Data & Analytics

Process barcoded spatial transcriptomics FASTQ pairs with st_pipeline, preserve upstream artifacts, convert the counts matrix into a standardized raw_counts.h5ad, and hand off cleanly to spatial-preprocess.

Load matrix-level spatial transcriptomics data (including raw_counts.h5ad from spatial-raw-processing), run the current OmicsClaw scanpy-standard preprocessing workflow, and export a downstream-ready AnnData with explicit effective QC parameters plus a standardized preprocessing visualization contract.

Find genes with spatially variable expression patterns using Moran's I, SpatialDE, SPARK-X, or FlashS. Identifies genes whose expression is non-randomly distributed across tissue coordinates.

Pathway and gene-set enrichment analysis for spatial transcriptomics using ORA-style enrichr, preranked GSEA, or ssGSEA with local-first gene-set resolution and method-aware parameter controls.

Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, BANKSY, or CellCharter.

Cell type deconvolution for spatial transcriptomics using FlashDeconv, Cell2location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD, with method-specific parameter hints and standardized proportion outputs.

Differential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.

Compare experimental conditions in spatial transcriptomics data using pseudobulk differential expression with method-aware PyDESeq2 or Wilcoxon testing and explicit replicate handling.

Cell-cell communication analysis for spatial transcriptomics using LIANA, CellPhoneDB, FastCCC, or CellChat, with method-specific parameter hints and standardized ligand-receptor outputs.

Cell type annotation for spatial transcriptomics data using Scanpy marker-gene overlap scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.

Start here for RNA velocity when you have Cell Ranger BAM, loom, or STARsolo Velocyto output. Creates the spliced and unspliced layers needed by scVelo.

Start here if you already have an external single-cell h5ad. Fixes the AnnData contract so downstream OmicsClaw scRNA skills can use it safely.

Review cell quality before filtering. Computes counts, detected genes, mitochondrial percentage, and ribosomal percentage, but does not remove cells.

Merge multiple single-sample scRNA-seq count matrices (from sc-count) into one downstream-ready AnnData with sample labels.

Compress scRNA-seq data into metacell-level summaries using SEACells or a lightweight k-means aggregation fallback.

Infer gene regulatory networks from scRNA-seq using the pySCENIC workflow: GRNBoost2 for adjacency inference, cisTarget-style motif pruning, and AUCell regulon scoring.

Filter cells and genes from single-cell RNA-seq AnnData objects using QC-derived thresholds or tissue presets. This wrapper removes low-quality cells/genes but does not normalize, cluster, or annotate the dataset.

Start here if you have raw single-cell FASTQ files. Checks read quality before counting with FastQC and MultiQC when available, plus a stable local fallback summary.

Differential expression for single-cell RNA-seq using exploratory Scanpy ranking, R-backed MAST, or replicate-aware pseudobulk DESeq2. The wrapper separates cluster/group marker ranking from sample-aware condition DE.

Predict cell differentiation potency from scRNA-seq data using gene expression complexity as a proxy for stemness.

Default scRNA counting route. Turn FASTQ or existing Cell Ranger, STARsolo, SimpleAF / Alevin-fry, or kb-python outputs into a downstream-ready standardized AnnData.

Cell-cell communication analysis for annotated scRNA-seq data using a built-in ligand-receptor scorer, LIANA, CellPhoneDB, CellChat, or a NicheNet R path.

Single-cell ATAC-seq preprocessing with a Signac-style TF-IDF + LSI workflow. Performs cell and peak filtering, top-peak selection, TF-IDF normalization, latent semantic indexing, neighborhood graph construction, UMAP, and Leiden clustering, then exports a downstream-ready AnnData plus a standard OmicsClaw gallery and reproducibility bundle.

Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Site localization, motif analysis, and quantitative PTM analysis with MSstatsPTM.