PipeOne integrates multi-modal data from RNA-seq to identify disease features and clinically-relevant subtypes
RNA sequencing (RNA-seq) represents one of the most widely-used technologies to investigate the transcriptome, and various algorithms have been developed to analyze RNA-seq data. However, a workflow integrating multi-modal information from these algorithms to study sequenced samples comprehensively is still lacking. Here, we present PipeOne, a cross-platform one-stop analysis workflow for a large number of RNA-seq samples. It includes three modules, data preprocessing and feature matrices construction by combining eight RNA analysis tools, disease feature prioritization by machine learning, and disease subtyping by clustering and survival analysis. PipeOne can be easily applied to other cancer types and complex diseases and extended to analyzing other types of high-throughput data. PipeOne is freely available at https://github.com/nongbaoting/PipeOne.
For detailed documentation, please check https://pipeone.readthedocs.io/en/latest/
If your project have used PipeOne please cite: Nong Baoting, Guo Mengbiao, Wang Weiwen, Songyang Zhou, Xiong Yuanyan: Comprehensive Analysis of Large-Scale Transcriptomes from Multiple Cancer Types. Genes 2021, 12(12):1865.