> ## 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. # Introduction > SeuratWrappers is a curated collection of community-provided extensions that bring cutting-edge single-cell analysis methods into the Seurat ecosystem. SeuratWrappers is a collection of community-provided methods and extensions for [Seurat](https://satijalab.org/seurat/), curated by the Satija Lab at the New York Genome Center (NYGC). These integrations provide functionality not yet available in core Seurat and can be updated independently and more frequently — allowing the community to rapidly incorporate newly published single-cell analysis methods without waiting for a Seurat release. Install SeuratWrappers and per-method dependencies. Browse source code, open issues, and contribute new methods. ## Why SeuratWrappers exists Seurat is a widely used framework for single-cell RNA sequencing (scRNA-seq) analysis. Its core package provides a stable, well-tested API for common operations: normalization, dimensionality reduction, clustering, and visualization. However, the single-cell field moves quickly. New methods for batch correction, trajectory inference, imputation, and spatial analysis are published continuously. SeuratWrappers bridges this gap by: * **Extending Seurat** with methods that are valuable but outside the scope of the core package * **Decoupling release cycles** so individual wrappers can be updated independently of Seurat itself * **Centralizing community contributions** into a single, discoverable package maintained under consistent conventions ## How the wrappers work Each wrapper follows a consistent pattern. Wrapper functions accept Seurat objects as their primary input, call the underlying method's native API, and return results in a Seurat-compatible format — either modifying the Seurat object in place or returning a modified copy. For example, calling `RunHarmony()` on a Seurat object passes the PCA embeddings to the Harmony algorithm and stores the corrected embeddings back as a new dimensional reduction in the same object. You continue to use standard Seurat functions (`FindNeighbors()`, `RunUMAP()`, etc.) on the result. ```r theme={null} library(Seurat) library(SeuratWrappers) # Run Harmony integration — input and output are Seurat objects obj <- RunHarmony(object = obj, group.by.vars = "batch") # Downstream Seurat steps work unchanged obj <- FindNeighbors(obj, reduction = "harmony", dims = 1:30) obj <- FindClusters(obj) ``` When the underlying method returns a non-Seurat object (such as Monocle 3's `cell_data_set`), SeuratWrappers provides conversion functions like `as.cell_data_set()` and `as.Seurat()` to move between representations. ## Method categories SeuratWrappers organizes methods into five broad categories: ### Integration Batch correction and multi-dataset integration methods that align cells from different samples, donors, or technologies. | Method | Description | | ---------------------------- | --------------------------------------------------------- | | [Harmony](/methods/harmony) | Iterative correction of PCA embeddings; fast and scalable | | [LIGER](/methods/liger) | Integrative non-negative matrix factorization | | [Conos](/methods/conos) | Joint graph-based integration across datasets | | [fastMNN](/methods/fast-mnn) | Mutual nearest-neighbor batch correction via batchelor | | [scVI](/methods/scvi) | Deep generative model for single-cell integration | ### Trajectory analysis Methods that infer developmental or dynamic relationships between cells and order them along pseudotime. | Method | Description | | --------------------------------- | ---------------------------------------------------------- | | [Monocle 3](/methods/monocle3) | Principal graph-based trajectory inference and pseudotime | | [RNA Velocity](/methods/velocity) | Transcriptional dynamics from spliced/unspliced RNA ratios | | [tricycle](/methods/tricycle) | Cell cycle position estimation | ### Dimensionality reduction and imputation Alternative methods for embedding cells in low-dimensional space and recovering signal from sparse count matrices. | Method | Description | | -------------------------- | ----------------------------------------------------- | | [ALRA](/methods/alra) | Zero-preserving imputation via low-rank approximation | | [GLM-PCA](/methods/glmpca) | PCA for count data using generalized linear models | | [PaCMAP](/methods/pacmap) | Pairwise-controlled manifold approximation | ### Spatial and visualization Methods for spatially resolved transcriptomics and enhanced visualization of single-cell data. | Method | Description | | ----------------------------- | ----------------------------------------------------------- | | [BANKSY](/methods/banksy) | Spatial clustering incorporating cell neighborhood context | | [Nebulosa](/methods/nebulosa) | Kernel density estimation for gene expression visualization | | [schex](/methods/schex) | Hexagonal binning for large single-cell datasets | ### Quality control and utilities Tools for filtering low-quality cells, importing data, running differential expression, and exporting results. | Method | Description | | ------------------------------------ | ------------------------------------------------------- | | [miQC](/methods/miqc) | Probabilistic QC using mixture models | | [CoGAPS](/methods/cogaps) | Non-negative matrix factorization for pattern discovery | | [CIPR](/methods/cipr) | Cell identity prediction using reference profiles | | [Presto](/methods/presto) | Fast Wilcoxon test for differential expression | | [alevin](/methods/alevin) | Import salmon/alevin quantification into Seurat | | [Cell Browser](/methods/cellbrowser) | Export Seurat objects for UCSC Cell Browser | ## Next steps Install SeuratWrappers and the dependencies for each method you plan to use. Browse the full function reference with parameters and return types.