2018
DOI: 10.1038/s42003-018-0091-x
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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… Show more

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Cited by 14 publications
(25 citation statements)
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“…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 .…”
Section: Methodsmentioning
confidence: 99%
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“…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 .…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
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confidence: 99%
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