Trajectory inference

Typically, the steps in the algorithm consist of dimensionality reduction to reduce the complexity of the data, trajectory building to determine the structure of the dynamic process, and projection of the data onto the trajectory so that cells are positioned by their development through the process and cells with similar expression profiles are situated near each other.

[6] Trajectory inference algorithms differ in the specific procedure used for dimensionality reduction, the kinds of structures that can be used to represent the dynamic process, and the prior information that is required or can be provided.

[6] The creation of the trajectory graph can be accomplished using k-nearest neighbors or minimum spanning tree algorithms.

[10] MARGARET employs a deep unsupervised metric learning approach for inferring the cellular latent space and cell clusters.

[11] Monocle first employs a differential expression test to reduce the number of genes then applies independent component analysis for additional dimensionality reduction.

[10] TSCAN performs dimensionality reduction using principal component analysis and clusters cells using a mixture model.

[14] Wanderlust was developed for analysis of mass cytometry data, but has been adapted for single-cell transcriptomics applications.

Wishbone combines principal component analysis and diffusion maps to achieve dimensionality reduction then also creates a KNN graph.

[16] Waterfall performs dimensionality reduction via principal component analysis and uses a k-means algorithm to find cell clusters.