Multi-batch cytometry data integration for optimal immunophenotyping

Ogishi, M.; Yang, R.; Gruber, C.; Pelham, S.; Spaan, A. N.; Rosain, J.; Chbihi, M.; Han, J. E.; Rao, V. K.; Kainulainen, L.; Bustamante, J.; Boisson, B.; Bogunovic, D.; Boisson-Dupuis, S.; Casanova, J.-L.

doi:10.1101/2020.07.14.202432

bioRxiv · 10.1101/2020.07.14.202432

Multi-batch cytometry data integration for optimal immunophenotyping

Ogishi, M.·Yang, R.·Gruber, C.·Pelham, S.·Spaan, A. N.·Rosain, J.·Chbihi, M.·Han, J. E.·Rao, V. K.·Kainulainen, L.·Bustamante, J.·Boisson, B.·Bogunovic, D.·Boisson-Dupuis, S.·Casanova, J.-L.

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Abstract

We describe the integration of multi-batch cytometry datasets (iMUBAC), a flexible, robust, and scalable computational framework for unsupervised cell-type identification across multiple batches of high-dimensional cytometry datasets. After overlaying cells from healthy controls across multiple batches, iMUBAC learns batch-specific cell-type classification boundaries and identifies aberrant immunophenotypes in patient samples. We illustrate unbiased and streamlined immunophenotyping, using both in-house and public mass and flow cytometry datasets.

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Ogishi, M., Yang, R., Gruber, C., Pelham, S., Spaan, A. N., Rosain, J., Chbihi, M., Han, J. E., Rao, V. K., Kainulainen, L., Bustamante, J., Boisson, B., Bogunovic, D., Boisson-Dupuis, S., Casanova, J.-L.. 2020-07-15. Multi-batch cytometry data integration for optimal immunophenotyping. https://doi.org/10.1101/2020.07.14.202432

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