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Data & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
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- 572 Usage Guide F7c88fd3MOFA2 (Multi-Omics Factor Analysis v2) is an unsupervised method for integrating multiple omics layers. It decomposes the data into latent factors that explain shared and view-specific variation.Votes: 0GitHub stars: 9
- 572 Usage Guide F65c534fDetect differential alternative splicing between experimental conditions. Identifies splicing events that change significantly between groups, reporting both statistical significance and effect size (delta PSI).Votes: 0GitHub stars: 9
- 572 Usage Guide F5a6c034Convert between different gene identifier systems (Ensembl, Symbol, Entrez, UniProt) using mygene, biomaRt, and org.db packages for cross-database integration.Votes: 0GitHub stars: 9
- 572 Usage Guide F277565fCNVkit is the standard tool for detecting copy number variants from targeted sequencing (exome, gene panels). It uses both on-target and off-target reads to infer copy number across the genome.Votes: 0GitHub stars: 9
- 572 Usage Guide F2597de7ReactomePA provides pathway enrichment analysis using the Reactome database, a curated peer-reviewed knowledgebase of biological pathways with deep hierarchy and reaction-level detail.Votes: 0GitHub stars: 9
- 572 Usage Guide F1960aadAnalyze cfDNA methylation patterns for cancer detection and tissue-of-origin analysis. Uses bisulfite sequencing or cfMeDIP-seq for methylation profiling.Votes: 0GitHub stars: 9
- 572 Usage Guide E926e31aAnalyze cfDNA fragment size distributions and fragmentomics patterns for cancer detection. Extract nucleosome positioning signals and DELFI-style fragmentation profiles.Votes: 0GitHub stars: 9
- 572 Usage Guide D7260645Effective CNV visualization helps identify patterns, validate calls, and communicate findings. This guide covers genome-wide profiles, region-specific plots, and cohort-level comparisons.Votes: 0GitHub stars: 9
- 572 Usage Guide D4164720Load gene expression count matrices from various quantification tools (featureCounts, Salmon, kallisto, STAR, HTSeq, 10X) into pandas DataFrames for downstream analysis.Votes: 0GitHub stars: 9
- 572 Usage Guide Ca240a8dThis workflow processes raw mass spectrometry data through peak detection, alignment, normalization, statistical analysis, and pathway interpretation.Votes: 0GitHub stars: 9
- 572 Usage Guide C9449acbmixOmics provides multivariate methods for multi-omics integration, including both supervised (DIABLO, sPLS-DA) and unsupervised (sPLS, sPCA) approaches with sparse feature selection.Votes: 0GitHub stars: 9
- 572 Usage Guide B3403d15Population structure analysis identifies genetic ancestry and stratification using PCA (continuous clustering) and ADMIXTURE (discrete ancestry proportions). Essential for GWAS stratification control and ancestry inference.Votes: 0GitHub stars: 9
- 572 Usage Guide A933bee7This skill covers analyzing cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Identify which cell types are communicating and through which signaling pathways.Votes: 0GitHub stars: 9
- 572 Usage Guide A312aea6Quality control of imputed data is essential before downstream analysis, as poor quality imputation can introduce false positives in GWAS and bias effect estimates.Votes: 0GitHub stars: 9
- 572 Usage Guide A2b6f959This workflow integrates multiple molecular data types (transcriptomics, proteomics, metabolomics, etc.) to discover shared biological signals and cross-modal biomarkers.Votes: 0GitHub stars: 9
- 572 Usage Guide 9ed96588Cell-cell communication analysis identifies ligand-receptor interactions between cell types from scRNA-seq data to understand tissue organization, signaling networks, and intercellular coordination.Votes: 0GitHub stars: 9
- 572 Usage Guide 9c01221aR Markdown combines code, results, and narrative into reproducible documents. Output to HTML, PDF, Word, or presentations.Votes: 0GitHub stars: 9
- 572 Usage Guide 963caa6bExplain machine learning predictions on omics data using SHAP values for global and local feature attribution, and LIME for model-agnostic explanations.Votes: 0GitHub stars: 9
- 572 Usage Guide 93678c76Statistical analysis identifies metabolites associated with biological conditions. Methods range from simple univariate tests to multivariate models for biomarker discovery.Votes: 0GitHub stars: 9
- 572 Usage Guide 8edb9336Delimits putative species boundaries from molecular data using complementary approaches: distance-based partitioning (ASAP), tree-based branching rate models (bPTP, GMYC), and full coalescent analysis (BPP). Follows integrative taxonomy best practice by comparing results across multiple methods to identify consensus species. Designed for DNA barcoding datasets (COI, 12S, ITS, matK), cryptic species complexes, and taxonomic validation studies.Votes: 0GitHub stars: 9
- 572 Usage Guide 8ea4f78dDifferential abundance testing identifies taxa that differ significantly between experimental groups while accounting for the compositional nature of microbiome data.Votes: 0GitHub stars: 9
- 572 Usage Guide 8e3e3eacThe enrichplot package provides visualization functions for clusterProfiler results, including dot plots, bar plots, networks, and GSEA-specific plots.Votes: 0GitHub stars: 9
- 572 Usage Guide 8de7bfb9Unsupervised clustering identifies cell populations without predefined gates. Useful for discovery and high-dimensional CyTOF/spectral flow data.Votes: 0GitHub stars: 9
- 572 Usage Guide 861951deThis skill covers creating publication-quality visualizations for differential expression results, including MA plots, volcano plots, PCA plots, and heatmaps. Works with both DESeq2 and edgeR output.Votes: 0GitHub stars: 9