Data & Analytics

Data analysis, BI, visualization, datasets, statistics, and ML workflows

skills in category: 12,629
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Showing 10,729–10,752 of 12,629 skills

Claw Ancestry PcaAAncestry decomposition PCA against the Simons Genome Diversity ProjectdatapythongoVotes: 0GitHub stars: 2,984
Bulk Wgcna AnalysisAAssist Claude in running PyWGCNA through omicverse—preprocessing expression matrices, constructing co-expression modules, visualising eigengenes, and extracting hub genes.datagoexpressVotes: 0GitHub stars: 2,984
Bulk Deg AnalysisAGuide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment. Use when a user has bulk count matrices and needs differential expression analysis in omicverse.datagoexpressVotes: 0GitHub stars: 2,984
Biomedical Data AnalysisA--> --- name: biomedical-data-analysis description: Omics data forge keywords: - pandas - R-tidyverse - SQL - visualization - reproducible measurable_outcome: Deliver a cleaned dataset + statistical summary + at least one visualization or dashboard spec for each request within 1 working session (≤30 minutes). license: MIT metadata: author: BioSkills Team version: "1.0.0" compatibility: - system: Python 3.9+ / R 4.0+ allowed-tools: - run_shell_command - read_file - python_repl --- Run the cros...datapythonshellVotes: 0GitHub stars: 2,984
Bio Workflows Spatial PipelineA--> --- name: bio-workflows-spatial-pipeline description: End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data. tool_type: python primary_tool: Squidpy workflow: true depends_on: - spatial-transcriptomics/spatial-data-io - spatial-transcriptomics/spatial-preprocessing - spatial-transcriptomics/spatial-neighbors - spatial-transcr...datapythonshellVotes: 0GitHub stars: 2,984
Bio Workflows Rnaseq To DeA--> --- name: bio-workflows-rnaseq-to-de description: End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results. tool_type: mixed primary_tool: DESeq2 workflow: true depends_on: - read-qc/fastp-workflow - rna-quantification/alignment-free-quant - rna-quantification/tximport-workflow - differential-expression/deseq2-basics - ...datagoshellVotes: 0GitHub stars: 2,984
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Bio Workflows Proteomics PipelineA--> --- name: bio-workflows-proteomics-pipeline description: End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with MSstats or limma. Use when processing mass spectrometry proteomics. tool_type: mixed primary_tool: MSstats workflow: true depends_on: - proteomics/data-import - proteomics/proteomics-qc - proteomics/quantification - proteomics/protein-inference - proteomics/different...datashelltestingVotes: 0GitHub stars: 2,984
Bio Workflows Microbiome PipelineA--> --- name: bio-workflows-microbiome-pipeline description: End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data. tool_type: r primary_tool: dada2 workflow: true depends_on: - microbiome/amplicon-processing - microbiome/taxonomy-assignment - microbiome/diversity-analysis - microbiome/differential-abundance measurab...datarustgoVotes: 0GitHub stars: 2,984
Bio Workflows Metabolomics PipelineA--> --- name: bio-workflows-metabolomics-pipeline description: End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data. tool_type: r primary_tool: XCMS workflow: true depends_on: - metabolomics/xcms-preprocessing - metabolomics/metabolite-annotation - metabolomics/normalization-qc - metabolomics/statistical-analysis - metabolomics/pa...datashelltestingVotes: 0GitHub stars: 2,984
Bio Workflows Imc PipelineA--> --- name: bio-workflows-imc-pipeline description: End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end. tool_type: python primary_tool: steinbock workflow: true depends_on: - imaging-mass-cytometry/data-preprocessing - imaging-mass-cytometry/cell-segmentation - imaging-mass-cytometry/phenotyping - imaging-mass-...datapythonshellVotes: 0GitHub stars: 2,984
Bio Workflows Hic PipelineA--> --- name: bio-workflows-hic-pipeline description: End-to-end Hi-C analysis workflow from contact pairs to compartments, TADs, and loops. Covers cooler matrices, cooltools analysis, and visualization. Use when processing Hi-C data to compartments and TADs. tool_type: mixed primary_tool: cooler workflow: true depends_on: - hi-c-analysis/hic-data-io - hi-c-analysis/contact-pairs - hi-c-analysis/matrix-operations - hi-c-analysis/compartment-analysis - hi-c-analysis/tad-detection - hi-c-analys...datapythongoVotes: 0GitHub stars: 2,984
