生物医学可视化图表索引 / Biomedical Visualization Chart Index¶

基因表达数据可视化 / Gene Expression Data Visualization¶

热图 / Heatmap: 使用颜色强度展示多个基因在多个样本中的表达水平矩阵 / Matrix showing gene expression levels across samples using color intensity
火山图 / Volcano Plot: 同时展示基因表达变化的显著性和幅度的散点图 / Scatter plot showing both significance and magnitude of gene expression changes
MA图 / MA Plot: 展示平均表达强度与表达变化关系的散点图 / Scatter plot showing relationship between mean expression and fold change
PCA/MDS图 / PCA/MDS Plot: 将高维表达数据降维到2D/3D空间展示样本关系 / Projects high-dimensional expression data to 2D/3D to show sample relationships
箱线图 / Box Plot: 展示不同条件下基因表达分布的箱形图 / Shows distribution of expression values across conditions with boxes and whiskers
相关性散点图 / Correlation Scatter Plot: 展示两个基因表达水平相关性的散点图 / Scatter plot showing correlation between expression levels of two genes
线图 / Line Plot: 展示基因表达随时间变化的折线图 / Shows gene expression changes over time with connected lines
时序热图 / Time-series Heatmap: 按时间顺序排列的热图，展示表达随时间变化 / Heatmap with samples ordered by time to show temporal patterns
轨迹图 / Trajectory Plot: 展示细胞状态随时间演变的路径图 / Shows path of cellular states changing over time
流图 / Stream Plot: 展示细胞类型或基因模块比例随时间变化的面积图 / Area chart showing proportions of cell types or gene modules changing over time

单细胞与空间组学数据 / Single-cell and Spatial Omics Data¶

降维图 / Dimensionality Reduction Plot: 使用t-SNE/UMAP等方法展示单细胞聚类 / Shows cell clusters using t-SNE/UMAP or similar methods
特征图 / Feature Plot: 在降维图上叠加基因表达水平的散点图 / Overlays gene expression levels on dimensionality reduction plots
点图 / Dot Plot: 同时展示基因表达比例和强度的点阵图 / Shows both percentage of expressing cells and expression intensity
小提琴图 / Violin Plot: 展示基因在不同细胞类型中表达分布的概率密度图 / Shows distribution of gene expression across cell types
细胞-细胞互作网络 / Cell-Cell Interaction Network: 展示细胞类型间预测的信号通路互作 / Shows predicted signaling interactions between cell types
轨迹推断图 / Trajectory Inference Plot: 展示细胞分化或状态转变路径 / Shows developmental or state transition paths between cell states
空间特征图 / Spatial Feature Plot: 在组织坐标上叠加基因表达的空间图 / Overlays gene expression on tissue coordinates
空间聚类图 / Spatial Clustering Map: 基于表达相似性对组织区域进行聚类 / Colors tissue regions based on expression similarity clusters
空间共表达网络 / Spatial Co-expression Network: 展示具有相似空间分布的基因网络 / Network of genes with similar spatial distribution patterns
细胞类型解卷积图 / Cell Type Deconvolution Map: 展示组织中预测的细胞类型比例 / Shows predicted cell type proportions across tissue

基因组与表观基因组数据 / Genomic and Epigenomic Data¶

曼哈顿图 / Manhattan Plot: 展示全基因组关联研究的统计显著性 / Shows statistical significance across entire genome in association studies
棒棒糖图 / Lollipop Plot: 展示基因或蛋白序列上的变异位点 / Shows variants along a gene or protein sequence
肿瘤图谱 / Oncoprint: 展示多个样本中的基因改变矩阵 / Matrix showing genetic alterations across samples
环形图 / Circos Plot: 展示基因组区域间关系的环形图 / Circular representation showing relationships between genomic regions
雨滴图 / Rainfall Plot: 展示基因组位置与相邻变异距离关系的散点图 / Plots genomic position versus distance to neighboring variants
基因组浏览器轨道 / Genome Browser Track: 沿染色体展示基因组特征的线性图 / Linear representation of genomic features along chromosomes
信号分布热图 / Heatmap Metaplot: 展示信号在基因组特征周围分布的热图 / Heatmap showing signal distribution around genomic features
染色质互作图 / Chromatin Interaction Map: 展示基因组区域间互作频率的热图 / Heatmap showing interaction frequency between genomic regions
峰信号聚合图 / Aggregated Peak Plot: 展示基因组特征周围的平均信号分布 / Shows average signal profiles around genomic features
染色质状态图 / Chromatin State Map: 展示预测的染色质状态分布 / Visualizes predicted chromatin states across genomic regions

蛋白质与结构数据 / Protein and Structural Data¶

蛋白互作网络 / Protein-Protein Interaction Network: 展示蛋白质之间物理或功能互作的网络图 / Network showing physical or functional interactions between proteins
蛋白结构图 / Protein Structure Visualization: 展示蛋白质三维结构的立体图 / 3D representation of protein structure
结构域架构图 / Domain Architecture Plot: 展示蛋白质结构域组织的线性图 / Linear representation of protein domains and functional regions
序列标志图 / Sequence Logo Plot: 展示多序列比对中的序列保守性 / Visualizes sequence conservation in multiple sequence alignments
翻译后修饰图 / Post-translational Modification Map: 展示蛋白质修饰位点分布 / Shows locations and types of protein modifications
拉氏图 / Ramachandran Plot: 展示蛋白质骨架二面角分布 / Shows distribution of backbone dihedral angles in protein structures
接触图 / Contact Map: 展示蛋白质结构中氨基酸残基间接触的矩阵 / Matrix showing residue-residue contacts within protein structures
分子动力学轨迹图 / Molecular Dynamics Trajectory Plot: 展示蛋白质构象随时间变化 / Visualizes protein motion and conformational changes over time
配体-蛋白互作图 / Ligand-Protein Interaction Diagram: 展示配体与蛋白结合位点互作 / 2D representation of interactions between protein and bound molecules

