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The integration workflow

Source: R/integration_scop.R
integration_scop.Rd

Integrate single-cell RNA-seq data using various integration methods. For ChromatinAssay, the current workflow uses TFIDF + SVD/LSI preprocessing. In this setting, Uncorrected is supported directly and Harmony5 will be automatically redirected to the legacy Harmony workflow. Seurat and RPCA are currently not supported for ChromatinAssay.

Usage

integration_scop(
  srt_merge = NULL,
  batch,
  append = TRUE,
  srt_list = NULL,
  assay = NULL,
  integration_method = c("Uncorrected", "Seurat", "CCA", "RPCA", "scVI", "PeakVI",
    "PoissonVI", "WNN", "MultiMAP", "GLUE", "scVI5", "MNN", "fastMNN", "fastMNN5",
    "Harmony", "Harmony5", "Scanorama", "BBKNN", "CSS", "Coralysis", "LIGER", "Conos",
    "ComBat"),
  compute_lisi = FALSE,
  lisi_label_colnames = NULL,
  lisi_reduction = NULL,
  lisi_dims = NULL,
  lisi_prefix = NULL,
  lisi_tool_name = NULL,
  lisi_perplexity = 30,
  lisi_nn_eps = 0,
  lisi_use_rann = TRUE,
  lisi_nn_method = c("auto", "rann", "fnn", "exact"),
  lisi_tol = 1e-05,
  lisi_max_iter = 50,
  compute_metrics = FALSE,
  metrics_batch_col = NULL,
  metrics_celltype_col = NULL,
  metrics_reduction = NULL,
  metrics_cluster_col = NULL,
  metrics_tool_name = NULL,
  metrics_k_graph = 15,
  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

Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.

integration_method

A character vector specifying the integration method to use. Supported methods are: "Uncorrected", "Seurat", "CCA", "RPCA", "scVI", "PeakVI", "PoissonVI", "WNN", "MultiMAP", "GLUE", "scVI5", "MNN", "fastMNN", "fastMNN5", "Harmony", "Harmony5", "Scanorama", "BBKNN", "CSS", "Coralysis", "LIGER", "Conos", "ComBat". Default is "Uncorrected". For ChromatinAssay, prefer "Uncorrected" or "Harmony5"; the latter is automatically switched to "Harmony".

compute_lisi

Whether to compute LISI scores on the integrated result. Default is FALSE.

lisi_label_colnames

Character vector of metadata columns used to compute LISI. If NULL and compute_lisi = TRUE, batch will be used when it is a single metadata column name.

lisi_reduction

Dimensional reduction used for LISI computation. Default is NULL, which uses DefaultReduction() from the integrated object.

lisi_dims

Dimensions used from lisi_reduction. Default is NULL, which uses all available dimensions.

lisi_prefix

Prefix used when storing LISI metadata columns. Default is NULL, which uses lisi_reduction.

lisi_tool_name

Name of the tool entry used to store LISI results. Default is NULL, which uses paste0(lisi_prefix, "_LISI").

lisi_perplexity

Effective neighborhood size used by LISI. Default is 30.

lisi_nn_eps

Approximation factor passed to nearest-neighbor search when applicable. Default is 0.

lisi_use_rann

Whether to prefer RANN as the approximate nearest-neighbor backend when lisi_nn_method = "auto". Default is TRUE.

lisi_nn_method

Nearest-neighbor backend passed to RunLISI(). One of "auto", "rann", "fnn", "exact". Default is "auto".

lisi_tol

Tolerance used in the LISI binary search. Default is 1e-5.

lisi_max_iter

Maximum iterations used in the LISI binary search. Default is 50.

compute_metrics

Whether to compute integration summary metrics on the selected reduction. Default is FALSE.

metrics_batch_col

Metadata column used for batch-mixing metrics. Default is NULL, which uses batch when it is a single metadata column name.

metrics_celltype_col

Metadata column used for biological conservation metrics. Default is NULL.

metrics_reduction

Reduction used for integration metric computation. Default is NULL, which uses DefaultReduction() from the integrated object.

metrics_cluster_col

Metadata column used as cluster labels for celltype_NMI, celltype_ARI, and celltype_purity. Default is NULL, which resolves the integrated cluster column automatically when possible.

metrics_tool_name

Name of the tool entry used to store integration metrics. Default is NULL, which uses paste0(integration_method, "_metrics").

metrics_k_graph

Number of neighbors used for graph-connectivity computation. Default is 15.

