Comparison of algorithms used in single-cell transcriptomic data analysis

TL;DR

This paper compares various algorithms used in single-cell transcriptomic data analysis, highlighting their differences, effectiveness, and providing recommendations for algorithm selection at different analysis stages.

Contribution

It offers a systematic comparison of widely-used algorithms in single-cell analysis and suggests minimal sets of algorithms for each workflow step based on dataset testing.

Findings

Identified key differences between algorithms in various analysis stages

Provided recommendations for algorithm choices based on dataset characteristics

Highlighted stages where algorithm choice is highly context-dependent

Abstract

Single-cell analysis is an increasingly relevant approach in "omics'' studies. In the last decade, it has been applied to various fields, including cancer biology, neuroscience, and, especially, developmental biology. This rise in popularity has been accompanied with creation of modern software, development of new pipelines and design of new algorithms. Many established algorithms have also been applied with varying levels of effectiveness. Currently, there is an abundance of algorithms for all steps of the general workflow. While some scientists use ready-made pipelines (such as Seurat), manual analysis is popular, too, as it allows more flexibility. Scientists who perform their own analysis face multiple options when it comes to the choice of algorithms. We have used two different datasets to test some of the most widely-used algorithms. In this paper, we are going to report the main differences between them, suggest a minimal number of algorithms for each step, and explain our suggestions. In certain stages, it is impossible to make a clear choice without further context. In these cases, we are going to explore the major possibilities, and make suggestions for each one of them.