2,361 件 ・ 関連度順
概要と使いどころ
Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metada
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。
This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enric
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。
The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualizati
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。
topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and
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。
This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract biclus
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。
R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.
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。
Expert knowledge for Azure Traffic Manager development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when configuring Traffic Manager profiles, routing methods, IaC (ARM/Bicep/Terraform), Traffic View, or nested designs, and other Azure Traffic Manager related development tasks. Not for Azure Front Door (use azure-front-door), Azure Load Balancer (use azure-load-balancer), Azure Application Gateway (use azure-application-gateway), Azure Virtual WAN (use azure-virtual-wan).
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。
Decode any Base transaction into a plain-English story - method, token movements, swaps/approvals, counterparties, and suspicious-approval flags. Keyless via Base RPC + Etherscan v2.
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。
Amazon FBA and FBM shipping cost calculator and fulfillment optimization. Dimensional weight analysis, storage fee calculation, removal cost estimation, and fulfillment method comparison. Use when the user asks about Amazon shipping costs, FBA fees, fulfillment costs, storage fees, or shipping calculations.
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。
Use ONLY when calling the `browser_execute` tool or driving a real browser via the Chrome DevTools Protocol. Required reading before the first `browser_execute` call in a session. Covers the three connection methods (local Chrome with remote debugging, isolated debug-port profile, Browser Use cloud), the in-process `session` / `console` snippet model, attaching to a page target, common CDP commands, the per-project `.bcode/agent-workspace/` for reusable scripts, and screenshot auto-attachment.
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。
Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".
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。
April Dunford's 10-step positioning methodology for B2B tech products. Use when customers don't understand the product, sales cycles are long because reps must "explain it," prospects compare you to wrong competitors, or you face price pressure despite a good product. Covers competitive alternatives, unique attributes, value themes, target market characteristics, market category (Head to Head / Big Fish Small Pond / New Game), and trend layering.
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。
Applies Neil Rackham's SPIN methodology (Situation/Problem/Implication/Need-payoff questions) to major B2B sales. Use for complex multi-call sales cycles, enterprise deals where the customer must justify the decision to others, when objections are mounting, when calls end in vague continuations instead of advances, when traditional closing techniques are backfiring on large deals, or when designing discovery-call structure. Triggers include 'my deal isn't closing', 'too many objections', 'B2B sales coaching', 'discovery call structure', 'stuck in the middle of the sale'. Not for transactional sub-$50 sales, pure consumer impulse, or PLG self-serve products.
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。
SOLID principles checklist with Java examples. Use when a class has too many responsibilities, an abstraction leaks, or a dependency points the wrong way, and when the user asks about Single Responsibility, Open/Closed, Liskov, Interface Segregation or Dependency Inversion. For naming, duplication and method length, use clean-code instead.
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。
Clean Code principles (DRY, KISS, YAGNI), naming, function design and readability. Use when code is hard to read, with long methods, unclear names, duplication or deep nesting. For how responsibilities are split across classes and which way dependencies point, use solid-principles instead.
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。
Interactive idea refinement using Socratic method to develop fully-formed designs
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。
Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.
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。
Plan, triage, and write academic rebuttals and review responses. Use when an AI research agent needs to handle conference, journal, workshop, or grant reviews; classify reviewer concerns; decide what evidence or experiments can change the decision; write an AC-facing executive summary; draft per-reviewer responses; correct factual misunderstandings without sounding hostile; handle novelty, baseline, method, experiment, clarity, and scope objections; or produce a final rebuttal checklist.
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。
Генерация и актуализация перехватчика метода в расширении 1С (CFE). Используй когда нужно перехватить метод заимствованного объекта — вставить код до, после, вместо оригинала, изменить его тело (ИзменениеИКонтроль) — или актуализировать перехватчик после изменения оригинала
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。
Perform bulk code refactoring operations like renaming variables/functions across files, replacing patterns, and updating API calls. Use when users request renaming identifiers, replacing deprecated code patterns, updating method calls, or making consistent changes across multiple locations.
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。
Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube videos, Bilibili videos, and similar content platforms. Use when the user needs headline/title ideation, batch title generation, platform-specific title adaptation, title-library reuse, title selection, title scoring, or a reusable title workflow based on universal formulas plus separated platform methods such as 公众号, X, YouTube, and B站.
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。
Use when generating mocks, stubbing methods, verifying interactions, capturing arguments, or choosing between mocks, fakes, and real objects (Mockito).
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。
Use when creating mocks, stubbing methods, verifying interactions, registering fallback values, or choosing between mocks, fakes, and real objects (Mocktail).
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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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。