Cəfər İsbarov, Elmir Məhəmmədov
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.