A Shiny Application for Modeling and Visualizing Data from High-Throughput Experiments
A Shiny Application for Modeling and Visualization of Large-Scale Biological Data
by Dijun Chen1,2, Liangyu Fu1, Dahui Hu3, Christian Klukas2, Ming Chen3 and Kerstin Kaufmann1
1 Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany.
2 Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466 Gatersleben, Germany.
3 Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
HTPmod is an interactive web tool for modeling and visualization of large-scale datasets from high-throughput plant phenotyping (HTP) or high-throughput sequencing (HTS) experiments. It consists of three function modules.
growModwas developed for plant growth modeling based on time-series data, e.g., from HTP experiments.
predModwas implemented with several prediction models that can be used to understand which among the independent measurements are related to the desirable target trait, and to dissect the forms of these relationships. For example, such prediction models have been widely used to model the contribution of chromatin features to the change of gene expression[2-7], and to predict plant biomass from image-derived features[8-12].
htpdViswas developed to explore and visualize high-dimensional data using machine learning approaches such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), self-organizing map (SOM), multidimensional scaling (MDS), K-means clustering (K-MC) or hierarchical cluster analysis (HCA) with heatmap.
To get started, select
htpdVisand read the instructions .