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Found 9 Skills
Guide Claude through omicverse's single-cell clustering workflow, covering preprocessing, QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration as demonstrated in t_cluster.ipynb and t_single_batch.ipynb.
Walk through omicverse's single-cell preprocessing tutorials to QC PBMC3k data, normalise counts, detect HVGs, and run PCA/embedding pipelines on CPU, CPU–GPU mixed, or GPU stacks.
Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.
Run omicverse's CellPhoneDB v5 wrapper on annotated single-cell data to infer ligand-receptor networks and produce CellChat-style visualisations.
Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.
Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.
Assist Claude in running PyWGCNA through omicverse—preprocessing expression matrices, constructing co-expression modules, visualising eigengenes, and extracting hub genes.
Walk Claude through PyDESeq2-based differential expression, including ID mapping, DE testing, fold-change thresholding, and enrichment visualisation.
Guide Claude through SCSA, MetaTiME, CellVote, CellMatch, GPTAnno, and weighted KNN transfer workflows for annotating single-cell modalities.