# SeuratWrappers

## Docs

- [ALRAChooseKPlot()](https://mintlify.wiki/satijalab/seurat-wrappers/api/alra-choose-k-plot.md): Visualize singular value spectrum and p-values to choose rank k for ALRA imputation
- [as.cell_data_set() / as.Seurat.cell_data_set()](https://mintlify.wiki/satijalab/seurat-wrappers/api/as-cell-data-set.md): Convert between Seurat objects and Monocle 3 cell_data_set objects
- [ExportToCellbrowser()](https://mintlify.wiki/satijalab/seurat-wrappers/api/export-to-cellbrowser.md): Export a Seurat object to UCSC Cell Browser format for interactive visualization
- [ReadAlevin()](https://mintlify.wiki/satijalab/seurat-wrappers/api/read-alevin.md): Import alevin quantification output into a Seurat object
- [ReadVelocity()](https://mintlify.wiki/satijalab/seurat-wrappers/api/read-velocity.md): Read spliced/unspliced RNA counts from a velocyto loom file
- [RunALRA()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-alra.md): Zero-preserving imputation of scRNA-seq data using adaptively-thresholded low-rank approximation
- [RunBanksy()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-banksy.md): Spatial transcriptomics clustering incorporating neighborhood gene expression context
- [RunCoGAPS()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-cogaps.md): Bayesian non-negative matrix factorization for pattern discovery in single-cell data
- [RunFastMNN() / FastMNNIntegration()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-fast-mnn.md): Mutual nearest neighbor batch correction using the batchelor package
- [RunGLMPCA()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-glmpca.md): Generalized linear model PCA for single-cell count data
- [RunMiQC() / PlotMiQC()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-miqc.md): Probabilistic quality control for single-cell datasets using mixture models
- [RunOptimizeALS()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-optimize-als.md): Run LIGER integrative NMF via optimizeALS on a Seurat object
- [RunPaCMAP()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-pacmap.md): Pairwise Controlled Manifold Approximation for dimensionality reduction and visualization
- [RunPresto() / RunPrestoAll()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-presto.md): Fast Wilcoxon rank-sum test for differential expression using Presto
- [RunQuantileAlignSNF()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-quantile-align-snf.md): Deprecated — calls RunQuantileNorm() internally
- [RunQuantileNorm()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-quantile-norm.md): Quantile normalize LIGER iNMF embeddings to align datasets
- [RunSNF()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-snf.md): Deprecated — use RunQuantileNorm() instead
- [Runtricycle()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-tricycle.md): Estimate cell cycle position from scRNA-seq data using tricycle
- [RunVelocity()](https://mintlify.wiki/satijalab/seurat-wrappers/api/run-velocity.md): Estimate RNA velocity from spliced and unspliced count matrices
- [scVIIntegration()](https://mintlify.wiki/satijalab/seurat-wrappers/api/scvi-integration.md): Deep generative model integration for Seurat v5 via IntegrateLayers()
- [Installation](https://mintlify.wiki/satijalab/seurat-wrappers/installation.md): Install SeuratWrappers and the per-method dependencies required for the wrappers you plan to use.
- [Introduction](https://mintlify.wiki/satijalab/seurat-wrappers/introduction.md): SeuratWrappers is a curated collection of community-provided extensions that bring cutting-edge single-cell analysis methods into the Seurat ecosystem.
- [Alevin Count Import](https://mintlify.wiki/satijalab/seurat-wrappers/methods/alevin.md): Import alevin RNA quantification output into Seurat as a count matrix for downstream single-cell analysis
- [ALRA Imputation](https://mintlify.wiki/satijalab/seurat-wrappers/methods/alra.md): Zero-preserving imputation of scRNA-seq data using low-rank approximation to recover dropout values.