通路与网络分析 / Pathway and Network Analysis¶

通路图 / Pathway Diagram: 展示生物通路中分子互作的图形表示 / Graphical representation of biological pathways with molecular interactions
富集图谱 / Enrichment Map: 展示富集通路及其关系的网络图 / Network visualization of enriched pathways and their relationships
桑基图 / Sankey Diagram: 展示集合间关系的流图 / Flow diagram showing relationships between sets or categories
集合交叉图 / UpSet Plot: 展示多个集合交集的替代维恩图 / Alternative to Venn diagrams for visualizing set intersections
基因调控网络 / Gene Regulatory Network: 展示转录调控关系的有向网络 / Directed network showing transcriptional regulation relationships
相关性网络 / Correlation Network: 展示基因表达相关性的网络图 / Network where edges represent correlation strength between nodes
模块检测图 / Module Detection Visualization: 展示网络中功能模块的社区结构 / Highlights densely connected subnetworks representing functional modules
中心性可视化 / Network Centrality Visualization: 突出显示基于网络拓扑度量的重要节点 / Highlights important nodes based on network topology metrics

临床与表型数据 / Clinical and Phenotypic Data¶

生存曲线 / Kaplan-Meier Curve: 展示不同组别随时间的生存概率 / Shows survival probability over time for different groups
森林图 / Forest Plot: 展示多个因素的风险比及置信区间 / Shows hazard ratios and confidence intervals for multiple factors
竞争风险累积发生率图 / Competing Risk Cumulative Incidence: 展示多种竞争性结局的概率 / Shows probability of different competing outcomes over time
时间依赖ROC曲线 / Time-dependent ROC Curve: 展示不同时间点的诊断性能 / Shows diagnostic performance at different time points
患者时间线图 / Patient Timeline Plot: 展示个体患者临床事件序列 / Shows sequence and timing of clinical events for individual patients
瀑布图 / Waterfall Plot: 展示个体患者反应幅度的条形图 / Bar chart showing response magnitude for individual patients
蜘蛛图 / Spider Plot: 展示个体患者随时间的反应轨迹 / Shows trajectory of response over time for individual patients
列线图 / Nomogram: 基于多变量预测结局的图形计算工具 / Graphical calculation tool for predicting outcomes based on multiple variables

多组学整合 / Multi-omics Integration¶

多组学热图 / Multi-omics Heatmap: 展示多种组学数据模式的并列热图 / Juxtaposed or integrated heatmaps showing patterns across data types
多组学环形图 / Circos Multi-omics Plot: 整合多种组学数据的环形可视化 / Circular visualization integrating multiple data types
降维双标图 / Dimension Reduction Biplot: 将样本和特征同时投影到降维空间 / Projects both samples and features onto reduced dimensional space
多组学桑基图 / Sankey Multi-omics Diagram: 展示多组学层级间特征关系的流图 / Flow diagram showing relationships between features across omics layers
多组学聚类热图 / Multi-omics Clustering Heatmap: 基于多组学整合分析的样本聚类 / Shows sample clustering based on integrated analysis of multiple data types
特征重要性图 / Feature Importance Plot: 展示来自不同组学的特征对分类的贡献 / Shows contribution of features from different omics to classification
驱动改变可视化 / Driver Alteration Visualization: 展示跨组学层级的关键分子改变 / Shows key molecular alterations driving phenotypes across omics layers
多模态富集可视化 / Multi-modal Enrichment Visualization: 展示多种数据类型的通路富集 / Shows pathway enrichment across multiple data types

I. Gene Expression Data Visualization¶

1. Differential Expression Analysis¶

Heatmaps¶

Description: Matrix visualization that uses color intensity to represent expression values, with rows typically representing genes and columns representing samples.
Best for: Visualizing patterns across multiple genes and samples simultaneously.
Variants:
Clustered heatmap: Includes hierarchical clustering dendrograms to group similar samples/genes.
Annotated heatmap: Includes color bars for sample/gene metadata (e.g., treatment, tissue type).
Z-score heatmap: Normalizes expression values for each gene to highlight relative changes.
Biclustered heatmap: Simultaneously clusters both rows and columns to identify gene modules.

Volcano Plots¶

Description: Scatter plot showing statistical significance (-log10 p-value) versus magnitude of change (log2 fold change).
Best for: Identifying genes with both statistical and biological significance.
Variants:
Enhanced volcano: Includes gene labels for top hits and customizable significance thresholds.
Interactive volcano: Allows hovering/clicking on points to reveal gene details.
Quadrant volcano: Divides plot into regions based on up/down-regulation and significance.
3D volcano: Adds a third dimension (e.g., expression level) represented by point size or color.

MA Plots¶

Description: Scatter plot of log-ratio (M) versus mean average (A) values, showing relationship between expression intensity and fold change.
Best for: Identifying intensity-dependent biases in differential expression.
Variants:
Smoothed MA plot: Includes a trend line showing average fold change across expression levels.
Highlighted MA plot: Colors points based on statistical significance.
Density MA plot: Uses color to show point density in crowded regions.
Paired MA plot: Shows multiple comparisons side-by-side for the same gene set.

PCA/MDS Plots¶

Description: Dimensionality reduction techniques that project high-dimensional expression data onto 2D/3D space.
Best for: Visualizing overall sample relationships and identifying batch effects.
Variants:
Biplot PCA: Shows both sample positions and gene loadings in the same plot.
3D PCA: Adds a third principal component for more detailed separation.
Ellipse PCA: Adds confidence ellipses around sample groups.
Animated PCA: Shows rotation through different principal components.

Box-and-Whisker Plots¶

Description: Shows distribution of expression values across sample groups with median, quartiles, and outliers.
Best for: Comparing expression distributions between conditions.
Variants:
Notched boxplot: Includes notches indicating confidence interval around median.
Violin plot: Combines boxplot with kernel density estimate for distribution shape.
Raincloud plot: Combines boxplot, violin plot, and individual data points.
Grouped boxplot: Shows multiple genes side-by-side across conditions.

Scatter Plots with Trend Lines¶

Description: Shows relationship between expression of two genes with optional trend lines.
Best for: Examining correlation between genes or comparing expression across conditions.
Variants:
Hexbin scatter: Uses hexagonal binning for dense data visualization.
Contour scatter: Adds density contours to highlight data distribution.
Marginal histogram scatter: Includes histograms along axes for distribution context.
LOESS smoothed scatter: Adds locally estimated scatterplot smoothing curve.

