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Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 1,849–1,872 of 13,012 skills
- Bio Proteomics Dia AnalysisData-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.Votes: 0GitHub stars: 2
- Bio Proteomics Data ImportLoad and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.Votes: 0GitHub stars: 2
- Bio Population Genetics Scikit Allel AnalysisPython population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.Votes: 0GitHub stars: 2
- Bio Population Genetics Plink BasicsPLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files or running QC.Votes: 0GitHub stars: 2
- Bio Phylo Modern Tree InferenceBuild maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.Votes: 0GitHub stars: 2
- Bio Phylo Distance CalculationsCompute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, or generating bootstrap consensus trees.Votes: 0GitHub stars: 2
- Bio Pdb Structure ModificationModify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.Votes: 0GitHub stars: 2
- Bio Multi Omics Mofa IntegrationMulti-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.Votes: 0GitHub stars: 2
- Bio Multi Omics Mixomics AnalysisSupervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.Votes: 0GitHub stars: 2
- Bio Multi Omics Data HarmonizationPreprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.Votes: 0GitHub stars: 2
- Bio Microbiome Taxonomy AssignmentTaxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.Votes: 0GitHub stars: 2
- Bio Microbiome Differential AbundanceDifferential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.Votes: 0GitHub stars: 2
- Bio Methylation MethylkitDNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.Votes: 0GitHub stars: 2
- Bio Methylation Dmr DetectionDifferentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.Votes: 0GitHub stars: 2
- Bio Metagenomics Strain TrackingTrack bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics.Votes: 0GitHub stars: 2
- Bio Metabolomics Xcms PreprocessingXCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.Votes: 0GitHub stars: 2
- Bio Metabolomics Targeted AnalysisTargeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.Votes: 0GitHub stars: 2
- Bio Metabolomics Statistical AnalysisStatistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.Votes: 0GitHub stars: 2
- Bio Metabolomics LipidomicsSpecialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.Votes: 0GitHub stars: 2
- Bio Longread AlignmentAlign long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.Votes: 0GitHub stars: 2
- Bio Imaging Mass Cytometry Spatial AnalysisSpatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.Votes: 0GitHub stars: 2
- Bio Imaging Mass Cytometry Quality MetricsQuality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.Votes: 0GitHub stars: 2
- Bio Imaging Mass Cytometry PhenotypingCell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.Votes: 0GitHub stars: 2
- Bio Imaging Mass Cytometry Interactive AnnotationInteractive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.Votes: 0GitHub stars: 2