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• W3123778052 abstract "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 tool’s ( 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." @default.
• W3123778052 created "2021-02-01" @default.
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• W3123778052 date "2021-01-20" @default.
• W3123778052 modified "2023-10-14" @default.
• W3123778052 title "DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets" @default.
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• W3123778052 doi "https://doi.org/10.1101/2021.01.20.427346" @default.
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