Comparative analysis of annotation tools in grouping cell types from single cell RNA sequencing data

Author(s): Meghana Kshirsagar,Gauri Vaidya

Affiliation(s): University of Limerick

Social media: https://www.linkedin.com/in/meghana-kshirsagar-0843415/

The identification of differentially expressed immune related genes in immune cells and subtypes holds huge potential in predicting prognosis and survival of immunotherapy treatment outcomes. Gene expression profiling can help to identify the patterns of genes expressed in major immune cells amongst cohorts of patients at different stages of cancer which can generate new biological hypotheses. There exists a number of platforms and libraries that allow investigation of gene regulation in cell populations such as 10x Genomics, Seurat Workflow etc. Various prognostic models such as tumor progression , survival analysis can be designed using machine learning approaches from gene signature patterns. However, the accuracy of the models largely depend on the tools used for cell annotation. This research makes a comparative analysis of the popular annotation tools in grouping cell populations and validates the tools on the biomarkers identified in different cell populations.