass cytometry data
Summary
The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. Unlike scRNA-seq data, flow and mass cytometry can focus on a subset of cellular markers or protein expression levels, producing data without many missing values [5]. The design of an scRNA-seq data analysis workflow could provide capabilities such as trajectory inference (studying the dynamic cellular processes [8]) and pseudotime estimation (reordering cells by their biological state to recapitulate the dynamics of biological processes [9]). The methods for trajectory inference and pseudotime estimation usually involve clustering, dimensionality reduction, and topological analysis based on a cell-to-cell network [8]
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