Scholarlink

<p>Multimodal visualizations are essential for identifying and interpreting complex relationships in diverse, high-dimensional biological datasets. However, existing visualization tools often lack native capabilities for embedding explicit statistical and computational annotations, hindering effective quantitative interpretation. We introduce MultiModalGraphics, an R package designed specifically for creating annotated scatterplots and heatmaps of multi-omics and high-dimensional biological data. The package allows seamless embedding of statistical summaries such as fold-changes, <i>p</i>-values, q-values, and standard deviations, facilitating direct quantitative comparisons. MultiModalGraphics interoperates with Bioconductor packages including MultiAssayExperiment, limma, voom, and iClusterPlus, streamlining workflows from data preprocessing and differential expression analysis to visualization. Case studies on three distinct real-world multimodal datasets illustrate its practical utility. Source code, documentation, and example datasets are available via GitHub (<a href="https://github.com/famanalytics0/MultiModalGraphics">https://github.com/famanalytics0/MultiModalGraphics</a>) and under review for inclusion into Bioconductor.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MultiModalGraphics: an R package for graphical integration of multi-omics datasets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

查看全文
  • Foziya Ahmed Mohammed,
  • El Hadj Malick Fall,
  • Kula Kekeba Tune,
  • Rasha Hammamieh,
  • Marti Jett,
  • Seid Muhie

摘要

Multimodal visualizations are essential for identifying and interpreting complex relationships in diverse, high-dimensional biological datasets. However, existing visualization tools often lack native capabilities for embedding explicit statistical and computational annotations, hindering effective quantitative interpretation. We introduce MultiModalGraphics, an R package designed specifically for creating annotated scatterplots and heatmaps of multi-omics and high-dimensional biological data. The package allows seamless embedding of statistical summaries such as fold-changes, p-values, q-values, and standard deviations, facilitating direct quantitative comparisons. MultiModalGraphics interoperates with Bioconductor packages including MultiAssayExperiment, limma, voom, and iClusterPlus, streamlining workflows from data preprocessing and differential expression analysis to visualization. Case studies on three distinct real-world multimodal datasets illustrate its practical utility. Source code, documentation, and example datasets are available via GitHub (https://github.com/famanalytics0/MultiModalGraphics) and under review for inclusion into Bioconductor.