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Fig. 1 | BMC Bioinformatics

Fig. 1

From: CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

Fig. 1

Overview of CytoTree package functionalities and algorithm. The preprocessing panel reveals the preparation steps before creating the CYT object. CytoTree provided functions to extract the expression matrix through a single FSC file or multiple FSC files. Both the clean expression matrix and meta-information are required to build the CYT object. The trajectory panel shows a the summary of the CytoTree workflow in constructing the tree-shaped trajectory. When the clustering was performed using all cells, all clusters of cells were linked by MST to illustrate the differentiation relationship based on the n-dimensional hull after dimensionality reduction. The analysis panel shows the model of pseudotime estimation and intermediate state identification. Each point represents one cell. A graph is built to connect all cells based on the KNN algorithm. Cells 1 and 2 (colored in yellow) are defined as the root cells. All the shortest paths from cells 1 and 2 to other cells are calculated to estimate the pseudotime. Cell 10 shows the maximum pseudotime and is then defined as the leaf cell (colored in purple). Forward and backward walks from the root cells and leaf cells are performed based on the shortest path. Cells 4 and 7 had the highest frequencies of occurrence during the walks and are considered to be the intermediate state cells. The running example panel shows the brief R code used to complete the entire workflow of CytoTree. Functions with “optional step” annotation are not the necessary steps in the CytoTree workflow

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