2. Time-series Expression Data¶

Line Plots¶

Description: Shows expression changes over time with lines connecting time points.
Best for: Visualizing temporal expression patterns of selected genes.
Variants:
Multi-gene line plot: Shows multiple genes with different colors/patterns.
Shadowed line plot: Includes confidence intervals or standard error bands.
Stacked line plot: Shows cumulative expression patterns.
Faceted line plot: Creates small multiples for different genes or conditions.

Heatmap with Time Ordering¶

Description: Heatmap with samples ordered by time point to show temporal patterns.
Best for: Visualizing global expression changes over time across many genes.
Variants:
Clustered time heatmap: Clusters genes with similar temporal patterns.
Interpolated time heatmap: Uses color gradient to smooth between time points.
Standardized time heatmap: Z-score normalizes each gene across time points.
Annotated time heatmap: Includes pathway or functional annotations.

Trajectory Plots¶

Description: Shows progression of cellular states in reduced dimensional space.
Best for: Single-cell RNA-seq time course experiments.
Variants:
Pseudotime trajectory: Orders cells by developmental or response progression.
Branched trajectory: Shows bifurcations in cell fate decisions.
RNA velocity trajectory: Includes arrows indicating future state predictions.
Annotated trajectory: Overlays cell type or state information.

Stream Plots¶

Description: Area plots showing how proportions of cell types or gene modules change over time.
Best for: Visualizing compositional changes in cell populations.
Variants:
Stacked stream plot: Shows absolute numbers stacked vertically.
Normalized stream plot: Shows relative proportions that sum to 100%.
Smoothed stream plot: Uses spline interpolation between time points.
Highlighted stream plot: Emphasizes specific cell types or gene modules.

Slope Graphs¶

Description: Connects expression values between two time points with straight lines.
Best for: Comparing expression changes between specific time points.
Variants:
Multi-condition slope graph: Uses color to distinguish experimental conditions.
Highlighted slope graph: Emphasizes specific genes of interest.
Ranked slope graph: Orders genes by expression at each time point.
Filtered slope graph: Shows only genes with significant changes.

II. Single-Cell and Spatial Omics Data¶

1. Single-Cell RNA-seq Visualization¶

Dimensionality Reduction Plots¶

Description: Reduces high-dimensional expression data to 2D/3D for visualization.
Best for: Identifying cell clusters and visualizing cell type relationships.
Variants:
t-SNE plot: Preserves local structure, good for revealing clusters.
UMAP plot: Preserves both local and global structure, faster than t-SNE.
PHATE plot: Emphasizes progression and continuum between cell states.
FDG plot: Force-directed graph layout based on expression similarity.

Feature Plots¶

Description: Overlays gene expression values on dimensionality reduction plots.
Best for: Visualizing expression patterns of marker genes across cell populations.
Variants:
Multi-feature plot: Shows several genes in small multiples.
Gradient feature plot: Uses color intensity for expression level.
Binary feature plot: Highlights cells above expression threshold.
Ridge feature plot: Adds vertical dimension showing expression distribution.

Dot Plots¶

Description: Shows both percentage of expressing cells (dot size) and average expression level (color intensity).
Best for: Comparing marker gene expression across cell types.
Variants:
Hierarchical dot plot: Orders cell types by similarity.
Pathway dot plot: Groups genes by functional pathway.
Comparative dot plot: Shows differences between conditions.
Annotated dot plot: Includes significance indicators.

Violin Plots¶

Description: Shows distribution of gene expression across cell types/clusters.
Best for: Comparing expression distributions between cell populations.
Variants:
Split violin plot: Compares distributions between two conditions.
Multi-gene violin plot: Shows several genes across cell types.
Ridgeline violin plot: Overlaps distributions for space efficiency.
Box-violin hybrid: Combines violin with boxplot statistics.

Cell-Cell Interaction Networks¶

Description: Network visualization showing predicted interactions between cell types.
Best for: Analyzing cellular communication and signaling pathways.
Variants:
Circos interaction plot: Shows interactions between cell types in circular layout.
Heatmap interaction plot: Shows interaction strength between cell pairs.
Chord diagram: Visualizes interaction flow between cell populations.
Sankey diagram: Shows signaling pathway flow between cell types.

Trajectory Inference Visualizations¶

Description: Shows developmental or differentiation paths between cell states.
Best for: Understanding cellular transitions and lineage relationships.
Variants:
Tree plot: Shows hierarchical differentiation paths.
Subway map plot: Shows parallel differentiation trajectories.
Stream plot: Shows density of cells along differentiation paths.
State transition graph: Shows probability of transitions between states.

2. Spatial Transcriptomics Visualization¶

Spatial Feature Plots¶

Description: Overlays gene expression on tissue coordinates/images.
Best for: Visualizing spatial expression patterns in tissue context.
Variants:
Spot-based spatial plot: Shows expression in discrete capture spots.
Cell-based spatial plot: Shows single-cell resolution expression.
Contour spatial plot: Uses isolines to show expression gradients.
3D spatial plot: Shows expression in three-dimensional tissue context.

Spatial Clustering Maps¶

Description: Colors tissue regions based on expression similarity clusters.
Best for: Identifying tissue domains with similar expression profiles.
Variants:
Hard boundary clustering: Shows discrete cluster assignments.
Soft boundary clustering: Uses color blending for gradual transitions.
Hierarchical spatial clustering: Shows nested tissue domains.
Spatially-aware clustering: Incorporates physical proximity in clustering.

Spatial Co-expression Networks¶

Description: Visualizes gene-gene correlations with spatial context.
Best for: Identifying spatially regulated gene programs.
Variants:
Spatial correlation network: Shows co-expressed genes in spatial domains.
Module spatial plot: Shows expression of gene modules across tissue.
Spatial gene-gene heatmap: Correlates genes with similar spatial patterns.
Spatial enrichment map: Shows pathway enrichment in spatial domains.

Cell Type Deconvolution Maps¶

Description: Visualizes predicted cell type proportions across tissue.
Best for: Inferring cellular composition in spatial transcriptomics data.
Variants:
Pie chart spatial map: Shows cell type proportions at each location.
Stacked bar spatial map: Shows absolute counts of each cell type.
Cell type probability map: Shows likelihood of specific cell type.
Dominant cell type map: Colors regions by most abundant cell type.

