Chapter 7 Structure of CytoTree

The CYT object is according to the R S4 object. The which consists of the imput matrix data information is required to build the CYT object. When running the CytoTree::createCYT function, in the CYT object is the log-transformed matrix and is filtered according to the markers included in The is essential for the next workflow. The slots som and cluster contain clustering information and parameter of clustering module, whereas pca.sdev, pca.value, pca.score, tsne.value, dm, umap.value store the running parameters and results of dimensionality reduction module. The network built by MST is stored in network slots. The root cells and leaf cells are stored in the slots root.cells and leaf.cells and are essential for pseudotime estimation and trajectory inference. The slots knn, knn.index and knn.distance contain the KNN information calculated by the R BiocNeighbors package. And the slots walk and diff.tree contains the intermediate calculation results in the pseudotime and trajectory analysis. The meta-information for cells and clusters are stored in the slots plot.meta and tree.meta, which can be fetched by CytoTree::fetchPlotMeta and CytoTree::fetchClustMeta, respectively. All slots are constrained based on the standardized data type in R.

Table 7.1: Structure of CYT object
Function Slot Description Type
Basic Raw signal data captured from FCS matrix
Basic Data enrolled in the computational modules matrix
Basic Meta data information of the experiment, and columns of “stage” and “cell” are required data.frame
Basic markers Markers used in the calculation of PCA, tSNE, diffusion map and UMAP. vector
Basic markers.idx Index of markers vector
Basic Cell names after downsampling vector
Clustering som Store som network information calculated using FlowSOM list
Clustering cluster Store clustering information after processing cell clusters data.frame
Dimensionality Reduction pca.sdev Storing PCA information vector
Dimensionality Reduction pca.value Storing PCA information matrix
Dimensionality Reduction pca.scores Storing PCA information matrix
Dimensionality Reduction tsne.value Storing tSNE information matrix
Dimensionality Reduction dm Storing diffusion maps information S4 object
Dimensionality Reduction umap.value Storing UMAP information matrix
Pseudotime root.cells Root cells vector
Pseudotime leaf.cells Leaf cells vector
Pseudotime network Minimum spanning tree information list
Pseudotime knn Numbers of nearest neighbors numeric
Pseudotime knn.index Matrix corresponds to a point in and contains the row indices in matrix
Pseudotime knn.distance Contains the distance of nearest neighbors of each cell matrix
Intermediate state walk Walk parameters between leaf cells and root cells list
Intermediate state diff.tree Storing walking information list
Meta information plot.meta Meta information for single cells in visualization data.frame
Meta information tree.meta Meta information for clusters in visualization list