> ## 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. # SeuratWrappers > Community-provided methods and extensions for Seurat — enabling cutting-edge single-cell analysis workflows Learn what SeuratWrappers is and how it extends Seurat for single-cell genomics. Install SeuratWrappers and its method-specific dependencies. Explore the full function reference with parameters and return types. View source code, report issues, and contribute new methods. ## What is SeuratWrappers? 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. 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. ## Available Methods Fast, scalable dataset integration via iterative correction of PCA embeddings. Integrative non-negative matrix factorization for single-cell data. Joint graph-based analysis for mapping between datasets. Mutual nearest-neighbor batch correction via Bioconductor batchelor. Deep generative model for single-cell data integration. Pseudotime trajectory inference and cell ordering. Estimate transcriptional dynamics using spliced/unspliced RNA ratios. Zero-preserving imputation using low-rank approximation. Spatial transcriptomics clustering incorporating neighborhood context. Probabilistic quality control for single-cell datasets. Fast Wilcoxon rank-sum test for differential expression. Generalized linear model PCA for count data. ## Quick Example ```r theme={null} # 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) ``` ## 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](https://github.com/satijalab/seurat.wrappers/wiki) on GitHub for guidelines.