The HTPmod Shiny application enables modeling and visualization of large-scale biological data

Abstract

The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.

Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana

“…Lasso regression was performed using the glmnet package in R to calculate the direct contribution (i.e., the parameter β ) of each TF to the expression change. The optimal model was chosen by five repeats of ten-fold cross-validation using the caret package in R. The above analysis was performed via the Shiny application HTPmod 80 .…”

“…If not specified, all statistical analyses and data visualization were done in R. t-SNE (t-distributed stochastic neighbor embedding) analysis was performed by the Rtsne library and the output was visualized by our HTPmod online tool 80 ( https://www.epiplant.hu-berlin.de/shiny/app/HTPmod/ ). Hive plots were generated using the HiveR library.…”

MVApp—Multivariate Analysis Application for Streamlined Data Analysis and Curation

“…The MVApp provides a flexible analytical pipeline, which can deal with multiple data structures and accommodates multiple independent variables, such as treatment, genotype, and time points, which can be used to subset or group the data for individual analyses. The existing tools, such as the software HTPmod (Chen et al, 2018), recognize the need for visualization and modeling of the high-throughput phenotyping data but do not offer opportunities for future extensions. MVApp uniquely strives to make a first step toward a future framework for standardizing data curation, analysis processing, and visualization of diverse datasets, with community input in this process being clearly invaluable.…”

“…Such optimization of data processing tools and protocols will not only accelerate and standardize data exploration and visualization but will also promote better reproducibility and transparency of data curation and analysis. Tools for high-throughput phenotyping, like HTPmod (Chen et al, 2018), are gradually providing easier access to the tools for data analysis, but integrating the community input, keeping the code open-source, and keeping those tools up-to-date is crucial for sustainable and transparent data analysis.…”

“…We developed MVApp in "R" using the "Shiny" framework (Chang et al, 2017), exploiting R's computational potential by integrating various packages into a graphical user-interface. Examples of previously developed Shiny applications are abundant, including: box plot generators (Spitzer et al, 2014), RNA sequencing data analysis pipelines (Li and Andrade, 2017;Nelson et al, 2017), genomic prediction simulators (Morota, 2017), growth modeling (Chen et al, 2018), and tools for teaching plant breeding (Matias et al, 2018) or business analytics (Nijs, 2018). MVApp not only combines the standard methods for multivariate analysis but also helps with data interpretation.…”