Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper.
Systematic workflow for clustering biological samples, features, or any quantitative data matrix. Implements multiple clustering algorithms with rigorous validation, comparison, and interpretation to identify meaningful data groupings.
Skill "Cell-Cell Communication Analysis (CellChat)" from TianGzlab/OmicsClaw, covering cell-cell communication analysis (cellchat v2), when to use this skill, installation, inputs and outputs.
Compare two groups of experiments to identify differential peak regions (DPR) or differentially methylated regions (DMR) using the ChIP-Atlas Diff Analysis API.
Skill "ClinicalTrials.gov Disease Landscape Scanner" from TianGzlab/OmicsClaw, covering clinicaltrials.gov disease landscape scanner, when to use this skill, installation, inputs and outputs.
Build weighted gene co-expression networks to identify modules of coordinately expressed genes and discover hub genes that may be key regulators. This workflow uses WGCNA (Weighted Gene Co-expression Network Analysis) to group genes into modules based on their expression patterns across samples, then correlates these…
Comprehensive workflow for statistical experimental design in genomics, from power analysis and sample size determination to batch-balanced experimental layouts and multiple testing strategy.
Infer gene regulatory networks (GRNs) de novo from single-cell RNA-seq data using pySCENIC. This workflow discovers transcription factor (TF) regulons directly from expression patterns and calculates cell-level TF activity scores.
Identify genes whose genetically regulated expression is associated with disease risk, determine therapeutic directionality (inhibit vs. activate), and prioritize drug targets with causal genetic evidence using Transcriptome-Wide Association Study (TWAS) analysis.
Select minimal, interpretable biomarker panels from high-dimensional omics data using penalized logistic regression (LASSO/elastic net) with nested cross-validation and stability selection.
Search Consensus (consensus.app) for preclinical studies on a molecular target in a disease, then extract structured in vitro and in vivo experiment details from each paper.
Identify latent factors driving variation across 2+ omics layers using MOFA+ (Multi-Omics Factor Analysis). Decomposes multi-omics data into interpretable factors, each capturing shared or view-specific biological signal. Handles missing data across views natively.
Analyze pooled CRISPR screens with single-cell RNA-seq readout using a tiered workflow: fast screening → target validation → rigorous differential expression.
Differential protein expression analysis for TMT/LFQ mass spectrometry proteomics data using limma linear models with DEqMS PSM-count-aware variance correction.
Complete workflow for single-cell RNA-seq analysis using Scanpy and the scverse ecosystem. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.