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Integrate a List of Seurat Objects Using Selected Features

Source: R/integrate_by_features.R
integrate_seurat_list.Rd

integrate_seurat_list integrates a list of Seurat objects (e.g., representing different samples or batches) using canonical correlation analysis (CCA) based on a set of selected features (genes). The function handles normalization, finding integration anchors, integrating data, and optional downstream processing steps such as scaling, PCA, and UMAP visualization.

Usage

integrate_seurat_list(
  seurat_list,
  features,
  int_order = NULL,
  process = TRUE,
  verbose = FALSE
)

Arguments

seurat_list

A list of Seurat objects to be integrated.

features

A character vector of gene names (features) used for integration.

int_order

An optional data frame specifying the integration order of samples within the Seurat list. See the sample.tree argument in IntegrateData for more details. If not provided, Seurat will construct the integration order using hierarchical clustering. Default is NULL.

process

Logical value indicating whether to further process the data after integration (i.e., scale it, run PCA, and compute UMAP embeddings). Default is TRUE.

verbose

Logical value indicating whether to display progress messages during integration. Default is FALSE.

Value

A Seurat object containing the integrated data. The default assay is set to "integrated".

Details

The function performs the following steps:

  1. Normalization: Each Seurat object in the list is log-normalized using NormalizeData.

  2. Parameter Adjustment: Integration parameters are adjusted based on the smallest dataset to accommodate cases with a small number of cells (e.g., metacells).

  3. Finding Integration Anchors: Uses FindIntegrationAnchors to find anchors between datasets based on the provided features.

  4. Integration: Integrates the datasets using IntegrateData.

  5. Optional Processing: If process = TRUE, the function scales the data, runs PCA, and computes UMAP embeddings.

The integration is performed using Seurat's CCA-based methods, and the function is designed to handle datasets with varying sizes efficiently.

See also

integrate_by_features, IntegrateData, FindIntegrationAnchors, NormalizeData, ScaleData, RunPCA, RunUMAP

Examples

if (FALSE) { # \dontrun{
# Integrate a list of seurat object using selected 
#  features (e.g. anglemania genes or HVGs)
#  and CCA integration method
seurat_list <- list(seurat_object1, seurat_object2)
integrated_seurat <- integrate_seurat_list(seurat_list, features)
} # }