PT - JOURNAL ARTICLE AU - Peng, Minshi AU - Wamsley, Brie AU - Elkins, Andrew AU - Geschwind, Daniel M AU - Wei, Yuting AU - Roeder, Kathryn TI - Cell Type Hierarchy Reconstruction via Reconciliation of Multi-resolution Cluster Tree AID - 10.1101/2021.02.06.430067 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.02.06.430067 4099 - http://biorxiv.org/content/early/2021/02/08/2021.02.06.430067.short 4100 - http://biorxiv.org/content/early/2021/02/08/2021.02.06.430067.full AB - A wealth of clustering algorithms are available for Single-cell RNA sequencing (scRNA-seq), but it remains challenging to compare and characterize the features across different scales of resolution. To resolve this challenge Multi-resolution Reconciled Tree (MRtree), builds a hierarchical tree structure based on multi-resolution partitions that is highly flexible and can be coupled with most scRNA-seq clustering algorithms. MRtree out-performs bottom-up or divisive hierarchical clustering approaches because it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. Application to fetal brain cells yields insight into subtypes of cells that can be reliably estimated.Competing Interest StatementThe authors have declared no competing interest.