PT  - JOURNAL ARTICLE
AU  - Jian Hu
AU  - Xiangjie Li
AU  - Gang Hu
AU  - Yafei Lyu
AU  - Katalin Susztak
AU  - Mingyao Li
TI  - Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
AID  - 10.1101/2020.02.02.931139
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.02.02.931139
4099  - http://biorxiv.org/content/early/2020/02/03/2020.02.02.931139.short
4100  - http://biorxiv.org/content/early/2020/02/03/2020.02.02.931139.full
AB  - An important step in single-cell RNA-seq (scRNA-seq) analysis is to cluster cells into different populations or types. Here we describe ItClust, an Iterative Transfer learning algorithm with neural network for scRNA-seq Clustering. ItClust learns cell type knowledge from well-annotated source data, but also leverages information in the target data to make it less dependent on the source data quality. Through extensive evaluations using datasets from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust significantly improves clustering and cell type classification accuracy compared to popular unsupervised clustering and supervised cell type classification algorithms.