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Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 1,825–1,848 of 13,012 skills
- Bio Spatial Transcriptomics Spatial DomainsIdentify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.Votes: 0GitHub stars: 2
- Bio Spatial Transcriptomics Spatial DeconvolutionEstimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.Votes: 0GitHub stars: 2
- Bio Spatial Transcriptomics Spatial Data IoLoad spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.Votes: 0GitHub stars: 2
- Bio Spatial Transcriptomics Spatial CommunicationAnalyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.Votes: 0GitHub stars: 2
- Bio Spatial Transcriptomics Image AnalysisProcess 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.Votes: 0GitHub stars: 2
- Bio Small Rna Seq Target PredictionPredict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs.Votes: 0GitHub stars: 2
- Bio Small Rna Seq Smrna PreprocessingPreprocess small RNA sequencing data with adapter trimming and size selection optimized for miRNA, piRNA, and other small RNAs. Use when preparing small RNA-seq reads for downstream quantification or discovery analysis.Votes: 0GitHub stars: 2
- Bio Small Rna Seq Mirge3 AnalysisFast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing.Votes: 0GitHub stars: 2
- Bio Small Rna Seq Mirdeep2 AnalysisDiscover novel miRNAs and quantify known miRNAs using miRDeep2 de novo prediction from small RNA-seq data. Use when identifying new miRNAs or performing comprehensive miRNA profiling with discovery.Votes: 0GitHub stars: 2
- Bio Small Rna Seq Differential MirnaPerform 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.Votes: 0GitHub stars: 2
- Bio Single Cell Trajectory InferenceInfer 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.Votes: 0GitHub stars: 2
- Bio Single Cell Perturb SeqAnalyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.Votes: 0GitHub stars: 2
- Bio Single Cell Lineage TracingReconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.Votes: 0GitHub stars: 2
- Bio Single Cell Data IoRead, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.Votes: 0GitHub stars: 2
- Bio Single Cell Cell CommunicationInfer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis. Use when inferring ligand-receptor interactions between cell types.Votes: 0GitHub stars: 2
- Bio Sequence PropertiesCalculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.Votes: 0GitHub stars: 2
- Bio Ribo Seq Orf DetectionDetect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.Votes: 0GitHub stars: 2
- Bio Read Qc Quality ReportsGenerate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.Votes: 0GitHub stars: 2
- Bio Proteomics Spectral LibrariesBuild, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.Votes: 0GitHub stars: 2
- Bio Proteomics QuantificationProtein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.Votes: 0GitHub stars: 2
- Bio Proteomics Ptm AnalysisPost-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.Votes: 0GitHub stars: 2
- Bio Proteomics Proteomics QcQuality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.Votes: 0GitHub stars: 2
- Bio Proteomics Peptide IdentificationPeptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.Votes: 0GitHub stars: 2
- Bio Proteomics Differential AbundanceStatistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.Votes: 0GitHub stars: 2