Create a Seurat object
Build an RNA Seurat object from a feature-by-cell count matrix and matching cell metadata, with identifier checks.
Seurat and Python notebooks for building objects, QC, normalization, scoring, markers, trajectories, dynamics, and network analysis.
Build an RNA Seurat object from a feature-by-cell count matrix and matching cell metadata, with identifier checks.
Apply cell-level QC thresholds to a Seurat object and save the filtered object.
Call doublets with scDblFinder, add the calls to a Seurat object, and record the exclusion rule.
Normalize filtered RNA data, select variable features, scale the assay, and run PCA.
Normalize a filtered Seurat object with SCTransform and expose the assay, covariates, and feature-retention choices.
Run Harmony on an existing PCA-ready Seurat object using a chosen batch variable.
Score gene signatures with UCell and store the scores in the Seurat object.
Run CytoTRACE2 on raw RNA counts, add potency outputs to a Seurat object, and check cell-ID alignment.
Compare two named cell groups with Seurat FindMarkers and export a compact marker table.
Summarize Slingshot calls for reduced dimensions, cluster labels, pseudotime, and curve weights.
Fit negative-binomial tradeSeq GAMs to raw counts along a specified trajectory and pseudotime.
Compute RNA velocity from an AnnData object with cell-aligned spliced and unspliced layers using the dynamical model.
Combine velocity and connectivity kernels in CellRank and run GPCCA fate inference on an AnnData object.
Infer transcription-factor or pathway activity with decoupler ULM using AnnData expression and a prior-knowledge network.
Construct co-expression modules and module eigengenes with hdWGCNA from a processed Seurat object.
Estimate metabolic-task activity with scCellFie from AnnData and save an enriched object with a grouped report.