The integration_scop function

Integrate single-cell RNA-seq data using various integration methods.

Usage

integration_scop(
  srt_merge = NULL,
  batch,
  append = TRUE,
  srt_list = NULL,
  assay = NULL,
  integration_method = "Uncorrected",
  do_normalization = NULL,
  normalization_method = "LogNormalize",
  do_HVF_finding = TRUE,
  HVF_source = "separate",
  HVF_method = "vst",
  nHVF = 2000,
  HVF_min_intersection = 1,
  HVF = NULL,
  do_scaling = TRUE,
  vars_to_regress = NULL,
  regression_model = "linear",
  scale_within_batch = FALSE,
  linear_reduction = "pca",
  linear_reduction_dims = 50,
  linear_reduction_dims_use = NULL,
  linear_reduction_params = list(),
  force_linear_reduction = FALSE,
  nonlinear_reduction = "umap",
  nonlinear_reduction_dims = c(2, 3),
  nonlinear_reduction_params = list(),
  force_nonlinear_reduction = TRUE,
  neighbor_metric = "euclidean",
  neighbor_k = 20L,
  cluster_algorithm = "louvain",
  cluster_resolution = 0.6,
  seed = 11,
  verbose = TRUE,
  ...
)

Arguments

srt_merge: A merged `Seurat` object that includes the batch information.
batch: A character string specifying the batch variable name.
append: Whether the integrated data will be appended to the original Seurat object (srt_merge). Default is TRUE.
srt_list: A list of Seurat objects to be checked and preprocessed.
assay: The name of the assay to be used for downstream analysis.
integration_method: A character vector specifying the integration method to use. Supported methods are: "Uncorrected", "Seurat", "scVI", "MNN", "fastMNN", "Harmony", "Scanorama", "BBKNN", "CSS", "LIGER", "Conos", "ComBat". Default is "Uncorrected".
do_normalization: Whether data normalization should be performed. Default is TRUE.
normalization_method: The normalization method to be used. Possible values are "LogNormalize", "SCT", and "TFIDF". Default is "LogNormalize".
do_HVF_finding: Whether highly variable feature (HVF) finding should be performed. Default is TRUE.
HVF_source: The source of highly variable features. Possible values are "global" and "separate". Default is "separate".
HVF_method: The method for selecting highly variable features. Default is "vst".
nHVF: The number of highly variable features to select. Default is 2000.
HVF_min_intersection: The feature needs to be present in batches for a minimum number of times in order to be considered as highly variable. The default value is 1.
HVF: A vector of highly variable features. Default is NULL.
do_scaling: Whether to perform scaling. If TRUE, the function will force to scale the data using the Seurat::ScaleData function.
vars_to_regress: A vector of variable names to include as additional regression variables. Default is NULL.
regression_model: The regression model to use for scaling. Options are "linear", "poisson", or "negativebinomial". Default is "linear".
scale_within_batch: Whether to scale data within each batch. Only valid when the integration_method is one of "Uncorrected", "Seurat", "MNN", "Harmony", "BBKNN", "CSS", "ComBat".
linear_reduction: The linear dimensionality reduction method to use. Options are "pca", "svd", "ica", "nmf", "mds", or "glmpca". Default is "pca".
linear_reduction_dims: The number of dimensions to keep after linear dimensionality reduction. Default is 50.
linear_reduction_dims_use: The dimensions to use for downstream analysis. If NULL, all dimensions will be used.
linear_reduction_params: A list of parameters to pass to the linear dimensionality reduction method.
force_linear_reduction: Whether to force linear dimensionality reduction even if the specified reduction is already present in the Seurat object.
nonlinear_reduction: The nonlinear dimensionality reduction method to use. Options are "umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", or "fr". Default is "umap".
nonlinear_reduction_dims: The number of dimensions to keep after nonlinear dimensionality reduction. If a vector is provided, different numbers of dimensions can be specified for each method. Default is c(2, 3).
nonlinear_reduction_params: A list of parameters to pass to the nonlinear dimensionality reduction method.
force_nonlinear_reduction: Whether to force nonlinear dimensionality reduction even if the specified reduction is already present in the Seurat object. Default is TRUE.
neighbor_metric: The distance metric to use for finding neighbors. Options are "euclidean", "cosine", "manhattan", or "hamming". Default is "euclidean".
neighbor_k: The number of nearest neighbors to use for finding neighbors. Default is 20.
cluster_algorithm: The clustering algorithm to use. Options are "louvain", "slm", or "leiden". Default is "louvain".
cluster_resolution: The resolution parameter to use for clustering. Larger values result in fewer clusters. Default is 0.6.
seed: An integer specifying the random seed for reproducibility. Default is 11.
verbose: Whether to print the message. Default is TRUE.
...: Additional arguments to be passed to the integration method function.

Value

A Seurat object.

