“correlation”的搜索结果

ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.

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Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

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Enforces consistent structured logging with request correlation IDs, standardized log schema, middleware integration, and best practices. Use for "structured logging", "log standardization", "request tracing", or "log correlation".

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Turn data into chart images by selecting a suitable format from 26 options. Visualize trends, comparisons, distributions, flows, or relationships, then receive the image URL and the complete settings used to generate it.

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• Visualize changes over time
• Compare categories and proportions
• Show distributions and correlations

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

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

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• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Process next-generation sequencing data with deepTools: convert alignment files into coverage tracks, normalize signals, compare samples, and check quality. Create heatmaps and profiles around genes or peak regions for ChIP-seq, RNA-seq, and ATAC-seq experiments.

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• Converting BAM files to normalized bigWig tracks
• Checking sequencing quality and replicate agreement
• Plotting heatmaps around transcription start sites

Build an evidence-backed incident timeline in a CTF sandbox by correlating logs, network captures, and other artifacts. Trace attacker actions across sources, distinguish confirmed events from inferred gaps, and preserve the sequence that supports the conclusion.

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• Combine logs and network captures into an incident timeline
• Trace attacker actions from initial access to persistence
• Separate confirmed event order from inferred gaps

Analyze packet captures in a CTF sandbox by rebuilding communication sessions and decoding message contents. Recover transferred files and connect packet order, timing, and reconstructed streams to observed host or malware behavior.

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• Rebuild TCP or UDP communication sessions
• Decode HTTP, DNS, or custom protocol messages
• Recover files transferred over the network

Investigate AWS production incidents using reusable CloudWatch log queries and metric calculations. Narrow affected resources, compare alarms with deployment events, and reconstruct a timeline to identify changes that may explain the symptoms.

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• Investigate error spikes, slow responses, and memory exhaustion
• Compare alarm transitions with recent deployments
• Narrow an incident to affected services and resources

Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.

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• Check whether price and return series are statistically stable
• Test long-term relationships between two assets
• Diagnose regression problems in factor research

Write signal generation code for backtests combining stocks, crypto, forex, and other markets. Apply market-specific indicator settings and volatility-adjusted weights, while the existing CompositeEngine handles trading calendars, shared capital, and each market's rules.

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• Building a backtest combining stocks and crypto
• Applying different indicator settings to each market
• Adjusting portfolio weights for differences in volatility

Assess serverless functions, primarily AWS Lambda, for unsafe execution of external input and unauthorized changes to code or added libraries. Combine source inspection, activity logs, and permission reviews to identify injection risks and recommend detection and prevention measures.

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• Reviewing Lambda event handlers for injection vulnerabilities
• Investigating unauthorized function or layer changes
• Checking privilege escalation through Lambda execution roles

Develop network monitoring for signs that may precede ransomware encryption. Uses detection rules, linked log events, and threat intelligence to identify suspicious activity and define how alerts should be investigated, prioritized, and escalated.

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• Creating detection rules for activity before ransomware encryption
• Linking suspicious logins, scans, and file share access
• Investigating unusual internal network communications

Investigate attackers moving between internal systems by correlating login records with network traffic. Supports detection rules, suspicious SMB and RDP activity analysis, and reports showing attack timelines, affected hosts, and monitoring gaps.

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• Investigating attacker movement across internal systems
• Creating rules for suspicious authentication patterns
• Tracing affected hosts after a PsExec alert

Investigate Entra ID reconnaissance by searching Graph API logs for tool signatures and unusual directory queries. Use KQL, a log query language, to correlate suspicious requests with sign-ins and turn useful searches into Sentinel detection rules.

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• Investigating Graph API activity after suspected credential theft
• Checking whether offensive tool activity is visible in logs
• Creating and tuning scheduled Sentinel detection rules

Analyze DNS logs for signs of hidden command traffic and data theft. Use character randomness, query timing, TXT record inspection, and machine learning to identify suspicious domains and support security monitoring rules.

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• Investigating suspected DNS tunneling
• Classifying algorithmically generated domains
• Detecting periodic DNS beacon traffic