Accurate sub-population detection and mapping across single cell experiments with PopCorn

Abstract

The identification of sub-populations of cells present in a sample, and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single cell data. Current tools for these kind of data however fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much needed tool for comparative analysis of populations of single-cells.