> ## 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.

# RunGLMPCA()

> Generalized linear model PCA for single-cell count data

`RunGLMPCA()` runs GLM-PCA on the raw count data in a Seurat object, avoiding the statistical artifacts introduced by normalizing sparse count matrices before PCA.

## Syntax

```r theme={null}
RunGLMPCA(
  object,
  L = 5,
  assay = NULL,
  features = NULL,
  reduction.name = "glmpca",
  reduction.key = "GLMPC_",
  verbose = TRUE,
  ...
)
```

## Parameters

<ParamField path="object" type="Seurat" required>
  A Seurat object. Must have counts in the `counts` slot.
</ParamField>

<ParamField path="L" type="integer" default="5">
  Number of dimensions (latent factors) to return.
</ParamField>

<ParamField path="assay" type="character" default="NULL">
  Assay to use. Defaults to the active default assay.
</ParamField>

<ParamField path="features" type="character vector" default="NULL">
  Features to use for GLM-PCA. Defaults to variable features set on the object.
</ParamField>

<ParamField path="reduction.name" type="character" default="glmpca">
  Name for the resulting DimReduc object.
</ParamField>

<ParamField path="reduction.key" type="character" default="GLMPC_">
  Prefix for the column names of the GLM-PCA embedding.
</ParamField>

<ParamField path="verbose" type="logical" default="TRUE">
  Print progress messages.
</ParamField>

<ParamField path="..." type="any">
  Additional parameters passed to `glmpca::glmpca()`.
</ParamField>

## Returns

A Seurat object with a new DimReduc object stored under `reduction.name` containing:

* Cell embeddings (factors)
* Feature loadings
* Standard deviations (L2 norms of factors)

The raw `glmpca` result metadata (excluding factors/loadings) is stored in the DimReduc `misc` slot.

## Examples

```r theme={null}
library(SeuratWrappers)
install.packages("glmpca")  # or: remotes::install_github("willtownes/glmpca")

# Find variable features first
object <- FindVariableFeatures(object, nfeatures = 2000)

# Run GLM-PCA with 10 dimensions
object <- RunGLMPCA(object, L = 10)

# Use for clustering
object <- FindNeighbors(object, reduction = "glmpca", dims = 1:10)
object <- FindClusters(object)

# Visualize
object <- RunUMAP(object, reduction = "glmpca", dims = 1:10)
DimPlot(object)
```

<Note>
  Do not call `NormalizeData()` or use normalized data before `RunGLMPCA()`. The function reads from the `counts` slot and performs its own statistical normalization internally.
</Note>

## See Also

* [GLM-PCA method guide](/methods/glmpca)