ICGNI: ICA-based Clustering with GBDT Network Inference Using Single-cell Expression Data

Biological network inference has always been one of the central topics in systems biology. Network inference can be regarded as a process of determining relations between nodes with efficient measurements. For gene regulatory networks, transcriptomic data such as single cell RNA sequencing (sc RNA-seq) have increasingly act as the main information source in reconstructing network structures. Although many methods have been proposed towards this challenge, most of them do not focus on sing-cell data and omit the characteristics of gene regulatory networks. Here, we presented a new method names ICGNI to solve these problems about gene functional clustering, network inference with single-cell data and hub genes finding. Three single cell datasets were used to evaluate the performance of our method with satisfying results.