525 skills · sorted by relevance
Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Covers field references, webhook inputs, dates, strings, and choosing between expressions and Code nodes for data transformations.
Predict spatial gene expression from H&E-stained tissue images using DeepSpot-M. Query a released panel of about 19,000 genes and combine predictions from small image tiles into slide-wide maps for research analysis.
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Guide for integrating GTEx tissue expression data with ENCODE regulatory elements. Use when users need to check if a gene is expressed in a tissue, correlate regulatory elements with expression, or validate ENCODE findings against GTEx. Trigger on: GTEx, tissue expression, gene expression levels, expression atlas, eQTL, tissue-specific expression, TPM values.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.
Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.
Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, mapping data between nodes, or referencing webhook data in workflows. Use this skill whenever configuring node fields that reference data from previous nodes — expressions are how n8n passes data between nodes, and getting the syntax wrong is the most common source of workflow errors. Also use when asked whether a complex expression hurts performance.
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Controls facial expressions in Seedance 2.0 with FACS (Facial Action Coding System) Action Unit codes — muscle-level direction (AU12 = lip-corner puller, AU6 = cheek raiser) instead of emotion labels. Use whenever the user wants precise facial acting, a forced/uncanny/mixed expression, micro-performance in a close-up, monologue or dialogue facial beats, a 'which AU code for anger/fear/disgust' answer, or to generate a FACS reference sheet for a character. Pairs with higgsfield-soul Micro-Expressions (named expressions), higgsfield-audio (dialogue + lip-sync), and higgsfield-gpt-image-2 (the reference-sheet image).
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Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.
Query public single-cell and spatial gene expression data by tissue, disease, or cell type. Retrieve selected expression values, cell metadata, and summary counts for analysis and comparisons across datasets without first downloading entire collections.
Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.
Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.
Plan or run bulk RNA-seq analysis to find genes whose expression differs between conditions. Check sample replication, study design, batch effects, and data quality, then produce comparison tables, diagnostic plots, and ranked gene lists.
Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
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Use when optimizing the performance of an existing native scalar expression in the datafusion-comet-spark-expr crate (native/spark-expr/) — casts, string/JSON/array/math kernels that run per-row or per-batch. Covers benchmarking, keeping output bit-identical, and the no-regression gate. Not for adding new expressions (use implement-comet-expression) or wiring upstream functions (use wire-datafusion-function).
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Use when picking the next Spark expression to implement natively in Comet, or when asked whether a specific codegen-dispatched expression is worth a native Rust implementation. Scores the candidate on how likely a native path can be 100% Spark-compatible and how much throughput or allocation it would save versus the JVM codegen dispatcher, records the verdict in the per-expression audit log so disqualified candidates are not re-litigated, and files a GitHub issue for the recommendation.
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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.
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Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.
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Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
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Evaluate and simplify Boolean expressions using truth tables, algebraic laws (De Morgan, distributive, absorption, idempotent, consensus), and Karnaugh maps for up to six variables. Use when you need to reduce a Boolean expression to its minimal sum-of-products or product-of-sums form, verify logical equivalence between two expressions, or prepare a minimized function for gate-level implementation.
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