4.7 Article

MASI enables fast model-free standardization and integration of single-cell transcriptomics data

Journal

COMMUNICATIONS BIOLOGY
Volume 6, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s42003-023-04820-3

Keywords

-

Ask authors/readers for more resources

MASI is a computational pipeline that enables the integration and annotation of large single-cell transcriptomic datasets with limited computational resources. It outperforms other methods in terms of integration, annotation, and speed. MASI can serve as a cheap computational alternative for the single-cell research community.
MASI is a computational pipeline that enables the integration and annotation of large single-cell transcriptomic datasets with limited computational resources. Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods, in terms of integration, annotation, and speed. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. Finally, we show MASI can annotate approximately one million cells on a personal laptop, making large-scale single-cell data integration more accessible. We envision that MASI can serve as a cheap computational alternative for the single-cell research community.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available