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

Abstract

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.

Keywords: Annotated heatmap; Composite feature heatmap; Multimodal visualization; Thresholded scatterplot.

Fig. 1

Example output from the AnnotatedHeatmap class, displaying statistical overlays (stars/dots) on heatmap tiles to indicate significant or trending molecular features. This enables immediate assessment of statistical relevance beyond color intensity alone. Data are drawn from integrated multimodal pan-cancer datasets (TCGA, ICGC, and cBioPortal), covering six cancer types: CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma), OV (ovarian serous cystadenocarcinoma), PRAD (prostate adenocarcinoma), TGCT (testicular germ cell tumors), UCEC (uterine corpus endometrial carcinoma), and UCS (uterine carcinosarcoma). The corresponding R code configuration used to generate this plot is provided in Supplementary Code Examples

Fig. 2

Example output from the CompositeFeatureHeatmap class, summarizing pathway-level activity across multiple brain regions and timepoints. Tile colors encode z-scores, overlaid dots encode significance, and dot size reflects feature counts, allowing multidimensional comparisons across conditions. The visualization summarizes results from 20 gene expression datasets (each ~ 45 K features across 10 mice) collected from five mouse brain regions at four timepoints (GEO accession GSE45035). The regions include AY (amygdala), HC (hippocampus), MPFC (medial prefrontal cortex), SE (septal region), ST (corpus striatum), and VS (ventral striatum). The heatmap highlights inhibition of neurogenesis and synaptic plasticity pathways associated with differentially expressed genes across time. The corresponding R code configuration used to generate this plot is provided in Supplementary Code Examples

Fig. 3

Example output from the ThresholdedScatterplot class (volcano-style), illustrating log2 fold changes against − log10 p-values with thresholds for significance explicitly marked. Numbers of up- and down-regulated features are annotated in each panel, enabling transparent and quantitative interpretation of differential results. Data are from multi-tissue, multi-timepoint studies of differentially expressed genes across spleen, heart, blood, and seven brain regions at five timepoints (T5R1, T5R10, T10R1, T10R28, and T10R42; GEO accession GSE68077). Brain region abbreviations: AY (amygdala), HC (hippocampus), MPFC (medial prefrontal cortex), SE (septal region), ST (corpus striatum), and VS (ventral striatum). T indicates days of trauma exposure, and R indicates post-trauma tissue collection days. These datasets were downloaded from Gene Expression Omnibus GSE68077. The corresponding R code configuration used to generate this plot is provided in Supplementary Code