Trajectory inference in single cell data: A systematic literature review

Abstract

Recent advances in single-cell transcriptomics have made it possible to explore the dynamic mechanisms of immunology in a high-throughput and objective manner. Unsupervised trajectory inference methods attempt to automatically reconstruct the developmental path cells are following by using a mixture of cells at various stages of development. In the past few years, there have been a multitude of new techniques for deducing the trajectory of a single cell from its data. This paper proposes that new researchers might focus on these criteria by examining the strategies and challenges of existing methodologies. Using specific databases (Scopus, Google Scholar and IEEE Xplore), these single cell data trajectory inference studies from 2016 to 2022 were reviewed. We have adhered to the PRISMA structure. Three databases and the most recent works on trajectory inference have been selected. The majority of studies compared their results to those of previously established methods. Several challenges were identified. Additionally, we attempted to identify the most recent work strategies. This may aid future researchers in locating suitable strategies.