Bio Workflows Gwas PipelineA--> --- name: bio-workflows-gwas-pipeline description: End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies. tool_type: mixed primary_tool: PLINK2 workflow: true depends_on: - population-genetics/plink-basics - population-genetics/population-structure - population-genetics/association-testing - population-genetics/linkage-dise...datashellbashVotes: 0GitHub stars: 2,984
Bio Workflows Cytometry PipelineA--> --- name: bio-workflows-cytometry-pipeline description: End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end. tool_type: r primary_tool: CATALYST workflow: true depends_on: - flow-cytometry/fcs-handling - flow-cytometry/compensation-transformation - flow-cytometry/gating-analysis - flow-cytometry/clus...datapythonshellVotes: 0GitHub stars: 2,984
Bio Workflows Crispr Screen PipelineA--> --- name: bio-workflows-crispr-screen-pipeline description: End-to-end CRISPR screen analysis from FASTQ to hit genes. Orchestrates guide counting, QC, statistical analysis with MAGeCK, and hit calling with multiple methods. Use when analyzing pooled CRISPR screens from count data to hit calling. tool_type: mixed primary_tool: MAGeCK workflow: true depends_on: - crispr-screens/screen-qc - crispr-screens/mageck-analysis - crispr-screens/hit-calling - crispr-screens/library-design - crispr-...datapythongoVotes: 0GitHub stars: 2,984
Bio Workflows Biomarker PipelineA--> --- name: bio-workflows-biomarker-pipeline description: End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data. tool_type: python primary_tool: sklearn workflow: true depends_on: - machine-learning/biomarker-discovery - machine-learning/model-validation - ...datapythongoVotes: 0GitHub stars: 2,984
Bio Variant Calling Joint CallingAJoint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.datagojavaVotes: 0GitHub stars: 2,984
Bio Tcr Bcr Analysis Vdjtools AnalysisACalculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.datapythongoVotes: 0GitHub stars: 2,984
Bio Splicing PipelineA--> --- name: bio-splicing-pipeline description: End-to-end alternative splicing analysis from FASTQ to differential splicing results. Aligns with STAR 2-pass mode, performs junction QC, runs rMATS-turbo for differential analysis, and generates sashimi visualizations. Use when performing comprehensive splicing analysis from raw RNA-seq data. tool_type: mixed primary_tool: rMATS-turbo measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - ...datapythonshellVotes: 0GitHub stars: 2,984
Bio Spatial Transcriptomics Spatial StatisticsACompute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.datapythongoVotes: 0GitHub stars: 2,984
Bio Small Rna Seq Target PredictionA--> --- name: bio-small-rna-seq-target-prediction description: Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs. tool_type: mixed primary_tool: miRanda measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---datapythongoVotes: 0GitHub stars: 2,984
Bio Small Rna Seq Differential MirnaA--> --- name: bio-small-rna-seq-differential-mirna description: Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages. tool_type: r primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---datashellexpressVotes: 0GitHub stars: 2,984
Bio Single Cell Metabolite CommunicationAAnalyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.datapythongoVotes: 0GitHub stars: 2,984
Bio Rna Quantification Tximport WorkflowA--> --- name: bio-rna-quantification-tximport-workflow description: Import transcript-level quantifications from Salmon/kallisto into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when importing transcript counts into R for DESeq2/edgeR. tool_type: r primary_tool: tximport measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Import transcript-level estimates from Salmon, kal...datashellexpressVotes: 0GitHub stars: 2,984
Bio Rna Quantification Count Matrix QcA--> --- name: bio-rna-quantification-count-matrix-qc description: Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis. tool_type: mixed primary_tool: DESeq2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Quality control and exploratory analys...datapythonshellVotes: 0GitHub stars: 2,984