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Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profiles of single cell by using marker genes databases, correlating expression data, transferring levels by supervised classification. In this SLR, we present a comprehensive evaluation of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.

Transcriptomics and lineage tracing have enabled the development of computational methods for determining developmental trajectories, allowing for high-resolution examination of cellular dynamics. Since 2014, numerous methodologies have been proposed in the field of pseudotime estimation. Although these algorithms are potent, they are still in their infancy, and consideration must be taken when employing their strengths and weaknesses. This paper describes prominent computational approaches in depth for pseudotime estimation based on the information they utilize, as well as describing future challenges and opportunities for the development of new methods.&nbsp