III. Genomic and Epigenomic Data¶

1. Variant Analysis Visualization¶

Manhattan Plots¶

Description: Plots genomic position (x-axis) versus statistical significance (y-axis).
Best for: Genome-wide association studies (GWAS) and similar analyses.
Variants:
Multi-trait Manhattan: Shows multiple phenotypes in different colors.
Annotated Manhattan: Labels significant loci with gene names.
QQ Manhattan: Includes quantile-quantile plot to assess statistical inflation.
Regional Manhattan: Zooms in on specific genomic region of interest.

Lollipop Plots¶

Description: Shows variants along a gene or protein sequence with stems indicating significance.
Best for: Visualizing mutation hotspots and functional domains.
Variants:
Domain-annotated lollipop: Includes protein domain information.
Multi-sample lollipop: Uses color to distinguish variants from different samples.
Frequency lollipop: Sizes lollipops by variant frequency.
Functional impact lollipop: Colors by predicted functional effect.

Oncoprints¶

Description: Matrix showing genetic alterations across samples.
Best for: Comparing mutation profiles across cancer samples.
Variants:
Multi-omics oncoprint: Integrates mutations, CNVs, and expression.
Clinical oncoprint: Includes patient metadata and outcomes.
Pathway oncoprint: Groups genes by functional pathway.
Clustered oncoprint: Orders samples by similarity of alteration profile.

Circos Plots¶

Description: Circular representation of genomic data showing relationships between regions.
Best for: Visualizing structural variants, translocations, and interactions.
Variants:
Multi-track circos: Includes multiple data types (CNV, expression, etc.).
Chord circos: Shows connections between genomic regions.
Heatmap circos: Includes heatmaps along circular tracks.
Histogram circos: Shows quantitative data as radial histograms.

Rainfall Plots¶

Description: Plots genomic position versus distance to neighboring variants.
Best for: Identifying hypermutated regions or mutation clusters.
Variants:
Mutation type rainfall: Colors points by mutation type (e.g., C>T, A>G).
Sample rainfall: Compares mutation distributions across samples.
Kataegis rainfall: Highlights regions of localized hypermutation.
Annotated rainfall: Includes gene annotations for relevant regions.

2. Epigenomic Data Visualization¶

Genome Browser Tracks¶

Description: Linear representation of genomic features along chromosomes.
Best for: Integrating multiple data types at specific genomic loci.
Variants:
Multi-sample tracks: Compares samples side-by-side.
Signal tracks: Shows continuous signal (e.g., ChIP-seq, ATAC-seq).
Annotation tracks: Shows genes, regulatory elements, etc.
Interaction tracks: Shows chromatin interactions (e.g., Hi-C, ChIA-PET).

Heatmap Metaplots¶

Description: Heatmap showing signal distribution around genomic features.
Best for: Visualizing patterns around transcription start sites, enhancers, etc.
Variants:
K-means clustered metaplot: Groups features by signal pattern.
Multi-condition metaplot: Compares signal across experimental conditions.
Composite metaplot: Combines multiple signal types.
Differential metaplot: Highlights differences between conditions.

Chromatin Interaction Maps¶

Description: Heatmap showing interaction frequency between genomic regions.
Best for: Visualizing 3D genome organization from Hi-C or similar data.
Variants:
Multi-resolution contact map: Shows different zoom levels.
Differential contact map: Highlights changes between conditions.
Virtual 4C plot: Shows interactions from specific viewpoint.
Triangular contact map: Shows TADs and domain boundaries.

Aggregated Peak Analysis¶

Description: Shows average signal profiles around genomic features.
Best for: Comparing epigenetic marks around regulatory elements.
Variants:
Profile plot: Shows average signal with confidence intervals.
Heatmap profile: Shows individual features stacked as heatmap rows.
K-means profile: Clusters features by signal pattern.
Multi-mark profile: Compares different epigenetic marks.

Chromatin State Maps¶

Description: Visualizes predicted chromatin states across genomic regions.
Best for: Interpreting combinatorial patterns of epigenetic marks.
Variants:
Genome browser state track: Shows states along linear genome.
State enrichment plot: Shows state enrichment near genomic features.
State transition map: Shows changes in states between conditions.
State composition plot: Shows proportion of genome in each state.

IV. Protein and Structural Data (continued)¶

1. Protein Analysis Visualization (continued)¶

Protein-Protein Interaction Networks (continued)¶

Variants (continued):
Clustered network: Groups proteins by functional modules or complexes.
Weighted network: Uses edge thickness to indicate interaction confidence.
Dynamic network: Shows changes in interactions across conditions.
Hierarchical network: Organizes proteins by cellular compartment or function.

Protein Structure Visualization¶

Description: 3D representation of protein structure showing atomic or residue-level details.
Best for: Understanding protein function, mutation effects, and binding sites.
Variants:
Ribbon diagram: Shows secondary structure elements (α-helices, β-sheets).
Surface representation: Shows molecular surface and binding pockets.
Ball-and-stick model: Emphasizes atomic bonds and interactions.
Ensemble visualization: Shows multiple conformational states.

Domain Architecture Plots¶

Description: Linear representation of protein domains and functional regions.
Best for: Comparing domain organization across protein families.
Variants:
Multi-protein domain plot: Aligns related proteins by domain structure.
Mutation-annotated domain plot: Shows mutation locations relative to domains.
Exon-mapped domain plot: Shows relationship between exons and domains.
Interactive domain plot: Allows exploration of domain features.

Sequence Logo Plots¶

Description: Visualizes sequence conservation in multiple sequence alignments.
Best for: Identifying conserved motifs and binding sites.
Variants:
Information content logo: Scales letter height by conservation.
Probability logo: Shows frequency of each residue at each position.
Difference logo: Highlights differences between two sequence sets.
3D structure-mapped logo: Projects conservation onto structural elements.

Post-translational Modification Maps¶

Description: Shows locations and types of protein modifications.
Best for: Understanding regulation of protein function.
Variants:
Linear PTM map: Shows modifications along protein sequence.
Structure-mapped PTM: Projects modifications onto 3D structure.
Quantitative PTM map: Shows modification abundance across conditions.
Temporal PTM map: Shows dynamic changes in modifications over time.

