Trajectory inference methods analyze single-cell RNA sequencing (scRNA-seq) data to construct a "trajectory" that represents the progression of cells through different states. The process generally involves:
Data Preprocessing: Filtering and normalizing the raw sequencing data. Dimensionality Reduction: Reducing the high-dimensional data to a lower-dimensional space for easier visualization and analysis. Clustering: Grouping cells with similar expression profiles. Trajectory Construction: Using algorithms to infer the most likely paths that cells take through different states.
