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Split-BioID — Proteomic Analysis of Context-specific Protein Complexes in Their Native Cellular Environment
Published on: April 20, 2018
SciBet as a portable and fast single cell type identifier
Chenwei Li1,2, Baolin Liu1,3, Boxi Kang1,2,3
1Peking-Tsinghua Center for Life Sciences, BIOPIC and School of Life Sciences, Peking University, Beijing, China.
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.
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