136건 · 관련도순
Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Python (matplotlib, seaborn, plotly) でデータ可視化を行うスキル。 「グラフを作って」「チャート作成」「データを可視化して」等のリクエストで発動。 チャート選定、デザイン原則、アクセシビリティ対応も含む。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Content-derived infographic poster — reads the source file and builds a statistical poster from the facts already in it. Not a dashboard, not a CV, not an AntV template. Uses common-scale encodings (Cleveland–McGill floor), ISOTYPE unit counts (Neurath), custom data glyphs (Lupi/Fragapane), and a lossless data table. Palette, glyphs, and composition are derived from the source's own content. No pies, donuts, gauges, 3D charts, or fabricated KPIs. Part of the reimagine-it Content-Derived Design suite.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Production guidance for converting sourced data and approved claims into truthful, accessible, deterministic animated visualizations with D3. Use for data-driven charts, maps, networks, hierarchies, transitions, annotations, responsive video variants, frame-by-frame browser rendering, and visualization QA; not for generic dashboards, unsourced decorative charts, or ordinary motion graphics without a data contract.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.