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 to perform high variable feature finding. If TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.

HVF_source

The source of highly variable features. Possible values are "global" and "separate". Default is "separate".

HVF_method

The method to use for finding highly variable features. Options are "vst", "mvp", or "disp". Default is "vst".

nHVF

The number of highly variable features to select. If NULL, all highly variable features will be used. 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. Default is 1.

HVF

A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.

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, estimated dimensions stored in the linear reduction will be used when available; otherwise, the first up to 50 dimensions will be used as a fallback.

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

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 functions.

Value

A Seurat object.

For ChromatinAssay, integrated outputs are additionally normalized to the ATAC naming convention used in scop, including *lsi, *UMAP2D, cluster aliases, and ATAC_default_* metadata when available.

See also

Seurat_integrate, scVI_integrate, MultiMAP_integrate, GLUE_integrate, MNN_integrate, fastMNN_integrate, Harmony_integrate, Scanorama_integrate, BBKNN_integrate, CSS_integrate, Coralysis_integrate, LIGER_integrate, Conos_integrate, ComBat_integrate

Examples

data(panc8_sub)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "LIGER"
)
#> ◌ [2026-04-26 02:32:32] Run integration workflow...
#> ℹ [2026-04-26 02:32:32] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:32:33] Checking a list of <Seurat>...
#> ! [2026-04-26 02:32:33] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:33] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:35] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ! [2026-04-26 02:32:35] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:35] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:38] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ! [2026-04-26 02:32:38] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:38] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:40] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ! [2026-04-26 02:32:40] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:40] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:43] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ! [2026-04-26 02:32:43] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:43] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:45] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:32:46] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:32:46] Number of available HVF: 2000
#> ℹ [2026-04-26 02:32:47] Finished check
#> Warning: Layer ‘ligerScaleData’ is empty
#> ℹ [2026-04-26 02:32:52] Prepare rliger layer "ligerScaleData" ...
#> ℹ [2026-04-26 02:32:52] Perform LIGER integration
#> ℹ [2026-04-26 02:33:00] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-04-26 02:33:01] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-04-26 02:33:01] Reorder clusters...
#> ℹ [2026-04-26 02:33:04] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:33:04] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ℹ [2026-04-26 02:33:10] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ℹ [2026-04-26 02:33:16] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ✔ [2026-04-26 02:33:25] LIGER integration completed
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)

panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Uncorrected",
  compute_lisi = TRUE,
  lisi_label_colnames = "tech"
)
#> ◌ [2026-04-26 02:33:25] Run integration workflow...
#> ℹ [2026-04-26 02:33:25] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:33:26] Checking a list of <Seurat>...
#> ℹ [2026-04-26 02:33:27] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:33:27] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:33:27] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:33:27] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:33:28] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:33:28] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:33:28] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:33:28] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:33:29] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:33:29] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:33:29] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:33:29] Number of available HVF: 2000
#> ℹ [2026-04-26 02:33:30] Finished check
#> ℹ [2026-04-26 02:33:35] Perform Uncorrected integration
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-04-26 02:33:35] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:33:36] Perform "pca" linear dimension reduction
#> ℹ [2026-04-26 02:33:37] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-04-26 02:33:37] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-04-26 02:33:37] Reorder clusters...
#> ℹ [2026-04-26 02:33:38] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:33:38] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:20)
#> ℹ [2026-04-26 02:33:44] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:20)
#> ℹ [2026-04-26 02:33:50] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:20)
#> ℹ [2026-04-26 02:33:56] Compute LISI scores from reduction "UncorrectedpcaUMAP2D"
#> ◌ [2026-04-26 02:33:56] Using "exact" nearest-neighbor backend for compute_lisi
#> ℹ [2026-04-26 02:33:56] Compute LISI scores from reduction "UncorrectedUMAP2D"
#> ◌ [2026-04-26 02:33:56] Using "exact" nearest-neighbor backend for compute_lisi
#> ✔ [2026-04-26 02:33:56] Stored LISI scores in metadata: "UncorrectedpcaUMAP2D_tech_LISI" and "UncorrectedUMAP2D_tech_LISI"
#> ✔ [2026-04-26 02:33:59] Uncorrected integration completed
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Harmony",
  compute_lisi = TRUE,
  lisi_label_colnames = "tech"
)
#> ◌ [2026-04-26 02:33:59] Run integration workflow...
#> ℹ [2026-04-26 02:33:59] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:34:00] Checking a list of <Seurat>...
#> ℹ [2026-04-26 02:34:00] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:00] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:01] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:01] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:01] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:01] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:02] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:02] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:02] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:02] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:03] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:34:03] Number of available HVF: 2000
#> ℹ [2026-04-26 02:34:04] Finished check
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-04-26 02:34:25] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:34:25] Perform linear dimension reduction("pca")
#> ℹ [2026-04-26 02:34:26] Perform Harmony integration
#> ℹ [2026-04-26 02:34:26] Using "Harmonypca" (1:20) as input
#> Error in check_legacy_args(...): Argument reduction.save is unhandled. Please refer to the documentation for the valid harmony options!
LISIPlot(
  panc8_sub,
  features = c("pca_tech_LISI", "Harmony5UMAP2D_tech_LISI")
)
#> Error in benchmark_feature_plot(srt = srt, features = features, tool_name = tool_name,     reduction = reduction, plot_type = plot_type, plot_boxplot = plot_boxplot,     boxplot_jitter = boxplot_jitter, combine = combine, nrow = nrow,     ncol = ncol, byrow = byrow, pt.size = pt.size, pt.alpha = pt.alpha,     palette = palette, palcolor = palcolor, theme_use = theme_use,     theme_args = theme_args, verbose = verbose, ...): The following benchmark columns are missing: "pca_tech_LISI" and
#> "Harmony5UMAP2D_tech_LISI"

if (requireNamespace("Signac", quietly = TRUE)) {
  data("pbmcmultiome_sub", package = "scop")
  pbmcmultiome_sub$batch <- rep(c("batch1", "batch2"), length.out = ncol(pbmcmultiome_sub))
  pbmcmultiome_sub <- integration_scop(
    pbmcmultiome_sub,
    batch = "batch",
    assay = "peaks",
    integration_method = "Harmony5",
    normalization_method = "TFIDF"
  )
}
#> ◌ [2026-04-26 02:34:28] Run integration workflow...
#> ! [2026-04-26 02:34:28] `integration_method = 'Harmony5'` is not compatible with <ChromatinAssay> in current Seurat v5 workflow. Automatically switch to "Harmony"
#> ℹ [2026-04-26 02:34:28] Split `srt_merge` into `srt_list` by "batch"
#> ℹ [2026-04-26 02:34:28] Checking a list of <Seurat>...
#> ! [2026-04-26 02:34:28] Data 1/2 of the `srt_list` is "raw_counts"
#> ℹ [2026-04-26 02:34:28] Perform `RunTFIDF()` on 1/2 of `srt_list`...
#> ℹ [2026-04-26 02:34:28] Perform `FindTopFeatures()` on 1/2 of `srt_list`...
#> ! [2026-04-26 02:34:29] Data 2/2 of the `srt_list` is "raw_counts"
#> ℹ [2026-04-26 02:34:29] Perform `RunTFIDF()` on 2/2 of `srt_list`...
#> ℹ [2026-04-26 02:34:29] Perform `FindTopFeatures()` on 2/2 of `srt_list`...
#> ℹ [2026-04-26 02:34:29] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:34:29] Number of available HVF: 2000
#> ℹ [2026-04-26 02:34:29] Finished check
#> ℹ [2026-04-26 02:34:29] `normalization_method` is "TFIDF". Use lsi workflow...
#> ℹ [2026-04-26 02:34:29] Perform linear dimension reduction("svd")
#> Running SVD
#> Scaling cell embeddings
#> ℹ [2026-04-26 02:34:29] Perform Harmony integration
#> ℹ [2026-04-26 02:34:29] Using "Harmonysvd" (2:23) as input
#> Error in check_legacy_args(...): Argument reduction.save is unhandled. Please refer to the documentation for the valid harmony options!

