SUMMARY: We developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics statistical analysis results and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customizable options and generates both interactive and publication-ready plots to uncover biological insights from data. The modular design makes the tool extensible with ease. AVAILABILITY AND IMPLEMENTATION: Researchers can experience the functionalities with their own data or demo RNA-Seq and proteomics datasets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/Quickomics for local installation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural network. Then linear CCA is performed using the transformed nonlinear data sets, which corresponds to make nonlinear CCA of the original data. A modified cost function of the neural network with Lagrange multipliers and a joint learning rule based on gradient ascent algorithm which maximizes the correlation coefficient of the network outputs is used to extract the maximal correlation pattern between the input and output of a prediction model. Finally, a regression model is constructed to implement the prediction problem. The performance of RBFCCA prediction algorithm is demonstrated via the prediction problem of Lorenz time series and some practical observed time series. The results compared with the traditional neural network method and the KCCA method indicate that the RBFCCA algorithm proposed in this paper is able to capture the dynamics of complex systems and give reliable prediction accuracy.