title	author	output
MOVICS: <br>Multi-Omics integration and VIsualization in Cancer Subtyping	name affiliation email <b>Xiaofan Lu</b> <b>State Key Laboratory of Natural Medicines, <br>Research Center of Biostatistics and Computational Pharmacy, <br>China Pharmaceutical University, Nanjing, China</b> <b>[email protected]</b>	prettydoc::html_pretty theme highlight tactile github

Introduction

The goal of MOVICS is to provide a unified interface for 10 state-of-the-art multi-omics clustering algorithms, and standardizes the output for each algorithm so as to form a pipeline for downstream analyses. For cancer subtyping studies, MOVICS forms a pipeline for most commonly used downstream analyses in cancer subtyping researches and to create feature rich customizable visualzations with minimal effort.

Installation

if (!require("remotes")) {
  install.packages("remotes")
} 
remotes::install_github("xlucpu/MOVICS")

Guidance

A detailed guide of how to use MOVICS could be find in the HTML vignette. If you have any questions, bug reports, or suggestions for improving MOVICS, please email them to [email protected].

Citation

If you use MOVICS R package in published research, please cite:

• HAVE NOT BEEN PUBLISHED

Acknowledgement

I would like to express my gratitude to Dr. Morgane Pierre-Jean for the inspiration brought by the study of evaluating unsupervised methods for multi-omics data integration. I also want to thank Dr. Enyu Lin for the helping in calculation and visualization of fraction genome altered, and to thank Dr. Rongfang Shen for the assistance in visualization of Transitions and Transversions. At last, sincere thanks to the brilliant contributors of all the functions incorporated in MOVICS package.