Examples

data(panc8_sub)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Uncorrected"
)
#> ◌ [2025-11-13 12:45:12] Run Uncorrected integration...
#> ℹ [2025-11-13 12:45:12] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-11-13 12:45:13] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:45:13] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:45:13] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:15] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ! [2025-11-13 12:45:15] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:45:15] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 2/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:17] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ! [2025-11-13 12:45:17] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:45:17] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 3/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:19] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ! [2025-11-13 12:45:19] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:45:19] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 4/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:21] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ! [2025-11-13 12:45:22] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:45:22] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:24] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:24] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:45:24] Number of available HVF: 2000
#> ℹ [2025-11-13 12:45:25] Finished check
#> ℹ [2025-11-13 12:45:27] Perform Uncorrected integration
#> ℹ [2025-11-13 12:45:28] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:45:28] Perform linear dimension reduction("pca")
#> ℹ [2025-11-13 12:45:30] Perform FindClusters ("louvain")
#> ℹ [2025-11-13 12:45:30] Reorder clusters...
#> ℹ [2025-11-13 12:45:30] Perform nonlinear dimension reduction ("umap")
#> ℹ [2025-11-13 12:45:30] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-10) as input
#> ℹ [2025-11-13 12:45:35] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-10) as input
#> ✔ [2025-11-13 12:45:42] Run Uncorrected integration done
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)


panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Uncorrected",
  HVF_min_intersection = 5
)
#> ◌ [2025-11-13 12:45:43] Run Uncorrected integration...
#> ℹ [2025-11-13 12:45:43] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-11-13 12:45:44] Checking a list of <Seurat> object...
#> ℹ [2025-11-13 12:45:44] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:45:44] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:44] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:45:44] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:45] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:45:45] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:45] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:45:45] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:46] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:45:46] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:45:46] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:45:47] Number of available HVF: 270
#> ℹ [2025-11-13 12:45:47] Finished check
#> ℹ [2025-11-13 12:45:52] Perform Uncorrected integration
#> ℹ [2025-11-13 12:45:52] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:45:53] Perform linear dimension reduction("pca")
#> ℹ [2025-11-13 12:45:56] Perform FindClusters ("louvain")
#> ℹ [2025-11-13 12:45:56] Reorder clusters...
#> ℹ [2025-11-13 12:45:56] Perform nonlinear dimension reduction ("umap")
#> ℹ [2025-11-13 12:45:56] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-12) as input
#> ℹ [2025-11-13 12:46:01] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-12) as input
#> ✔ [2025-11-13 12:46:09] Run Uncorrected integration done
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)


panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Uncorrected",
  HVF_min_intersection = 5,
  scale_within_batch = TRUE
)
#> ◌ [2025-11-13 12:46:09] Run Uncorrected integration...
#> ℹ [2025-11-13 12:46:09] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-11-13 12:46:10] Checking a list of <Seurat> object...
#> ℹ [2025-11-13 12:46:11] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:46:11] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ℹ [2025-11-13 12:46:11] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:46:11] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ℹ [2025-11-13 12:46:12] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:46:12] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ℹ [2025-11-13 12:46:12] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:46:12] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ℹ [2025-11-13 12:46:13] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:46:13] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:46:13] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:46:13] Number of available HVF: 270
#> ℹ [2025-11-13 12:46:14] Finished check
#> ℹ [2025-11-13 12:46:34] Perform Uncorrected integration
#> ℹ [2025-11-13 12:46:34] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:46:35] Perform linear dimension reduction("pca")
#> ℹ [2025-11-13 12:46:36] Perform FindClusters ("louvain")
#> ℹ [2025-11-13 12:46:36] Reorder clusters...
#> ℹ [2025-11-13 12:46:36] Perform nonlinear dimension reduction ("umap")
#> ℹ [2025-11-13 12:46:36] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-13) as input
#> ℹ [2025-11-13 12:46:42] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-13) as input
#> ✔ [2025-11-13 12:46:53] Run Uncorrected integration done
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)


if (FALSE) { # \dontrun{
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Seurat"
)
CellDimPlot(panc8_sub, group.by = c("tech", "celltype"))

panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Seurat",
  FindIntegrationAnchors_params = list(reduction = "rpca")
)
CellDimPlot(panc8_sub, group.by = c("tech", "celltype"))

integration_methods <- c(
  "Uncorrected", "Seurat", "scVI", "MNN", "fastMNN", "Harmony",
  "Scanorama", "BBKNN", "CSS", "LIGER", "Conos", "ComBat"
)
for (method in integration_methods) {
  panc8_sub <- integration_scop(
    panc8_sub,
    batch = "tech",
    integration_method = method,
    linear_reduction_dims_use = 1:50,
    nonlinear_reduction = "umap"
  )
  print(
    CellDimPlot(panc8_sub,
      group.by = c("tech", "celltype"),
      reduction = paste0(method, "UMAP2D"),
      xlab = "", ylab = "", title = method,
      legend.position = "none", theme_use = "theme_blank"
    )
  )
}

nonlinear_reductions <- c(
  "umap", "tsne", "dm", "phate",
  "pacmap", "trimap", "largevis", "fr"
)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Seurat",
  linear_reduction_dims_use = 1:50,
  nonlinear_reduction = nonlinear_reductions
)
for (nr in nonlinear_reductions) {
  print(
    CellDimPlot(
      panc8_sub,
      group.by = c("tech", "celltype"),
      reduction = paste0("Seurat", nr, "2D"),
      xlab = "", ylab = "", title = nr,
      legend.position = "none", theme_use = "theme_blank"
    )
  )
}
} # }

Usage

Arguments

Value

See also

Examples