The following study was conducted by Scientists from Peking-Tsinghua Center for Life Sciences, BIOPIC and School of Life Sciences, Peking University, Beijing, China; Analytical Biosciences Limited, Beijing, China; Beijing Advanced Innovation Centre for Genomics, Peking University, Beijing, China; Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, USA. Study is published in Nature Communications Journal as detailed below.
Nature Communications; Volume 11, Article Number: 1818; (2020)
SciBet as a Portable and Fast Single Cell Type Identifier
Abstract
Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell type identification.
Source:
Nature Communications
URL: https://www.nature.com/articles/s41467-020-15523-2
Citation:
Li, C., B. Liu, et al. (2020). “SciBet as a portable and fast single cell type identifier.” Nature Communications 11(1): 1818.
