Data & Analytics

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

skills in category: 13,012
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Showing 1,801–1,824 of 13,012 skills

Bio Workflows Multi Omics PipelineAEnd-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.datagoexpressVotes: 0GitHub stars: 2
Bio Workflows Microbiome PipelineAEnd-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.datarustgoVotes: 0GitHub stars: 2
Bio Workflows Methylation PipelineAEnd-to-end bisulfite sequencing workflow from FASTQ to differentially methylated regions. Covers Bismark alignment, methylation calling, and DMR detection with methylKit. Use when analyzing bisulfite sequencing data.datagobashVotes: 0GitHub stars: 2
Bio Workflows Metagenomics PipelineAEnd-to-end metagenomics workflow from FASTQ to taxonomic and functional profiles. Covers Kraken2 classification, Bracken abundance estimation, and HUMAnN functional profiling. Use when profiling metagenomic samples.datapythonbashVotes: 0GitHub stars: 2
Bio Workflows Metabolomics PipelineAEnd-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.datatestingdatabaseVotes: 0GitHub stars: 2
Bio Workflows Imc PipelineAEnd-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.datapythonbashVotes: 0GitHub stars: 2
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Bio Workflows Hic PipelineAEnd-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.datapythongoVotes: 0GitHub stars: 2
Bio Workflows Gwas PipelineAEnd-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.databashtestingVotes: 0GitHub stars: 2
Bio Workflows Expression To PathwaysAWorkflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.datagoreactVotes: 0GitHub stars: 2
Bio Workflows Cytometry PipelineAEnd-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.datapythonexpressVotes: 0GitHub stars: 2
Bio Workflows Crispr Screen PipelineAEnd-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.datapythonbashVotes: 0GitHub stars: 2
Bio Workflows Cnv PipelineAEnd-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data.databashVotes: 0GitHub stars: 2
Bio Workflows Chipseq PipelineAEnd-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3, and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.datagobashVotes: 0GitHub stars: 2
Bio Workflows Atacseq PipelineAEnd-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.datagobashVotes: 0GitHub stars: 2
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
Bio Tcr Bcr Analysis Scirpy AnalysisAAnalyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with cell state analysis.datapythongoVotes: 0GitHub stars: 2
Bio Tcr Bcr Analysis Repertoire VisualizationACreate publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.datapythonbashVotes: 0GitHub stars: 2
Bio Tcr Bcr Analysis Mixcr AnalysisAPerform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.datapythongoVotes: 0GitHub stars: 2
Bio Tcr Bcr Analysis Immcantation AnalysisAAnalyze BCR repertoires for somatic hypermutation, clonal lineages, and B cell phylogenetics using the Immcantation framework. Use when studying B cell affinity maturation, germinal center dynamics, or antibody evolution.dataVotes: 0GitHub stars: 2
Bio Structural Biology Modern Structure PredictionAPredict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.datapythonbashVotes: 0GitHub stars: 2
Bio Sra DataADownload sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads.datapythonbashVotes: 0GitHub stars: 2
Bio Spatial Transcriptomics Spatial VisualizationAVisualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.datapythonexpressVotes: 0GitHub stars: 2
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
Bio Spatial Transcriptomics Spatial MultiomicsAAnalyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.datapythongoVotes: 0GitHub stars: 2