Data & Analytics

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

skills in category: 12,625
pages available: 527

Security grades appear on each card once the skill has been scanned. Newly imported skills may briefly show without a grade until the backfill job runs.

Open in full browser

Browse data & analytics skills

Showing 12,145–12,168 of 12,625 skills

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: 32
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: 32
Mhc Binding PredictionAPredict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.datapythonbashVotes: 0GitHub stars: 32
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: 32
Quality MetricsAQuality 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.datapythongoVotes: 0GitHub stars: 32
Myeloma MRD AgentAAI-powered minimal residual disease (MRD) analysis for multiple myeloma using next-generation flow cytometry, NGS, and mass spectrometry approaches.datapythongoVotes: 0GitHub stars: 32
Skills DirectorySkills Directory 3 slots · $399/mo · one ad per page · sponsors can never buy grades.See placements
Fcs HandlingARead and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.dataexpressVotes: 0GitHub stars: 32
Compensation TransformationASpillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.dataexpressVotes: 0GitHub stars: 32
Bead NormalizationABead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.dataexpresstestingVotes: 0GitHub stars: 32
Genotype ImputationAImpute missing genotypes using reference panels with Beagle or Minimac4. Use when increasing variant density for GWAS, harmonizing data across genotyping platforms, or inferring variants not directly typed in array data.datapythongoVotes: 0GitHub stars: 32
Long Read QcAQuality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.datapythonbashVotes: 0GitHub stars: 32
Coverage AnalysisACalculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.datapythonbashVotes: 0GitHub stars: 32
StravaCTrack athletic activities with Strava - view workouts, analyze performance, and access training historydatagobashVotes: 0GitHub stars: 207
Bedgraph HandlingACreate, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.datapythonbashVotes: 0GitHub stars: 32
Bed File BasicsABED file format fundamentals, creation, validation, and basic operations. Covers BED3 through BED12 formats, coordinate systems, sorting, and format conversion using bedtools and pybedtools. Use when working with genomic coordinates or preparing interval files for downstream tools.datapythonbashVotes: 0GitHub stars: 32
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.datajavabashVotes: 0GitHub stars: 32
Filtering Best PracticesAComprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.datapythongoVotes: 0GitHub stars: 32
Spatial ProteomicsAAnalyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.datapythonexpressVotes: 0GitHub stars: 32
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: 32
Image AnalysisAProcess and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.datapythonexpressVotes: 0GitHub stars: 32
Trajectory InferenceAInfer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.datapythonbashVotes: 0GitHub stars: 32
Scatac AnalysisASingle-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.datapythongoVotes: 0GitHub stars: 32
Perturb SeqAAnalyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.datapythongoVotes: 0GitHub stars: 32
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: 32