Open AccessIterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
Author(s) -
Jian Hu,
Xiangjie Li,
Gang Hu,
Yafei Lyu,
Katalin Suszták,
Mingyao Li
Publication year - 2020
Publication title -
nature machine intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.894
H-Index - 16
ISSN - 2522-5839
DOI - 10.1038/s42256-020-00233-7
Subject(s) - cluster analysis , computer science , transfer of learning , artificial intelligence , machine learning , data mining , unsupervised learning , supervised learning , biclustering , pattern recognition (psychology) , artificial neural network , correlation clustering , canopy clustering algorithm
Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell type classification methods that utilize external well-annotated source data start to gain popularity over unsupervised clustering algorithms. However, the performance of existing supervised methods is highly dependent on source data quality, and they often have limited accuracy to classify cell types that are missing in the source data. To overcome these limitations, we developed ItClust, a transfer learning algorithm that borrows idea from supervised cell type classification algorithms, but also leverages information in target data to ensure sensitivity in classifying cells that are only present in the target data. Through extensive evaluations using data from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust significantly improves clustering and cell type classification accuracy over popular unsupervised clustering and supervised cell type classification algorithms.