stellaromics
Official@stellaromics
Offers reproducible bioinformatics pipelines, multi-omics integration, and interactive scientific reporting for high-throughput genomic and spatial transcriptomics research.
Agent Skills by stellaromics
Showing 140 vetted skills indexed across 1 GitHub repositories.
stellaromicsbio-workflows-hic-pipeline
Automate Hi-C data analysis from raw reads to genome features.
stellaromicsbio-machine-learning-atlas-mapping
Maps query single-cell data to reference atlases using scArches transfer learning.
stellaromicsbio-workflows-rnaseq-to-de
Automate RNA-seq analysis from FASTQ to differential expression results.
stellaromicsbio-reporting-quarto-reports
Create reproducible multi-language scientific reports with Quarto.
stellaromicsbio-data-visualization-interactive-visualization
Generate interactive omics visualizations with plotly and bokeh.
stellaromicsbio-crispr-screens-base-editing-analysis
Quantify base and prime editing outcomes from CRISPR amplicon sequencing data.
stellaromicsbio-workflows-imc-pipeline
Process IMC data from raw acquisitions to spatial cell analysis.
stellaromicsbio-pathway-enrichment-visualization
Generate publication-ready dotplots, barplots, and network plots from clusterProfiler enrichment results.
stellaromicsbio-crispr-screens-library-design
Design CRISPR sgRNA libraries with off-target filtering and oligo design.
stellaromicsbio-crispr-screens-batch-correction
Normalize and correct batch effects in CRISPR screen data using Python.
stellaromicsbio-gene-regulatory-networks-multiomics-grn
Infer enhancer-driven gene regulatory networks from paired scRNA-seq and scATAC-seq data using SCENIC+.
stellaromicsbio-data-visualization-circos-plots
Generate circular genome visualizations with Circos and pyCircos multi-track plots.
stellaromicsbio-single-cell-cell-annotation
Annotate single-cell cell types using reference models and trained classifiers.
stellaromicsbio-crispr-screens-jacks-analysis
Analyzes multiple CRISPR knockout screens using JACKS inference in Python to model sgRNA efficacy and gene essentiality.
stellaromicsbio-spatial-transcriptomics-pyxa-starmap
Loads Pyxa STARmap spatial transcriptomics data into AnnData objects with 3D coordinates and metadata.
stellaromicswasm-compatibility
Scan marimo notebook dependencies and runtime patterns for WASM compatibility.
stellaromicsbio-spatial-transcriptomics-spatial-visualization
Generate tissue plots colored by gene expression, clusters, and annotations with histology background.
stellaromicsbio-data-visualization-genome-browser-tracks
Automate genome browser visualizations from bigWig, BED, and GTF tracks.
stellaromicsbio-data-visualization-heatmaps-clustering
Generate clustered heatmaps from gene expression or omics matrices with annotations.
stellaromicsbio-reporting-figure-export
Export matplotlib and ggplot2 figures to PDF, SVG, PNG, and TIFF formats.
stellaromicsbio-single-cell-multimodal-integration
Integrate RNA with protein or chromatin data for joint single-cell clustering.
stellaromicsbio-clinical-databases-dbsnp-queries
Query dbSNP for rsID lookups and variant annotations via myvariant.info and NCBI Entrez APIs.
stellaromicsbio-pathway-reactome
Identify enriched Reactome pathways in gene lists using ReactomePA.
stellaromicsbio-workflows-biomarker-pipeline
Automate biomarker discovery from omics data to validated panels and classifiers.
Frequently Asked Questions About stellaromics
FAQPage SchemaWhat specific biological tasks can I perform using these pipelines?▼
You can execute end-to-end analysis for RNA-seq, ATAC-seq, CRISPR screens, spatial transcriptomics, and multi-omics integration. These pipelines handle everything from raw read processing and QC to differential expression, variant calling, and gene regulatory network inference.
Who is the target persona for these bioinformatics resources?▼
These resources are designed for computational biologists, bioinformaticians, and research scientists who require reproducible, scalable analysis of high-throughput sequencing data and interactive visualization of complex biological datasets.
What are the prerequisites for running these analysis pipelines?▼
Users require a standard Unix-based environment with access to core bioinformatics software like Seurat, Scanpy, Squidpy, and MAGeCK. Most pipelines are designed for execution within interactive notebook environments or as modular command-line processes.