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

Yang XuRafael KramannRachel Patton McCordSikander Hayat
Published in: Communications biology (2023)
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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • electronic health record
  • big data
  • induced apoptosis
  • machine learning
  • oxidative stress
  • cell proliferation
  • cell death
  • cell cycle arrest