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bioRxiv · 10.1101/2021.01.20.427346

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

Wilson, S. B.·Howden, S. E.·Vanslambrouck, J. M.·Dorison, A.·Alquicira-Hernandez, J.·Powell, J. E.·Little, M. H.
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Abstract

Kidney organoids provide a valuable resource to understand kidney development and disease. Clustering algorithms and marker genes fail to accurately and robustly classify cellular identity between human pluripotent stem cell (hPSC)-derived organoid datasets. Here we present a new method able to accurately classify kidney cell subtypes, a hierarchical machine learning model trained using comprehensive reference data from single cell RNA-sequencing of human fetal kidney (HFK). We demonstrate the tools (DevKidCC) performance by application to all published kidney organoid datasets and a novel dataset. DevKidCC is available on Github and can be used on any kidney single cell RNA-sequence data.

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Wilson, S. B., Howden, S. E., Vanslambrouck, J. M., Dorison, A., Alquicira-Hernandez, J., Powell, J. E., Little, M. H.. 2021-01-20. DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets. https://doi.org/10.1101/2021.01.20.427346

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