Seurat and Python notebooks for building objects, QC, normalization, scoring, markers, trajectories, dynamics, and network analysis.

16 notebooks · view or download the QMD files below

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Create a Seurat object

Source-backed Draft

Build an RNA Seurat object from a feature-by-cell count matrix and matching cell metadata, with identifier checks.

Single-cell quality control

Source-backed Draft

Apply cell-level QC thresholds to a Seurat object and save the filtered object.

Doublet detection with scDblFinder

Source-backed Draft

Call doublets with scDblFinder, add the calls to a Seurat object, and record the exclusion rule.

Log-normalization and variable features

Source-backed Draft

Normalize filtered RNA data, select variable features, scale the assay, and run PCA.

SCTransform normalization

Source-backed Draft

Normalize a filtered Seurat object with SCTransform and expose the assay, covariates, and feature-retention choices.

Harmony batch integration

Source-backed Draft

Run Harmony on an existing PCA-ready Seurat object using a chosen batch variable.

UCell signature scoring

Source-backed Draft

Score gene signatures with UCell and store the scores in the Seurat object.

CytoTRACE2 cell potency

Source-backed Draft

Run CytoTRACE2 on raw RNA counts, add potency outputs to a Seurat object, and check cell-ID alignment.

Single-cell marker comparison

Source-backed Draft

Compare two named cell groups with Seurat FindMarkers and export a compact marker table.

Slingshot trajectory inference

API reminder Draft

Summarize Slingshot calls for reduced dimensions, cluster labels, pseudotime, and curve weights.

tradeSeq trajectory-associated testing

Source-backed Draft

Fit negative-binomial tradeSeq GAMs to raw counts along a specified trajectory and pseudotime.

scVelo RNA velocity

Source-backed Draft

Compute RNA velocity from an AnnData object with cell-aligned spliced and unspliced layers using the dynamical model.

CellRank velocity-kernel fate inference

Source-backed Draft

Combine velocity and connectivity kernels in CellRank and run GPCCA fate inference on an AnnData object.

Python decoupler ULM activity inference

Source-backed Draft

Infer transcription-factor or pathway activity with decoupler ULM using AnnData expression and a prior-knowledge network.

hdWGCNA co-expression modules

Source-backed Draft

Construct co-expression modules and module eigengenes with hdWGCNA from a processed Seurat object.

scCellFie metabolic activity

Source-backed Draft

Estimate metabolic-task activity with scCellFie from AnnData and save an enriched object with a grouped report.