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Interactive single-cell data analysis using Cellar

Author

Listed:
  • Euxhen Hasanaj

    (Carnegie Mellon University)

  • Jingtao Wang

    (McGill University Health Centre)

  • Arjun Sarathi

    (Carnegie Mellon University)

  • Jun Ding

    (McGill University Health Centre)

  • Ziv Bar-Joseph

    (Carnegie Mellon University
    Carnegie Mellon University)

Abstract

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, we developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. We 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. We 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.

Suggested Citation

  • Euxhen Hasanaj & Jingtao Wang & Arjun Sarathi & Jun Ding & Ziv Bar-Joseph, 2022. "Interactive single-cell data analysis using Cellar," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29744-0
    DOI: 10.1038/s41467-022-29744-0
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