「maps」の検索結果

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

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Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot with 5'UTR / CDS / 3'UTR scaling; MetaPlotR; deepTools `computeMatrix scale-regions`), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (bigWig of log2 IP/input via deepTools `bamCompare`; ggcoverage; pyGenomeTracks; Gviz; IGV / UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), 5'UTR / CDS / 3'UTR stacked-bar feature-distribution summaries, and volcano / MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012 *Nature* 485:201; Meyer 2012 *Cell* 149:1635 — concurrent founding papers from different labs both showed this independently). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features for figure 1, generating DRACH motif logos as sanity checks on the peak set, rendering volcano plots of differential m6A, or reproducing the Dominissini 2012 / Meyer 2012 stop-codon enrichment plot.

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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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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.

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Three.js textures - texture types, UV mapping, environment maps, texture settings. Use when working with images, UV coordinates, cubemaps, HDR environments, or texture optimization.

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Azure Maps SDK for .NET. Location-based services including geocoding, routing, rendering, geolocation, and weather. Use for address search, directions, map tiles, IP geolocation, and weather data.

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Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.

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AutoFixture and NSubstitute Integration Guide - Implementing Auto-Mocking. Use when you need to automatically create mock objects and simplify testing of complex dependency injection. Covers AutoNSubstituteDataAttribute, Frozen mechanism, Greedy construction strategy. Includes customized handling for special dependencies like IMapper (AutoMapper/Mapster). Keywords: autofixture nsubstitute, auto mocking, AutoNSubstituteDataAttribute, auto-mocking, Frozen, AutoNSubstituteCustomization, AutoFixture.AutoNSubstitute, Greedy, fixture.Freeze, Received(), Returns(), IMapper, AutoMapper, Mapster, mapper testing

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Kotlinでデータベースの検索・更新処理を実装するためのパターン集です。JetBrains Exposedのクエリから接続管理、構造変更、テストまで扱います。

• データベースの検索・更新を実装したいとき
• データベース接続の再利用を設定したいとき
• データベース構造の変更管理

採点済みの競合情報を、市場マップや比較表、差別化の余地、優先施策を含むレポートにまとめます。経営層への報告や、チームで競争方針を決める場面に役立ちます。

• 経営層や取締役会向けの競合報告
• 完成済み競合プロフィールの統合
• 差別化の余地と競合脅威の検討

Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.

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Keep a project's verification skill and product/agent access maps aligned with current source and real evidence. Use for /maintain-verification-skill, auditing the verify skill, or reconciling feature and agent access coverage.

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For product and technical management work: turn PRDs, roadmaps, RFCs, architecture reviews, and retros into decision documents. Built around the core query "pm-feature-business-case-deck", with product strategy lead judgment, buyer-ready proof, and this outcome: approve the feature, architecture, roadmap, or incident follow-up.

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Use when auditing a local business website for local SEO. Applies to any business that has a physical address and wants to appear in local search results or Google Maps.

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Interactive quiz that maps your AI/ML knowledge to a starting point in the 523-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"

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Remotion Map animation knowledge

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When the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," "site navigation," "which website builder should I use," "migrate my website," or "add a blog to my existing site." Use this whenever someone is planning what pages a website should have, how they connect, and where to build them. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.

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Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Browser automation CLI using DOMShell MCP server. Maps Chrome's Accessibility Tree to a virtual filesystem for agent-native navigation.

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Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.

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Creates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.

日本語の概要は準備中です。原文の説明を表示しています。