Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and modalities, Carnegie Mellon University researchers developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. The researchers discuss the different methods implemented by Cellar, how these can be used with different data types, how to combine complementary data types and how to analyze and visualize spatial data. They demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is open-source and includes several annotated HuBMAP datasets.
a–c Preprocessing (optional). Cellar can filter cells based on the number of expressed genes, and genes which are rarely expressed. Next the input is normalized. d, e Dimensionality reduction and visualization. Several methods for dimensionality reduction are implemented as part of Cellar. The reduced data is then visualized by running another (possibly the same) dimensionality reduction method. f–i Clustering. Cellar supports several unsupervised and semi-supervised clustering methods. It also implements supervised label transfer methods. j–l Cell-type assignment. Cellar enables the use of several functional annotation databases for the assignment of cell types.
Availability – Code is available from the GitHub repository: https://github.com/euxhenh/cellar/.