2. Structural Biology Visualization¶

Ramachandran Plots¶

Description: Shows distribution of backbone dihedral angles in protein structures.
Best for: Assessing protein structure quality and conformational preferences.
Variants:
Residue-specific Ramachandran: Highlights specific amino acids.
Density Ramachandran: Uses contours to show preferred regions.
Multi-structure Ramachandran: Compares angles across protein structures.
Secondary structure Ramachandran: Colors points by secondary structure.

Contact Maps¶

Description: Matrix showing residue-residue contacts within protein structures.
Best for: Comparing protein folds and identifying structural domains.
Variants:
Distance map: Uses color to represent inter-residue distances.
Difference contact map: Highlights structural changes between states.
Native contact map: Shows contacts present in native structure.
Evolution-based contact map: Integrates evolutionary coupling information.

Molecular Dynamics Trajectory Analysis¶

Description: Visualizes protein motion and conformational changes over time.
Best for: Understanding protein flexibility and function.
Variants:
RMSD plot: Shows structural deviation over simulation time.
Principal component projection: Shows major modes of motion.
Free energy landscape: Maps conformational space by energy.
Residue fluctuation plot: Shows flexibility of individual residues.

Ligand-Protein Interaction Diagrams¶

Description: 2D representation of interactions between protein and bound molecules.
Best for: Understanding binding mechanisms and drug design.
Variants:
2D interaction diagram: Shows hydrogen bonds, hydrophobic interactions, etc.
Interaction fingerprint: Compares interactions across multiple ligands.
Interaction network: Shows residue interaction network around binding site.
Pharmacophore map: Highlights key features for molecular recognition.

V. Pathway and Network Analysis¶

1. Pathway Visualization¶

Pathway Diagrams¶

Description: Graphical representation of biological pathways with genes/proteins as nodes.
Best for: Visualizing molecular interactions in biological processes.
Variants:
Expression-overlaid pathway: Maps expression data onto pathway components.
Multi-condition pathway: Shows pathway changes across conditions.
Mutation-annotated pathway: Highlights mutated components.
Flux pathway: Shows metabolic flux through pathways.

Enrichment Maps¶

Description: Network visualization of enriched pathways and their relationships.
Best for: Interpreting functional enrichment analysis results.
Variants:
Clustered enrichment map: Groups related pathways.
Edge-weighted enrichment map: Shows pathway similarity strength.
Multi-dataset enrichment map: Compares enrichment across conditions.
Temporal enrichment map: Shows pathway enrichment changes over time.

Alluvial/Sankey Diagrams¶

Description: Flow diagram showing relationships between sets or categories.
Best for: Visualizing pathway cross-talk or gene set overlaps.
Variants:
Gene-pathway Sankey: Shows gene membership across pathways.
Multi-omics Sankey: Connects findings across data types.
Temporal Sankey: Shows changes in pathway activity over time.
Hierarchical Sankey: Shows nested pathway relationships.

Upset Plots¶

Description: Alternative to Venn diagrams for visualizing set intersections.
Best for: Comparing membership across multiple gene sets or pathways.
Variants:
Interactive UpSet plot: Allows selection of specific intersections.
Quantitative UpSet plot: Sizes bars by statistical significance.
Connected UpSet plot: Shows relationships between intersections.
Attribute-enriched UpSet: Colors intersections by additional metadata.

2. Network Analysis Visualization¶

Gene Regulatory Networks¶

Description: Directed network showing transcriptional regulation relationships.
Best for: Understanding gene expression control mechanisms.
Variants:
TF-centered regulatory network: Focuses on specific transcription factors.
Inferred regulatory network: Shows predicted regulatory relationships.
Layered regulatory network: Organizes by regulatory hierarchy.
Condition-specific regulatory network: Shows context-dependent regulation.

Correlation Networks¶

Description: Network where edges represent correlation strength between nodes.
Best for: Identifying co-expressed genes or co-regulated processes.
Variants:
Thresholded correlation network: Shows only strong correlations.
Signed correlation network: Distinguishes positive and negative correlations.
Partial correlation network: Controls for indirect correlations.
Differential correlation network: Shows changes in correlations between conditions.

Module Detection Visualizations¶

Description: Highlights densely connected subnetworks representing functional modules.
Best for: Identifying biological processes from network structure.
Variants:
Community detection map: Colors nodes by module membership.
Module eigengene plot: Shows module activity across samples.
Module preservation plot: Compares module structure across datasets.
Module-trait correlation heatmap: Relates modules to phenotypic traits.

Network Centrality Visualizations¶

Description: Highlights important nodes based on network topology metrics.
Best for: Identifying key regulators or hub genes.
Variants:
Degree centrality map: Sizes nodes by number of connections.
Betweenness centrality map: Highlights nodes that bridge network regions.
Eigenvector centrality map: Emphasizes connections to other important nodes.
Multi-metric centrality plot: Compares different centrality measures.

VI. Clinical and Phenotypic Data¶

1. Survival Analysis Visualization¶

Kaplan-Meier Curves¶

Description: Shows survival probability over time for different groups.
Best for: Comparing survival outcomes between patient cohorts.
Variants:
Annotated Kaplan-Meier: Includes confidence intervals and risk tables.
Multi-group Kaplan-Meier: Compares multiple patient subgroups.
Landmark Kaplan-Meier: Analyzes survival from specific time points.
Restricted mean survival time plot: Alternative to hazard ratio comparison.

Forest Plots¶

Description: Shows hazard ratios and confidence intervals for multiple factors.
Best for: Visualizing results from Cox regression or similar analyses.
Variants:
Subgroup forest plot: Analyzes effect in different patient subgroups.
Multi-endpoint forest plot: Compares effects on different outcomes.
Interaction forest plot: Shows effect modification by other factors.
Time-dependent forest plot: Shows changes in hazard ratios over time.