- [BANKSY Spatial Clustering](https://mintlify.wiki/satijalab/seurat-wrappers/methods/banksy.md): Spatial transcriptomics clustering that unifies cell type identification and tissue domain segmentation by incorporating neighborhood context into gene expression.
- [UCSC Cell Browser Export](https://mintlify.wiki/satijalab/seurat-wrappers/methods/cellbrowser.md): Export Seurat objects to the UCSC Cell Browser for interactive, web-based single-cell visualization
- [CIPR Cell Type Annotation](https://mintlify.wiki/satijalab/seurat-wrappers/methods/cipr.md): Annotate single-cell clusters by scoring against curated reference immune cell profiles using logFC or correlation-based methods
- [CoGAPS Pattern Analysis](https://mintlify.wiki/satijalab/seurat-wrappers/methods/cogaps.md): Bayesian non-negative matrix factorization for discovering latent patterns of gene activity in single-cell RNA-seq data
- [Conos Integration](https://mintlify.wiki/satijalab/seurat-wrappers/methods/conos.md): Joint graph construction for mapping between multiple single-cell RNA-seq datasets.
- [fastMNN Batch Correction](https://mintlify.wiki/satijalab/seurat-wrappers/methods/fast-mnn.md): Mutual nearest neighbors batch correction for single-cell RNA-seq data via the Bioconductor batchelor package.
- [GLM-PCA](https://mintlify.wiki/satijalab/seurat-wrappers/methods/glmpca.md): Generalized linear model-based PCA for count data that avoids normalization artifacts in scRNA-seq dimensionality reduction.
- [Harmony Integration](https://mintlify.wiki/satijalab/seurat-wrappers/methods/harmony.md): Fast iterative dataset integration via PCA embedding correction across batches, donors, or conditions.
- [LIGER Integration](https://mintlify.wiki/satijalab/seurat-wrappers/methods/liger.md): Integrative non-negative matrix factorization for joint analysis of multiple single-cell datasets.
- [miQC Quality Control](https://mintlify.wiki/satijalab/seurat-wrappers/methods/miqc.md): Probabilistic quality control for single-cell datasets using mixture models to identify and remove low-quality cells
- [Monocle 3 trajectory analysis](https://mintlify.wiki/satijalab/seurat-wrappers/methods/monocle3.md): Pseudotime trajectory inference and cell ordering using Monocle 3 with Seurat objects.
- [Nebulosa Gene Expression Visualization](https://mintlify.wiki/satijalab/seurat-wrappers/methods/nebulosa.md): Kernel density estimation for visualizing gene expression across single cells, recovering dropout signal and revealing co-expression patterns.
- [PaCMAP Dimensionality Reduction](https://mintlify.wiki/satijalab/seurat-wrappers/methods/pacmap.md): Pairwise Controlled Manifold Approximation for robust, trustworthy visualization of high-dimensional single-cell data.
- [Presto Fast Differential Expression](https://mintlify.wiki/satijalab/seurat-wrappers/methods/presto.md): Fast Wilcoxon rank-sum tests for marker gene identification, scaling to millions of cells with ~1000x speedup over base R
- [schex Hexagonal Binning Visualization](https://mintlify.wiki/satijalab/seurat-wrappers/methods/schex.md): Hexagonal binning for single-cell data that reduces overplotting by summarizing cells into hexagon bins, enabling clear visualization of large datasets.
- [scVI Integration](https://mintlify.wiki/satijalab/seurat-wrappers/methods/scvi.md): Deep generative model for single-cell RNA-seq integration using a variational autoencoder via scvi-tools.
- [Cell cycle analysis with tricycle](https://mintlify.wiki/satijalab/seurat-wrappers/methods/tricycle.md): Estimate continuous cell cycle position using tricycle's transfer-learning approach on Seurat objects.
- [RNA velocity](https://mintlify.wiki/satijalab/seurat-wrappers/methods/velocity.md): Estimate transcriptional dynamics and predict future cell states using spliced and unspliced RNA counts.

## OpenAPI Specs

- [openapi](https://mintlify.wiki/satijalab/seurat-wrappers/api-reference/openapi.json)