'sample' 검색 결과

Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.

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• Estimating participant or sample counts before data collection
• Finding the smallest effect detectable with a fixed sample
• Preparing sample size justifications for grants or protocols

Converts and transcodes audio file formats and encoding parameters using Volcengine LAS. Audio format conversion between wav, mp3, flac, m4a, ogg, aac and other audio formats. Adjusts sample rate (resample, downsample, upsample), bit rate (bitrate), channels (mono, stereo, channel mixing), audio compression, and audio quality settings via ffmpeg parameters. Supports TOS cloud storage paths and local file upload. Use this skill when the user wants to convert audio format (wav/mp3/flac/m4a/ogg/aac), transcode or re-encode audio files, adjust audio sample rate, bitrate, channels, compress audio files, resample or downsample audio, prepare audio for downstream tasks, or do any audio preprocessing.

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Store genomic variant records in TileDB-VCF, add samples over time, and retrieve selected samples or genomic regions. Supports batch reading, allele statistics, quality checks, and VCF/BCF export, with workflows for local storage and TileDB Cloud.

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• Build a variant dataset and add samples incrementally
• Extract variants for selected samples and genomic regions
• Review allele statistics and sample quality

Building or editing Simulink (.slx) models that stream signals in frames using DSP System Toolbox — audio, vibration, radar, comms. Use when the prompt names a Simulink block (Discrete FIR Filter, Buffer, Unbuffer, Rate Transition, Downsample, Upsample, Sample-Rate Converter, FIR Decimation, Time Scope, Spectrum Analyzer, From Multimedia File) or an action (frame-based processing, InputProcessing, buffering, overlap, windowing, STFT, short-time Fourier, decimate, downsample, upsample, resample, multirate, anti-aliasing, tunable filter). Prevents silent numerical errors from sample-vs-frame mismatch, wrong block choice, or missing anti-aliasing.

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Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Prepare and launch nf-core/pacsomatic workflows for matched tumor and normal PacBio HiFi samples. Generate sample sheets and launch files from unaligned BAM inputs, run local checks, submit cluster launchers, and troubleshoot startup failures.

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• Prepare sample sheets for matched tumor and normal samples
• Review generated launch files with a dry run
• Submit a Nextflow launcher to a computing cluster

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Use when reviewing a Rezolus sampler change before merge — a new sampler, a change to an existing sampler's probes/refresh/metrics, or core changes that affect samplers; or whenever a sampler's overhead, cadence, or data source is in question.

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Establishes a disciplined malware sample repository: content-addressed storage by hash, encrypted/password-protected archiving, consistent metadata records, and chain-of-custody tracking so samples are reproducible and safe to handle. Activates for requests to organize a malware repository, manage samples, or track sample metadata and provenance.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Analyze a set of design samples (HTML mockups + website URLs) and produce a structured style guide that anchors the rest of the stardust redesign pipeline. Use when the user supplies reference designs ("design samples", "mockups", "inspiration sites", "style references") and wants to extract a consistent visual language — type system, color palette, spacing, motion, and reusable component patterns — before redesigning a target site. Triggers on phrases like "distill these samples", "extract a style guide from these examples", "use these as design references", or "analyze these mockups". Outputs a `trait-matrix.json` (structured per-sample + cross-sample design tokens) and a `SAMPLES.md` narrative brief that `stardust:direct` reads under Mode B (anchor-reference precedence) when picking a redesign direction.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Build, run, and troubleshoot data processing pipelines with Nextflow and nf-core. The skill guides modular workflow development, input preparation, testing, cached restarts, and configuration for local computers, computing clusters, or cloud environments.

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• Run an nf-core workflow with a samplesheet
• Build and test reusable DSL2 modules
• Investigate failed runs and restart with cached results

Plan experiments before collecting data. Helps choose study designs, randomly assign subjects or samples, organize treatment combinations, and generate reproducible schedules while checking for confounding and repeated measurements incorrectly counted as independent samples.

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• Create random assignments for subjects or samples
• Screen multiple experimental factors
• Balance conditions across batches, days, or plate positions

Validate Illumina BCL run folders and sample sheets, plan demultiplexing—the separation of reads by sample—and convert data to FASTQ using installed tools. Review conversion metrics, index settings, and reads that could not be assigned.

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• Checking run folders and sample sheets before conversion
• Reviewing index, UMI, and lane settings
• Converting Illumina BCL data to FASTQ

Retrieve concise information about microbiome studies, samples, and habitat metadata from MGnify. Narrow requests by study or sample identifier or biome, a type of biological environment, and receive readable summaries or JSON for further processing.

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• Looking up a microbiome study by its identifier
• Summarizing metadata for a specific sample
• Browsing biological environment categories

Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.

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Sets up BMad Samples module in a project. Use when the user requests to 'install samples module', 'configure BMad Samples', or 'setup BMad Samples'.

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Start and manage a Shopify store, from exploring business names and sample catalogs to adding products, updating inventory, checking orders, creating discounts, and reviewing sales. Store connection also supports people creating their first Shopify account and store.

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• Exploring store names, domains, and sample catalogs
• Opening a first online store and registering products
• Organizing product collections and updating inventory

Turn code from the current session into a focused blog walkthrough with a generalized code sample and usage example. Learn the author's style from writing samples, adapt to the destination, and save only after approval.

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• Turning a completed function into a technical blog post
• Matching a tutorial to the author's previous writing
• Preparing a code walkthrough for a gist

Identify and unpack malware compressed with UPX to prepare executable files for static analysis, which examines code without running it. The workflow covers altered header repair, debugger-based recovery, and validation before analysis in Ghidra or IDA.

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• Identifying suspected UPX-packed samples
• Investigating failed UPX decompression
• Recovering binaries with a debugger