Competing Risk Cumulative Incidence¶

Description: Shows probability of different competing outcomes over time.
Best for: Analyzing events where multiple outcomes can occur.
Variants:
Stacked cumulative incidence: Shows all competing outcomes together.
Cause-specific cumulative incidence: Focuses on specific outcome.
Multi-group cumulative incidence: Compares incidence across patient groups.
Conditional cumulative incidence: Shows incidence given survival to specific time.

Time-dependent ROC Curves¶

Description: Shows diagnostic performance at different time points.
Best for: Evaluating predictive biomarkers for time-to-event outcomes.
Variants:
Multi-timepoint ROC: Compares ROC curves at different follow-up times.
AUC timeline plot: Shows changes in AUC over follow-up time.
Integrated time-dependent ROC: Summarizes performance across time range.
Comparative time-dependent ROC: Compares multiple predictors.

2. Clinical Data Visualization¶

Patient Timeline Plots¶

Description: Shows sequence and timing of clinical events for individual patients.
Best for: Visualizing disease progression and treatment history.
Variants:
Swimmer plot: Shows duration of response for individual patients.
Event timeline: Shows discrete events along patient history.
Treatment timeline: Focuses on therapy administration periods.
Biomarker timeline: Integrates quantitative measurements over time.

Waterfall Plots¶

Description: Bar chart showing response magnitude for individual patients.
Best for: Visualizing treatment response heterogeneity.
Variants:
Annotated waterfall: Includes patient characteristics as annotations.
Sorted waterfall: Orders patients by response magnitude.
Grouped waterfall: Organizes patients by subgroups.
Before-after waterfall: Shows changes from baseline.

Spider Plots¶

Description: Shows trajectory of response measurements over time for individual patients.
Best for: Visualizing dynamics of treatment response.
Variants:
Color-coded spider: Distinguishes patient subgroups.
Landmark spider: Highlights specific timepoints of interest.
Annotated spider: Includes treatment changes or events.
Normalized spider: Shows percent change from baseline.

Nomograms¶

Description: Graphical calculation tool for predicting outcomes based on multiple variables.
Best for: Creating clinically useful prediction models.
Variants:
Cox nomogram: Based on proportional hazards model.
Logistic nomogram: Predicts binary outcomes.
Competing risk nomogram: Accounts for multiple possible outcomes.
Dynamic nomogram: Interactive tool with adjustable parameters.

VII. Multi-omics Integration¶

1. Multi-omics Data Integration¶

Multi-omics Heatmaps¶

Description: Juxtaposed or integrated heatmaps showing patterns across data types.
Best for: Comparing patterns across different molecular layers.
Variants:
Stacked multi-omics heatmap: Aligns multiple data types for same samples.
Integrated clustering heatmap: Uses joint clustering across data types.
Correlation heatmap: Shows relationships between features across omics.
Block heatmap: Organizes by data type and sample clusters.

Circos Multi-omics Plots¶

Description: Circular visualization integrating multiple data types.
Best for: Showing relationships between genomic features and other omics data.
Variants:
Multi-track circos: Shows different omics in concentric rings.
Link-enhanced circos: Shows connections between features across omics.
Histogram circos: Uses bar heights to show quantitative multi-omics data.
Heatmap circos: Incorporates heatmaps in circular layout.

Dimension Reduction Biplots¶

Description: Projects both samples and features onto reduced dimensional space.
Best for: Identifying relationships between molecular features and sample groups.
Variants:
Multi-omics MOFA plot: Uses multi-omics factor analysis.
Joint PCA biplot: Integrates multiple data types in PCA.
Canonical correlation biplot: Shows maximally correlated projections.
Multi-block PLS biplot: Uses partial least squares for integration.

Sankey Multi-omics Diagrams¶

Description: Flow diagram showing relationships between features across omics layers.
Best for: Visualizing multi-omics pathway analysis results.
Variants:
Vertical multi-omics Sankey: Flows from genome to transcriptome to proteome.
Horizontal multi-omics Sankey: Shows relationships within each omics layer.
Pathway-centric Sankey: Organizes by biological pathways.
Sample-flow Sankey: Shows how samples cluster across different omics.

2. Integrative Analysis Visualization¶

Multi-omics Clustering Heatmaps¶

Description: Shows sample clustering based on integrated analysis of multiple data types.
Best for: Identifying molecular subtypes from multi-omics data.
Variants:
SNF clustering heatmap: Based on similarity network fusion.
iCluster heatmap: Uses integrative clustering approach.
MOFA clustering: Based on multi-omics factor analysis.
Consensus clustering heatmap: Shows agreement across clustering solutions.

Feature Importance Plots¶

Description: Shows contribution of features from different omics to classification or prediction.
Best for: Understanding which molecular features drive sample classification.
Variants:
SHAP multi-omics plot: Uses Shapley values for feature importance.
Random forest importance plot: Shows feature importance from RF models.
Elastic net coefficient plot: Shows non-zero coefficients from regularized models.
Omics-stratified importance: Groups features by data type.

Driver Alteration Visualizations¶

Description: Shows key molecular alterations driving phenotypes across omics layers.
Best for: Identifying causal mechanisms in disease.
Variants:
Multi-omics oncoprint: Integrates mutations, CNV, and expression.
Causal network graph: Shows inferred causal relationships.
eQTL effect plot: Shows genetic variants affecting expression.
Mediator analysis plot: Shows molecular mediators of genetic effects.

Description: Shows pathway or functional enrichment across multiple data types.
Best for: Understanding biological processes affected across molecular layers.
Variants:
Multi-omics enrichment map: Network of enriched pathways across omics.
Stacked enrichment bar plot: Compares enrichment across data types.
Enrichment overlap diagram: Shows common and unique enriched terms.
Hierarchical enrichment treemap: Organizes enrichment by ontology structure.

VIII. Specialized Visualization Techniques (continued)¶

1. Machine Learning Model Visualization (continued)¶

Model Performance Visualizations (continued)¶

Description: Shows predictive performance of machine learning models.
Best for: Evaluating and comparing prediction models.
Variants:
ROC curves: Plots sensitivity vs. 1-specificity across thresholds.
Precision-recall curves: Alternative to ROC for imbalanced datasets.
Calibration plots: Shows agreement between predicted and actual probabilities.
Learning curves: Shows performance as a function of training set size.