integration_methods <- c(
  "Uncorrected", "Seurat", "CCA", "RPCA", "scVI", "scVI5",
  "MNN", "fastMNN", "fastMNN5", "Harmony", "Harmony5",
  "Scanorama", "BBKNN", "CSS", "Coralysis", "LIGER", "Conos", "ComBat"
)
p_list <- list()
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"
  )
  p_list[[method]] <- CellDimPlot(
    panc8_sub,
    group.by = c("tech", "celltype"),
    reduction = paste0(method, "UMAP2D"),
    xlab = "", ylab = "",
    title = method,
    legend.position = "none",
    theme_use = "theme_blank"
  )
}
#> ◌ [2026-04-26 02:34:29] Run integration workflow...
#> ℹ [2026-04-26 02:34:29] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:34:31] Checking a list of <Seurat>...
#> ℹ [2026-04-26 02:34:31] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:31] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:32] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:32] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:32] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:32] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:33] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:33] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:33] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:34:33] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:34:34] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:34:34] Number of available HVF: 2000
#> ℹ [2026-04-26 02:34:35] Finished check
#> ℹ [2026-04-26 02:34:56] Perform Uncorrected integration
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-04-26 02:34:56] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:34:57] Perform "pca" linear dimension reduction
#> ℹ [2026-04-26 02:34:58] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-04-26 02:34:59] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-04-26 02:34:59] Reorder clusters...
#> ℹ [2026-04-26 02:34:59] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:34:59] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ℹ [2026-04-26 02:35:05] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ℹ [2026-04-26 02:35:12] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ✔ [2026-04-26 02:35:23] Uncorrected integration completed
#> ◌ [2026-04-26 02:35:23] Run integration workflow...
#> ℹ [2026-04-26 02:35:24] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:35:25] Checking a list of <Seurat>...
#> ℹ [2026-04-26 02:35:25] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:35:25] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:35:26] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:35:26] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:35:26] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:35:26] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:35:27] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:35:27] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:35:27] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:35:27] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:35:28] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:35:28] Number of available HVF: 2000
#> ℹ [2026-04-26 02:35:29] Finished check
#> ℹ [2026-04-26 02:37:12] Perform FindIntegrationAnchors
#> Error in getGlobalsAndPackages(expr, envir = envir, globals = globals): The total size of the 3 globals exported for future expression (‘FUN()’) is 523.77 MiB. This exceeds the maximum allowed size 500.00 MiB per plan() argument 'maxSizeOfObjects'. This limit is set to protect against transfering too large objects to parallel workers by mistake, which may not be intended and could be costly. See help("future.globals.maxSize", package = "future") for how to adjust or remove the default threshold via an R option There are three globals: ‘FUN’ (523.74 MiB of class ‘function’), ‘anchor.features’ (25.71 KiB of class ‘character’) and ‘ScaleData’ (406 bytes of class ‘function’)

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
)
#> ◌ [2026-04-26 02:37:12] Run integration workflow...
#> ℹ [2026-04-26 02:37:12] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-04-26 02:37:13] Checking a list of <Seurat>...
#> ℹ [2026-04-26 02:37:13] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:37:13] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-04-26 02:37:14] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:37:14] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-04-26 02:37:16] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:37:16] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-04-26 02:37:17] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:37:17] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-04-26 02:37:17] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-04-26 02:37:17] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-04-26 02:37:18] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:37:18] Number of available HVF: 2000
#> ℹ [2026-04-26 02:37:18] Finished check
#> ℹ [2026-04-26 02:38:56] Perform FindIntegrationAnchors
#> Error in getGlobalsAndPackages(expr, envir = envir, globals = globals): The total size of the 3 globals exported for future expression (‘FUN()’) is 523.77 MiB. This exceeds the maximum allowed size 500.00 MiB per plan() argument 'maxSizeOfObjects'. This limit is set to protect against transfering too large objects to parallel workers by mistake, which may not be intended and could be costly. See help("future.globals.maxSize", package = "future") for how to adjust or remove the default threshold via an R option There are three globals: ‘FUN’ (523.74 MiB of class ‘function’), ‘anchor.features’ (25.71 KiB of class ‘character’) and ‘ScaleData’ (406 bytes of class ‘function’)
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"
    )
  )
}