proteomics-de
Differential protein abundance testing using MSstats, limma, proDA, and scipy/statsmodels for Python. Multiple testing correction with BH FDR.
Differential protein abundance testing using MSstats, limma, proDA, and scipy/statsmodels for Python. Multiple testing correction with BH FDR.
Annotate putative doublets in single-cell RNA-seq data using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds. The wrapper preserves the current AnnData matrix semantics, standardizes output columns in `obs`, and exports a reusable figure/table gallery.
Database search for peptide/protein identification using MaxQuant, MS-GF+, Comet, or Mascot.
Find breakthrough insights by forcing unrelated concepts together, detecting meta-patterns across domains, and discovering simplification cascades. When stuck on complex problems. When searching for innovative solutions. When same issue appears in different domains. When complexity feels excessive. When conventional approaches aren't working. When seeking radical simplification.
Feature quantification, missing value imputation, and normalization for metabolomics data.
Bulk RNA-seq count matrix quality control — library sizes, gene detection, sample correlation, outlier detection, CPM normalization.
Mass spectrometry raw data quality control using PTXQC, rawTools, or MSstatsQC.
Metabolomics differential analysis using univariate tests (t-test, FDR), multivariate methods (PCA, PLS-DA, OPLS-DA, sPLS-DA), Random Forest, and ROC analysis for biomarker discovery.
Metabolomics data normalization, scaling and transformation.
Statistical analysis for metabolomics — PCA, PLS-DA, clustering, and univariate tests.
XCMS3 workflow for LC-MS/GC-MS metabolomics preprocessing. Peak detection (CentWave/MatchedFilter), RT alignment (Obiwarp), correspondence, gap filling, and CAMERA adduct/isotope annotation.
Multi-omics query routing and pipeline orchestration across all OmicsClaw domains. Routes natural language queries to the correct analysis skill across spatial transcriptomics, single-cell omics, genomics, proteomics, and metabolomics.
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Site localization, motif analysis, and quantitative PTM analysis with MSstatsPTM.
Compare experimental conditions in spatial transcriptomics data using pseudobulk differential expression with method-aware PyDESeq2 or Wilcoxon testing and explicit replicate handling.
Differential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.
Sets up BMad Samples module in a project. Use when the user requests to 'install samples module', 'configure BMad Samples', or 'setup BMad Samples'.
Use when Antigravity MCP is available locally and you want to query workspaces, check quota, run lightweight Antigravity asks, or generate images through the local antigravity-mcp-server.
Cointegration testing for pairs trading using Engle-Granger, Johansen, and rolling stability analysis
Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens
Atomic state management with auto-dependency tracking
Remove visible Gemini AI watermarks from images via reverse alpha blending. Use for cleaning Gemini-generated images, removing the star/sparkle logo watermark, batch watermark removal.