Feature Importance Visualizations¶

Description: Shows contribution of features to model predictions.
Best for: Interpreting complex models and identifying key predictors.
Variants:
SHAP summary plot: Shows feature impact on model output.
Permutation importance plot: Shows performance drop when features are shuffled.
Partial dependence plots: Shows relationship between features and predictions.
Feature interaction heatmaps: Shows pairwise feature interactions.

Classifier Decision Boundary Plots¶

Description: Shows how a classifier separates classes in feature space.
Best for: Understanding classification model behavior.
Variants:
2D decision boundary: Shows boundary in two-feature space.
Decision surface heatmap: Uses color to show probability across feature space.
Margin plot: Highlights samples near decision boundary.
Multi-class decision boundary: Shows boundaries between multiple classes.

Confusion Matrix Visualizations¶

Description: Shows agreement between predicted and actual classes.
Best for: Detailed analysis of classification performance.
Variants:
Normalized confusion matrix: Shows proportions instead of counts.
Hierarchical confusion matrix: Groups related classes.
Difference confusion matrix: Compares performance between models.
Interactive confusion matrix: Allows exploration of misclassified examples.

2. Network Medicine Visualization¶

Disease-Gene Networks¶

Description: Shows relationships between diseases and associated genes.
Best for: Understanding genetic relationships between diseases.
Variants:
Bipartite disease-gene network: Shows connections between diseases and genes.
Disease similarity network: Links diseases by shared genetic basis.
Gene-centric disease network: Focuses on genes associated with multiple diseases.
Hierarchical disease network: Organizes by disease taxonomy.

Drug-Target Networks¶

Description: Shows relationships between drugs and their molecular targets.
Best for: Drug repurposing and understanding polypharmacology.
Variants:
Bipartite drug-target network: Shows direct drug-protein interactions.
Drug similarity network: Links drugs by shared targets or mechanisms.
Target-centric drug network: Focuses on proteins targeted by multiple drugs.
Drug-pathway network: Integrates pathway information with drug targets.

Symptom-Disease Networks¶

Description: Shows relationships between clinical symptoms and diseases.
Best for: Differential diagnosis and phenotypic analysis.
Variants:
Bipartite symptom-disease network: Shows symptom-disease associations.
Symptom co-occurrence network: Links symptoms that frequently co-occur.
Disease comorbidity network: Shows diseases that frequently co-occur.
Temporal symptom network: Shows progression of symptoms over time.

Multi-scale Biological Networks¶

Description: Integrates molecular, cellular, and phenotypic data in network representation.
Best for: Understanding disease mechanisms across biological scales.
Variants:
Hierarchical multi-scale network: Organizes by biological scale.
Causal multi-scale network: Shows causal relationships across scales.
Functional multi-scale network: Organizes by biological function.
Dynamic multi-scale network: Shows changes across time or disease progression.

3. Metabolomics Visualization¶

Metabolite Pathway Maps¶

Description: Shows metabolites in context of biochemical pathways.
Best for: Understanding metabolic alterations in disease.
Variants:
Expression-overlaid pathway: Maps concentration changes onto pathways.
Flux pathway map: Shows metabolic flux through pathways.
Time-series pathway map: Shows dynamic changes in metabolism.
Comparative pathway map: Shows differences between conditions.

Metabolite Correlation Networks¶

Description: Network showing correlations between metabolite levels.
Best for: Identifying co-regulated metabolites and metabolic modules.
Variants:
WGCNA metabolite network: Uses weighted correlation network analysis.
Gaussian graphical model network: Shows direct metabolite associations.
Biochemical-distance network: Incorporates knowledge of reaction steps.
Multi-condition correlation network: Shows changes in correlations across conditions.

Mass Spectrometry Visualizations¶

Description: Shows mass spectrometry data for metabolite identification.
Best for: Analyzing complex metabolomic datasets.
Variants:
Mirror plots: Compares experimental and reference spectra.
Chromatogram plots: Shows separation of metabolites by retention time.
Ion map: 2D visualization of m/z versus retention time.
Fragmentation tree: Shows hierarchical fragmentation patterns.

Metabolite Set Enrichment Visualizations¶

Description: Shows enrichment of metabolite sets in biological pathways.
Best for: Functional interpretation of metabolomics data.
Variants:
Metabolite set enrichment bar plot: Shows enriched pathways.
Metabolite enrichment network: Shows relationships between enriched pathways.
Metabolite over-representation map: Shows metabolite distribution across pathways.
Joint pathway analysis: Integrates metabolomics with other omics data.

IX. Emerging Visualization Techniques¶

1. Interactive and Dynamic Visualizations¶

Interactive Web Applications¶

Description: Browser-based tools allowing user-driven exploration of data.
Best for: Sharing complex datasets with collaborators or the public.
Variants:
Shiny apps: R-based interactive visualizations.
Plotly dashboards: JavaScript-based interactive plots.
D3.js visualizations: Custom interactive data visualizations.
Tableau dashboards: Business intelligence-style interactive reports.

Dynamic Animations¶

Description: Shows changes in data over time through animation.
Best for: Visualizing temporal processes or comparing multiple conditions.
Variants:
Time-lapse plots: Shows data changing over time.
Transition animations: Smoothly morphs between different data views.
Animated trajectories: Shows movement through data space.
Storytelling animations: Guides viewer through sequential data insights.

Virtual Reality Data Exploration¶

Description: Immersive 3D visualization of complex biological data.
Best for: Exploring highly dimensional or spatial datasets.
Variants:
VR protein structures: Allows manipulation of 3D molecular structures.
VR genome browsers: Navigates 3D genome organization.
VR pathway maps: Walks through 3D representations of biological pathways.
VR single-cell landscapes: Explores high-dimensional single-cell data in 3D space.

Real-time Data Monitoring¶

Description: Continuously updated visualizations as new data arrives.
Best for: Clinical monitoring or ongoing experimental data collection.
Variants:
Clinical dashboard: Shows patient vital signs and lab values.
Experiment monitoring: Tracks experimental measurements in real-time.
Sequencing run visualizations: Shows quality metrics during sequencing.
Alert-based visualizations: Highlights deviations from expected patterns.

