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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/satijalab/seurat-wrappers/llms.txt

Use this file to discover all available pages before exploring further.

Get Started

Learn what SeuratWrappers is and how it extends Seurat for single-cell genomics.

Installation

Install SeuratWrappers and its method-specific dependencies.

API Reference

Explore the full function reference with parameters and return types.

GitHub

View source code, report issues, and contribute new methods.

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What is SeuratWrappers?

SeuratWrappers is a collection of community-provided methods and extensions for Seurat, curated by the Satija Lab at the New York Genome Center. These integrations provide functionality not yet available in core Seurat and can be updated more frequently, enabling the community to rapidly incorporate new single-cell analysis methods. Each wrapper integrates a published algorithm into the Seurat ecosystem — accepting Seurat objects as input, running the underlying method, and returning results in a Seurat-compatible format.

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Available Methods

Harmony

Fast, scalable dataset integration via iterative correction of PCA embeddings.

LIGER

Integrative non-negative matrix factorization for single-cell data.

Conos

Joint graph-based analysis for mapping between datasets.

fastMNN

Mutual nearest-neighbor batch correction via Bioconductor batchelor.

scVI

Deep generative model for single-cell data integration.

Monocle 3

Pseudotime trajectory inference and cell ordering.

RNA Velocity

Estimate transcriptional dynamics using spliced/unspliced RNA ratios.

ALRA

Zero-preserving imputation using low-rank approximation.

BANKSY

Spatial transcriptomics clustering incorporating neighborhood context.

miQC

Probabilistic quality control for single-cell datasets.

Presto

Fast Wilcoxon rank-sum test for differential expression.

GLM-PCA

Generalized linear model PCA for count data.

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Quick Example

# Install SeuratWrappers
remotes::install_github("satijalab/seurat-wrappers")
library(SeuratWrappers)

# Integrate multiple datasets with Harmony
obj <- RunHarmony(object = merged_seurat, group.by.vars = "orig.ident")

# Impute dropout values with ALRA
obj <- RunALRA(object = obj)

# Infer trajectories with Monocle 3
cds <- as.cell_data_set(obj)

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Contributing

SeuratWrappers is community-driven. If you have developed a method that works with Seurat objects, consider contributing it to the collection. See the contribution guide on GitHub for guidelines.

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