“sample”的搜索结果

Plan a prospective clinical study by ranking endpoints, estimating participant numbers and statistical power, and assessing feasibility. Outputs include assumptions and named reviewers, providing material for expert review before a protocol is finalized.

中文简介正在准备中。当前显示英语简介。

• Choosing primary and secondary study endpoints
• Estimating sample size with dropout allowance
• Assessing feasibility before a study review

Plan and evaluate product A/B tests with clear hypotheses, success metrics, sample size estimates, and stopping rules. Rank proposed experiments and interpret results using effect sizes and uncertainty to support practical product decisions.

中文简介正在准备中。当前显示英语简介。

• Turn a product improvement idea into a testable hypothesis
• Estimate the sample size needed for an A/B test
• Rank experiments by impact, confidence, and ease

Analyze A/B test results to decide whether to launch a change, extend testing, stop, or investigate further. Check sample size, statistical significance, uncertainty ranges, and effects on other important metrics, then summarize the recommendation and next steps.

中文简介正在准备中。当前显示英语简介。

• Checking whether an A/B test shows a meaningful difference
• Reviewing sample size and test duration
• Deciding whether to launch a tested change

Build Handsontable demo pages for visual regression tests, which compare screenshots for appearance changes. It guides feature routes, URL-driven display states, sample data, and decisions about testing the same feature across framework wrappers.

中文简介正在准备中。当前显示英语简介。

• Adding a demo route for a new feature
• Creating multiple visual states through URL parameters
• Planning visual coverage across framework wrappers

Create or modify Excel workbooks, then independently check the saved file with Python. Verify required sheets, columns, populated cells, row counts, and sample selections against the requested structure before reporting the work as complete.

中文简介正在准备中。当前显示英语简介。

• Creating Excel workbooks from reference data
• Identifying the correct workbook before editing
• Checking required sheets, columns, and row counts

Update or create Excel workbooks with checks against the requested structure. Identify the correct file before editing, then reread the saved workbook to verify sheet names, populated columns, row counts, and selected sample counts.

中文简介正在准备中。当前显示英语简介。

• Identify the correct workbook among similarly named files
• Verify required sheets and columns after editing
• Check selected sample counts and required selection criteria

Plan or run ATAC-seq analysis to identify accessible DNA regions. Check sequencing quality, align reads, detect peaks, combine evidence across replicate samples, and prepare comparisons between conditions with quality summaries and visualization tracks.

中文简介正在准备中。当前显示英语简介。

• Check the quality of ATAC-seq samples
• Detect accessible DNA regions from FASTQ or BAM files
• Build consensus peaks across replicate samples

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

中文简介正在准备中。当前显示英语简介。

• Planning analysis of paired tumor and normal samples
• Reviewing filtering and limitations for tumor-only analysis
• Checking quality in whole-genome, exome, or cancer-panel data

Retrieve study, mutation, clinical, and sample data from cBioPortal and turn the results into concise summaries. Use it to browse studies, inspect molecular profiles, or look up mutations for specified genes.

中文简介正在准备中。当前显示英语简介。

• Find studies by keyword
• Look up mutations for a specified gene
• Review clinical and sample information

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

中文简介正在准备中。当前显示英语简介。

• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

中文简介正在准备中。当前显示原文说明。

Use this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from the full public enum (`DOCA_ARGP_TYPE_STRING`, `_INT`, `_BOOLEAN`, `_DEVICE`, `_DEVICE_REP`, `_DOUBLE` — six values, not three); preserving the standard `--device` / `--representor` / `--json` (`-j`; real flag is `--json`, NOT `--json-config`) / `--sdk-log-level` surface; or debugging `DOCA_ERROR_BAD_STATE` / `INVALID_VALUE` / `NOT_SUPPORTED` / `IO_FAILED` from `doca_argp_*`. Trigger on implicit phrasings: "add a custom flag to a DOCA sample", "should I use getopt here", "BAD_STATE registering a new param", "my JSON config key is rejected", or "my sample's --json is ignored". Refuse and route elsewhere for variadic-flag / subcommand / shell-completion features, DOCA Core context, or DOCA Log internals.

中文简介正在准备中。当前显示原文说明。

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

中文简介正在准备中。当前显示原文说明。

Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.

中文简介正在准备中。当前显示原文说明。

Use when generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.

中文简介正在准备中。当前显示原文说明。

Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.

中文简介正在准备中。当前显示原文说明。

Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

中文简介正在准备中。当前显示原文说明。

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

中文简介正在准备中。当前显示原文说明。

Populates Dataverse tables with sample records for testing and demoing a Power Pages site. Use when the user wants to add sample data, seed data, generate test records, or insert demo data into their tables.

中文简介正在准备中。当前显示原文说明。

Use when the user wants to seed Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order. Mirrors microsoft/power-platform-skills/power-pages/add-sample-data, adapted for mobile apps.

中文简介正在准备中。当前显示原文说明。

A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.

中文简介正在准备中。当前显示原文说明。

Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.

中文简介正在准备中。当前显示原文说明。

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz

中文简介正在准备中。当前显示原文说明。

Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.

中文简介正在准备中。当前显示原文说明。