Author:
Song Tao,Dai Huanhuan,Wang Shuang,Wang Gan,Zhang Xudong,Zhang Ying,Jiao Linfang
Abstract
Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province
Fundamental Research Funds for the Central Universities
Subject
Genetics (clinical),Genetics,Molecular Medicine
Cited by
5 articles.
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