easyMF: a Web Platform for Matrix Factorization-based Gene Discovery from Large-scale Transcriptome Data

Introduction

• easyMF is a flexible and easy-to-use framework that can facilitate gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms.
• easyMF comprises three functional modules, named Matrix Preparation, Matrix Factorization, and Deep Mining. The Matrix Preparation module can perform matrix generation from raw RNA-Seq reads; the Matrix Factorization module can perform decompose high-dimensional gene expression matrix into low-dimensional matrices (e.g., the amplitude matrix [AM] and the pattern matrix [PM]); the Deep Mining module can perform gene discovery from AM and PM.
• easyMF is equipped with the Galaxy system, allowing users to perform accessible, reproducible, collaborative and transparent analyses of large-scale transcriptome data.
• easyMF is also powered with an advanced packaging technology, which enables compatibility and portability.
• easyMF project is hosted on https://github.com/cma2015/easyMF, easyMF docker image is available at https://hub.docker.com/r/malab/easymf, A demo easyMF server can be accessed via http://easymf.omicstudio.cloud.

How to use easyMF

• Test data and tutorial of easyMF are presented at http://easymf.omicstudio.cloud.

News and updates

Dec 1, 2020

• Web server and Docker image of easyMF were released for the first time.

Oct 18, 2021

• Web server and Docker image of easyMF were ahjusted for the second time

How to cite easyMF

• Wenlong Ma, Siyuan Chen, Yuhong Qi, Minggui Song, Jingjing Zhai, Ting Zhang, Shang Xie, Guifeng Wang, Chuang Ma, easyMF: a Web Platform for Matrix Factorization-based Gene Discovery from Large-scale Transcriptome Data. Interdisciplinary Sciences: Computational Life Sciences, 2022, doi: 10.1007/s12539-022-00522-2

How to access help

• For comments/suggestions/bug reports, please leave a message at Github issues, or contact: Wenlong Ma [email protected] or Siyuan Chen [email protected]