> ## 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.

# RunQuantileNorm()

> Quantile normalize LIGER iNMF embeddings to align datasets

`RunQuantileNorm()` aligns iNMF factor loadings across datasets using quantile normalization, producing the final integrated embedding. It wraps `rliger::quantile_norm()` and stores the normalized embeddings as a new DimReduc object in the Seurat object.

This is the second step of the LIGER workflow, called after `RunOptimizeALS()`.

## Syntax

```r theme={null}
RunQuantileNorm(
  object,
  split.by = "orig.ident",
  reduction = "iNMF_raw",
  reduction.name = "iNMF",
  reduction.key = "iNMF_",
  quantiles = 50,
  ref_dataset = NULL,
  min_cells = 20,
  knn_k = 20,
  dims.use = NULL,
  do.center = FALSE,
  max_sample = 1000,
  eps = 0.9,
  refine.knn = TRUE,
  ...
)
```

## Parameters

<ParamField path="object" type="Seurat" required>
  A Seurat object after running `RunOptimizeALS()`.
</ParamField>

<ParamField path="split.by" type="character" default="orig.ident">
  Metadata column identifying which dataset each cell belongs to. Must match the `split.by` used in `RunOptimizeALS()`.
</ParamField>

<ParamField path="reduction" type="character" default="iNMF_raw">
  Name of the raw iNMF reduction from `RunOptimizeALS()` to normalize.
</ParamField>

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

<ParamField path="reduction.key" type="character" default="iNMF_">
  Prefix for the normalized iNMF dimension column names.
</ParamField>

<ParamField path="quantiles" type="integer" default="50">
  Number of quantile bins used in the normalization procedure.
</ParamField>

<ParamField path="ref_dataset" type="character or integer" default="NULL">
  Name or index of the reference dataset for alignment. Defaults to the dataset with the most cells.
</ParamField>

<ParamField path="min_cells" type="integer" default="20">
  Minimum number of cells required in a cluster for it to be used in quantile alignment.
</ParamField>

<ParamField path="knn_k" type="integer" default="20">
  Number of nearest neighbors used in the kNN graph for quantile normalization.
</ParamField>

<ParamField path="dims.use" type="integer vector" default="NULL">
  Which iNMF dimensions to use. Defaults to all dimensions.
</ParamField>

<ParamField path="do.center" type="logical" default="FALSE">
  Whether to center embeddings before normalization. Should match the value used in `ScaleData()` (always `FALSE` for LIGER).
</ParamField>

<ParamField path="max_sample" type="integer" default="1000">
  Maximum number of cells to sample per dataset when computing quantiles.
</ParamField>

<ParamField path="eps" type="numeric" default="0.9">
  Epsilon parameter for approximate nearest neighbor search precision.
</ParamField>

<ParamField path="refine.knn" type="logical" default="TRUE">
  Whether to refine the kNN graph after initial construction.
</ParamField>

## Returns

A Seurat object with:

* A new DimReduc under `reduction.name` (default: `iNMF`) containing the quantile-normalized embeddings
* Additional metadata columns from the quantile normalization output stored as cell-level metadata
* Active identities (`Idents()`) set to the cluster assignments from quantile normalization

## Examples

```r theme={null}
library(SeuratWrappers)
install.packages('rliger')

# Full LIGER workflow
object <- NormalizeData(object)
object <- FindVariableFeatures(object)
object <- ScaleData(object, split.by = "orig.ident", do.center = FALSE)
object <- RunOptimizeALS(object, k = 20, split.by = "orig.ident")
object <- RunQuantileNorm(object, split.by = "orig.ident")

# Downstream analysis on normalized iNMF embedding
object <- FindNeighbors(object, reduction = "iNMF", dims = 1:ncol(Embeddings(object, "iNMF")))
object <- FindClusters(object)
object <- RunUMAP(object, reduction = "iNMF", dims = 1:ncol(Embeddings(object, "iNMF")))
```

## See Also

* [`RunOptimizeALS()`](/api/run-optimize-als) — Run iNMF factorization (first step)
* [LIGER method guide](/methods/liger)