2. AI-assisted Visualization¶

Dimension Reduction for Complex Data¶

Description: Uses advanced AI techniques to visualize high-dimensional data.
Best for: Exploring complex relationships in large datasets.
Variants:
UMAP plots: Non-linear dimension reduction preserving global and local structure.
VAE latent space visualization: Shows learned representations from variational autoencoders.
Self-organizing maps: Uses unsupervised neural networks for visualization.
Graph neural network embeddings: Visualizes learned node representations.

Attention Mechanism Visualizations¶

Description: Shows what parts of input data AI models focus on for predictions.
Best for: Interpreting deep learning model decisions.
Variants:
Attention heatmaps: Shows attention weights across input features.
Saliency maps: Highlights input regions important for classification.
Class activation maps: Shows regions contributing to specific class predictions.
Attention flow diagrams: Shows how attention propagates through model layers.

Generative Model Visualizations¶

Description: Shows data generated or completed by AI models.
Best for: Understanding data distributions and model capabilities.
Variants:
GAN output galleries: Shows examples generated by generative adversarial networks.
Latent space interpolation: Shows smooth transitions between data points.
Conditional generation plots: Shows generated data under different conditions.
Data imputation visualizations: Shows AI-completed missing data.

Uncertainty Visualization¶

Description: Shows confidence or uncertainty in AI predictions.
Best for: Assessing reliability of model outputs.
Variants:
Confidence interval plots: Shows prediction ranges with uncertainty.
Ensemble prediction plots: Shows variation across multiple models.
Bayesian posterior plots: Shows distribution of possible parameter values.
Uncertainty heatmaps: Maps prediction uncertainty across feature space.

3. Integrated Multi-view Visualizations¶

Coordinated Multiple Views¶

Description: Multiple linked visualizations showing different aspects of the same data.
Best for: Exploring complex datasets from multiple perspectives.
Variants:
Brushing and linking: Selection in one view highlights corresponding data in others.
Overview+detail views: Combines broad overview with detailed inspection.
Cross-filtered dashboards: Allows filtering data across multiple visualizations.
Multi-scale views: Shows data at different levels of granularity.

Integrated Genomic Views¶

Description: Combines multiple genomic data types in coordinated visualization.
Best for: Comprehensive analysis of genomic regions.
Variants:
Multi-omics genome browser: Shows multiple data tracks aligned to genome.
Circular-linear genome view: Combines circular overview with linear detail.
3D-1D genome integration: Links 3D structure with linear genomic features.
Comparative genomics viewer: Aligns multiple species or samples.

Clinical-Molecular Integrated Views¶

Description: Combines clinical data with molecular profiling in unified visualization.
Best for: Translational research connecting molecular mechanisms to clinical outcomes.
Variants:
Patient-molecular profile dashboard: Links patient data with molecular features.
Survival-molecular correlation view: Connects molecular features to outcomes.
Treatment response-biomarker view: Links therapy response to molecular markers.
Phenotype-genotype network: Shows relationships between clinical and molecular features.

Spatial-Molecular Integrated Views¶

Description: Combines spatial tissue information with molecular profiles.
Best for: Understanding tissue architecture and molecular heterogeneity.
Variants:
H&E-molecular overlay: Maps molecular data onto histology images.
Spatial transcriptomics viewer: Shows gene expression in spatial context.
Multi-channel IF viewer: Shows multiple protein markers in tissue context.
3D tissue reconstruction: Reconstructs molecular profiles in three dimensions.

X. Visualization Best Practices¶

1. Design Principles¶

Color Selection Strategies¶

Description: Choosing appropriate color schemes for different data types.
Best practices:
Use perceptually uniform colormaps (viridis, magma, plasma) for continuous data
Use colorblind-friendly palettes for categorical data
Reserve red/blue for opposing conditions (up/down, high/low)
Use sequential color scales for quantities without natural midpoint
Use diverging color scales for data with meaningful midpoint

Annotation and Labeling¶

Description: Adding context and explanation to visualizations.
Best practices:
Label axes clearly with units
Use informative titles that explain the main finding
Add annotations highlighting key features
Include legends explaining all visual encodings
Consider direct labeling instead of legends when possible

Layout Optimization¶

Description: Arranging multiple plots or elements effectively.
Best practices:
Create logical reading flow (typically left-to-right, top-to-bottom)
Group related visualizations
Maintain consistent scales across related plots
Use white space effectively to separate logical sections
Consider data-to-ink ratio to minimize clutter

Accessibility Considerations¶

Description: Making visualizations accessible to all viewers.
Best practices:
Use colorblind-friendly palettes
Ensure sufficient contrast between elements
Avoid relying solely on color to convey information
Make text large enough to be legible
Provide alternative text descriptions for complex figures

2. Technical Implementation¶

Reproducible Visualization Workflows¶

Description: Creating visualizations that can be regenerated from raw data.
Best practices:
Use code-based visualization tools (R, Python) rather than manual tools
Document all preprocessing steps
Version control visualization code
Use consistent random seeds for reproducible results
Share both code and data when possible

File Format Selection¶

Description: Choosing appropriate formats for different use cases.
Best practices:
Use vector formats (SVG, PDF) for publication figures
Use PNG for web display with transparency
Consider EPS for journal submissions
Use HTML+JavaScript for interactive visualizations
Balance resolution and file size for presentations

Software Tool Selection¶

Description: Choosing appropriate visualization tools for different needs.
Best options:
R (ggplot2, plotly, Bioconductor) for statistical and bioinformatics visualization
Python (matplotlib, seaborn, plotly) for data science and machine learning
D3.js for custom interactive web visualizations
Cytoscape for network visualization
IGV/UCSC Genome Browser for genomic data

Automation and Templating¶

Description: Creating reusable visualization templates for consistent reporting.
Best practices:
Create function libraries for common plot types
Use parameterized reports (R Markdown, Jupyter) for reproducible documents
Develop consistent style guides for organizational visualizations
Create reusable templates for regular analyses
Implement automated quality checks for visualizations

This comprehensive guide covers the vast landscape of visualization techniques in biomedical research, from basic expression analysis to cutting-edge AI-assisted visualization approaches. By selecting the most appropriate visualization technique for your specific data type and research question, you can effectively communicate complex findings and gain deeper insights from your biomedical data.