23 papers · ranked by Valyu relevance
Zuguang Gu
Heatmap is a widely used statistical visualization method on matrix-like data to reveal similar patterns shared by subsets of rows and columns. In the R programming language, there are many packages that make heatmaps. Among them, the ComplexHeatmap package provides the richest toolset for constructing highly…
Wankun Deng, Yongbo Wang, Zexian Liu, Han Cheng + 2 more
'Zhang Zhang'] Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under…
Zuguang Gu, Roland Eils, Matthias Schlesner, Naveed Ishaque
The normalized matrix is essentially a normal matrix with extra enrichment parameters attached. EnrichedHeatmap inherits and extends the ComplexHeatmap package, thus it provides great flexibility to arrange heatmaps as well as complex annotations, which is unique compared to other tools. On top of enriched heatmaps is…
Bohdan B. Khomtchouk, James R. Hennessy, Claes Wahlestedt, Chun-Hsi Huang
shinyheatmap is hosted online as an R Shiny web server application. shinyheatmap may also be run locally from within R Studio, as shown here: [https://github.com/Bohdan-Khomtchouk/shinyheatmap](). shinyheatmap leverages the cumulative utility of R’s heatmaply , shiny , data.table , and gplots libraries to create a…
Bohdan B. Khomtchouk, Claes Wahlestedt
Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since…
Bohdan B. Khomtchouk, Vytas Dargis-Robinson, James R. Hennessy, Claes Wahlestedt
Large gene expression heatmaps (≥ 200 genes) are difficult to read: while general expression level patterns may be observed via hierarchical clustering, the identities of specific gene names become entirely unreadable at large scale. More importantly, current state-of-the-art heatmaps are entirely static, require…
Bohdan B Khomtchouk, Derek J Van Booven, Claes Wahlestedt
Background The graphical visualization of gene expression data using heatmaps has become an integral component of modern-day medical research. Heatmaps are used extensively to plot quantitative differences in gene expression levels, such as those measured with RNAseq and microarray experiments, to provide qualitative…
Joshua R. Williams, Ruoting Yang, John L. Clifford, Daniel Watson + 5 more
'Ross Campbell' 'Derese Getnet' 'Raina Kumar' 'Rasha Hammamieh' 'Marti Jett'] Background Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective…
Eugénie Lohmann, Laurent Gorvel, Samuel Granjeaud
High-content cytometry is an important technique in clinical research, producing data that is rich and complex to analyze. The resulting clusters of cells must be profiled to determine their role and function, and analyzed to identify changes in cell frequency or marker intensity. The heat map is the most suitable tool…
Yan Liu, Melissa Allen‐Dumas
Heatmap is a common geovisualization method that interpolates and visualizes a set of point observations on a map surface. Most of online web mapping libraries implement a one-pass heatmap algorithm using HTML5 canvas or WebGL for efficient heatmap generation. However, such implementation applies additive operations…
Zuguang Gu, Daniel Hübschmann
Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In R, there are many packages that make heatmaps. Among them, ComplexHeatmap provides rich tools for constructing highly customizable heatmaps. It can easily establish connections between…
Daniel Fried, Stephen Kobourov
We describe a practical approach for visual exploration of research papers. Specifically, we use the titles of papers from the DBLP database to create what we call maps of computer science (MoCS). Words and phrases from the paper titles are the cities in the map, and countries are created based on word and phrase…
Rebecca Barter, Bin Yu
The technological advancements of the modern era have enabled the collection of huge amounts of data in science and beyond. Extracting useful information from such massive datasets is an ongoing challenge as traditional data visualization tools typically do not scale well in high-dimensional settings. An existing…
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach + 1 more
'Klaus‐Robert Müller'] Abstract—Deep Neural Networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multi-layer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at…
Trang T. Le, Jason H. Moore
treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the…
Udo Schlegel, Daniel A. Keim
Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In many cases, heatmaps visualize such attributions for samples, for instance, on images. However, heatmaps are not always the ideal…
Eckehard Hermann, Harald Lampesberger
—Traditional two-dimensional risk matrices (heatmaps) are widely used to model and visualize likelihood and impact relationships, but they face fundamental methodological limitations when applied to complex infrastructures. In particular, regulatory frameworks such as NIS2 and DORA call for more context-sensitive and…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann + 7 more
Chemical reaction optimization (RO) is an iterative process that results in large and high-dimensional datasets. Current tools only allow for limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using…
Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. van der Hooft
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot…