Trajectory Inference and Cell Fate Prediction

Advancements in single-cell RNA sequencing (scRNA-seq) technology have revolutionized the analysis of cellular development, enabling high-resolution profiling of heterogeneous cell populations. This chapter delves into the concepts of trajectory inference and cell fate prediction, essential for understanding dynamic biological processes such as development, differentiation, and cellular response to stimuli. The chapter also highlights various computational methods and software tools, with an emphasis on the role of machine learning and deep learning. Additionally, a case study on scRNA-seq data for clustering analysis, and trajectory inference has been presented. Further, the key challenges associated with trajectory inference and cell fate prediction have been discussed.