LotOfCells: data visualization and statistics of single cell metadata

Abstract

AbstractSingle-cell sequencing unveils a treasure trove into the biological and molecular characteristics of samples. Yet, within this flood of data, the challenge to draw meaningful conclusions sometimes can be time consuming and a tortuous process.Here we introduce LotOfCells: a simple R package designed to explore the intricate landscape of phenotypic data within single-cell studies. Normally, we are interested in visualizing and measuring if the differences in the proportion of number of cells across various covariates is significant or biologically relevant. As an example, one of the most common questions is the proportion of different cell types across conditions in our experiment, or the cluster composition before and after treatment (e.g.: difference in cell type proportions between wild type and mutant). LotOfCells helps with the interpretation and visualization of meta-data of these recurrent scenarios, including the test of proportion changes across multiple ordered stages. Additionally, it computes a symmetric divergence score to measure global deregulation of cell proportions due to a condition.Code repositoryR package, manual and relevant examples can be accessed on the GitHub repository:https://github.com/OscarGVelasco/LotOfCells