Table 1.

Data-driven algorithms for high-dimensional analysis discussed in this review.

Method	Description	Ref.
PCA	A linear dimensionality reduction method that maps data into a new coordinate system that captures the covariation in the data	[52]
PLS-DA	A dimensionality reduction method that linearly links covarying signals to associated outcomes; can be used for classification and prediction	[53]
t-SNE	t-distributed stochastic neighbor embedding is a nonlinear dimensionality reduction method used for visualizing high-dimensional data in 2D or 3D	[10,11]
Self-organizing maps	A dimensionality reduction and unsupervised clustering method to visualize discrete populations on a map. FlowSOM is modified for flow- and mass- cytometry data.	[43,54]
SPADE	A clustering method that hierarchically orders changes in marker expression to depict groups in a minimally spanning tree	[12]
Scaffold Maps	A clustering method that visualizes cellular landscapes with pre-defined landmark populations	[17]
PhenoGraph	An unsupervised clustering method based on Louvain modularity often used to partition single-cell data into subsets	[25]
CellCnn	A clustering method based on convolutional neural networks that has been applied to detect rare cell populations associated with disease	[44]
ISOMAP	A trajectory visualization method that can be used to model cellular phenotypic progression based on the geodesic distances between cells	[16]
DREVI, DREMI	An inference method that applies conditional density estimation to visualize pairwise interactions and then calculates mutual information to score the strength of the interactions	[21]
SLIDE	An inference method that calculates differences between nearest neighbor cells to identify remodeled signaling pathways	[35]
Gaussian Graphical Modeling	A network inference method that quantifies partial correlations to define the dependence between variables.	[55]
DBScan	A clustering method that uses a density-based algorithm to discover clusters in high-dimensional space.	[56]
SC3	A clustering method that combined multiple clustering solutions to reach a consensus clustering of single-cell RNA-seq data	[57]