malab/tamf repository overview

TAMF is designed to provide an easily accessible large-scale transcriptomic data analysis platform to help botanists even with little bioinformatics background completing complicated processing with terminal-based applications that are not user-friendly. It smoothly integrated the TAMF tools into Galaxy scientific analysis platform to provide web-based, easy-to-use and thoroughly tested tools enable users perfom comparative analysis. By integrating three major matrix factorization algorithmes (PCA [principle component analysis]; ICA [independent component analysis]; NMF [non-negative matrix factorization]), TAMF enables users perform a series of analysis based on pattern matrix (PM) and amplitude matrix (AM). For PM-based deep mining, four sub-modules are implemented to perform Clustering analysis (automatically), single-cell analysis (identify cell-types in single-cell RNA-Seq), Spatial-course analysis (illuminate the spatial dependency of sampled regions/voxels), and Time-course analysis (track the gene expression dynamics across temporal variations along the developmental stages); For AM-based deep mining, Functional gene discovery and Pathway activity analysis are provided to perform gene function prediction, gain biological insights into GWAS result, as well as active pathways, respectively. The TAMF project is hosted on GitHub (https://github.com/cma2015/TAMF⁠). In addition, in order to enable large-scale analysis, we also provided a standardized Docker image: TAMF Docker image⁠.