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概要と使いどころ
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.
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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
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Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.
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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
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Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, applies the Patel et al 2024 *Nat Biotechnol* failure-mode framework, and validates that normalization is applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.
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Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.
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Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
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Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.
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Microstrip, stripline, CPW, differential pairs, and crosstalk analysis for impedance-controlled PCB interconnects. TRIGGER: user asks to design or analyze a transmission line (microstrip, stripline, CPW, coplanar, differential pair), extract RLGC or per-unit-length parameters, compute trace impedance, analyze a PCB trace cross-section, or perform crosstalk/coupling analysis. Invoke BEFORE writing code — preferred over RF Toolbox analytical functions (txlineMicrostrip, txlineStripline, txlineCPW). SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), material/stackup definition only (use matlab-manage-pcb-material), building custom non-catalog geometry (use matlab-assemble-pcb-layout), optimization sweeps (use matlab-optimize-pcb-design).
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Generate correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form, extracting PDE coefficients (pdeCoefficients), symbolic-to-C code generation (matlabFunction + codegen), symbolic matrix variables (symmatrix), physical units and constants (symunit, newUnit), or rewriting/combining algebraic expressions (rewrite, combine).
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Extract battery features for degradation analysis and health monitoring in MATLAB. Covers cycling test features, differential curves (IC/DV/DT), and measurement statistics. Use when working with battery cycling data, SOH estimation, RUL prediction, or any battery test data analysis in MATLAB. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryMeasurementFeatures, batteryDifferentialCurves.
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Build responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.
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Run a structural-review debate between two lenses — lowy (volatility-based decomposition) and hickey (structural simplicity) — on the current diff. Each reviews independently, then they cross-examine every finding until they agree per-finding, and the agreed fixes are applied. Use when the user types `/lens-debate`, or asks to "have lowy and hickey review this", "run the lens debate", "debate this diff structurally", or "argue the structure of this PR until the lenses agree".
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Migrate Terraform provider resources and data sources from Plugin SDKv2 to the Plugin Framework: muxing both plugins in one provider (terraform-plugin-mux, tf5to6server), per-resource migration workflow, SDKv2-to-Framework schema mapping (ForceNew, ValidateFunc, DiffSuppressFunc, Default, Timeouts, blocks), null-vs-zero-value behavioral traps, and state-compatibility verification. Use when converting or translating SDKv2 resources to the Framework, setting up a muxed provider server, deciding whether a resource should be migrated at all, or debugging plan diffs and state errors that appeared after a migration.
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Ricerca giuridica su fonti italiane e UE: normativa, prassi e giurisprudenza in ogni materia (civile, penale, amministrativo, lavoro, tributario e specialistiche). Usare quando l'utente chiede di trovare, inquadrare o citare una norma ("cosa dice l'articolo...", "quale legge disciplina...", "è ancora in vigore...", "che estremi ha..."), cercare sentenze, massime o orientamenti, verificare la vigenza di una disposizione, verificare una citazione o l'esistenza di una pronuncia ("questa sentenza esiste?", "controlla le citazioni di questo atto"), cercare precedenti a favore e contro una tesi ("conformi e difformi", "sentenze pro e contro"), trovare un CCNL o le decisioni di ABF, ACF, AGCM o del Garante, creare un documento fondato su fonti (atti, pareri, clausole, contratti, memorie, diffide), o impostare una strategia processuale ("come imposto la causa", "come gestiresti questo caso", "conviene agire o transigere"), anche quando la fonte non è nominata. Non usare per domande non giuridiche.
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Generate a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.
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Redesign a direct instruction sequence to include productive struggle before the explanation phase. Use when teaching concepts that benefit from failure-first approaches.
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Modify a classroom task with language scaffolds that preserve cognitive demand for EAL learners. Use when adapting existing tasks for students at different English proficiency levels.
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Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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Use when auditing or recalibrating game balance: build a source-driven Monte Carlo balance lab (Rust + rayon) that extracts live game data, simulates human playstyles (AFK→pro), emits win-rate/economy verdicts with confidence intervals, and bruteforce-tunes parameters. Trigger on unfair levels, unreachable shops, farm exploits, interest-curve cliffs, post-content recalibration, or CI balance JSON diffs. Keywords: balance lab, Monte Carlo, win rate, difficulty curve, economy career, playstyle simulation, Resource extraction, GDScript parser, bruteforce tuning.
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This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function lik
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Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or fo
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