Analysis of transcriptome of single-cell RNA sequencing data using machine learning.

Single-cell ribonucleic acid (RNA) sequencing technology is used to analyze transcriptomes of each cell individually and helps to identify rare cell populations. By using traditional applications, it is difficult to understand and analyze the transcriptomic profiles of cells at the single-cell level. So, to overcome these kinds of issues, machine learning technologies are playing a great role. In this paper, we analyzed single-cell RNA seq data by implementing linear dimensional reduction, identifying highly variable features, clustering the cells, nonlinear dimensional reduction, and identifying gene markers. This type of single-cell RNA sequencing analysis is much needed in identifying transcriptomic profile challenges in cells and heterogeneous characteristics. Our study helps researchers who are doing fundamental research in the field of bioinformatics and computational biology concerning single-cell RNA sequencing data. [ABSTRACT FROM AUTHOR]