Integrate single-cell data with one or more methods. For ChromatinAssay,
the workflow uses TFIDF + SVD/LSI; Uncorrected is supported directly,
Harmony5 is redirected to Harmony, and Seurat/RPCA are not supported.
Usage
RunIntegration(
srt_merge = NULL,
batch,
append = TRUE,
srt_list = NULL,
assay = NULL,
integration_methods = 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_tol = 1e-05,
lisi_max_iter = 50,
lisi_knn_algorithm = c("auto", "brute_force", "clustered"),
lisi_cores = NULL,
lisi_max_dense_bytes = Inf,
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,
integration_method = NULL,
...
)Arguments
- srt_merge
A merged `Seurat` object that includes the batch information.
- batch
Batch variable name.
- append
Append integrated results to
srt_merge.- srt_list
A list of
Seuratobjects to be checked and preprocessed.- assay
Assay to use.
NULLuses the default assay.- integration_methods
Method(s) to run. Multiple methods require
append = TRUEand are applied sequentially. ForChromatinAssay, prefer"Uncorrected"or"Harmony5".- compute_lisi, lisi_label_colnames, lisi_reduction, lisi_dims, lisi_prefix, lisi_tool_name, lisi_perplexity, lisi_tol, lisi_max_iter, lisi_knn_algorithm, lisi_cores, lisi_max_dense_bytes
LISI scores.
lisi_label_colnames = NULLusesbatchwhen it is a single metadata column;lisi_reduction = NULLusesDefaultReduction().- compute_metrics, metrics_batch_col, metrics_celltype_col, metrics_reduction, metrics_cluster_col, metrics_tool_name, metrics_k_graph
Integration summary metrics on the selected reduction.
- do_normalization
Whether data normalization should be performed.
- normalization_method
The normalization method to be used. Possible values are
"LogNormalize","SCT","TFIDF", and"scran".- do_HVF_finding, HVF_method, nHVF, HVF
Highly variable features.
HVF_methodis"vst","mvp","disp", or"scran".- HVF_source
The source of highly variable features. Possible values are
"global"and"separate".- 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.
- do_scaling
Force scaling via ScaleData.
- vars_to_regress
A vector of variable names to include as additional regression variables.
- regression_model
"linear","poisson", or"negativebinomial".- scale_within_batch
Scale within each batch. Only used by
"Uncorrected","Seurat","MNN","Harmony","BBKNN","CSS","ComBat".- linear_reduction, linear_reduction_dims, linear_reduction_dims_use, linear_reduction_params, force_linear_reduction
Linear reduction (
"pca","svd","ica","nmf","mds","glmpca").linear_reduction_dims_use = NULLuses estimated dimensions, else the first 50.- nonlinear_reduction, nonlinear_reduction_dims, nonlinear_reduction_params, force_nonlinear_reduction
Nonlinear reduction (
"umap","umap-naive","tsne","dm","phate","pacmap","trimap","largevis","fr").- neighbor_metric, neighbor_k
Neighbor graph (
"euclidean","cosine","manhattan","hamming").- cluster_algorithm, cluster_resolution
Clustering (
"louvain","slm","leiden"). Largercluster_resolutionyields fewer clusters.- seed
Random seed.
- verbose
Whether to print the message. Default is
TRUE.- integration_method
Deprecated alias of
integration_methods.- ...
Passed to the integration method functions.
Value
A Seurat object. For ChromatinAssay, names follow the ATAC
convention (*lsi, *UMAP2D, cluster aliases, ATAC_default_*).
Examples
data(panc8_sub)
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "Harmony",
nHVF = 500,
linear_reduction_dims = 20,
linear_reduction_dims_use = 1:10,
nonlinear_reduction_dims = 2,
compute_lisi = TRUE,
lisi_label_colnames = "tech",
lisi_perplexity = 10
)
#> ◌ [2026-08-30 05:02:40] Run integration workflow...
#> ℹ [2026-08-30 05:02:40] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:02:40] Checking a list of <Seurat>...
#> ! [2026-08-30 05:02:40] Data 1/5 of the `srt_list` is "unknown"
#> Warning: Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:40] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ! [2026-08-30 05:02:41] Data 2/5 of the `srt_list` is "unknown"
#> Warning: Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ! [2026-08-30 05:02:41] Data 3/5 of the `srt_list` is "unknown"
#> Warning: Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ! [2026-08-30 05:02:41] Data 4/5 of the `srt_list` is "unknown"
#> Warning: Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ! [2026-08-30 05:02:41] Data 5/5 of the `srt_list` is "unknown"
#> Warning: Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:41] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:02:42] Number of available HVF: 500
#> ℹ [2026-08-30 05:02:42] Finished check
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:02:43] Perform `Seurat::ScaleData()`
#> ℹ [2026-08-30 05:02:44] Perform linear dimension reduction("pca")
#> ℹ [2026-08-30 05:02:44] Perform Harmony integration
#> ℹ [2026-08-30 05:02:44] Using "Harmonypca" (1:10) as input
#> Warning: restarting interrupted promise evaluation
#> Warning: internal error 1 in R_decompress1 with libdeflate
#> Error in run_harmony(data_mat = data_use[, dims.use, drop = FALSE], meta_data = metavars_df, vars_use = group.by.vars, verbose = verbose, return_object = TRUE, ...): lazy-load database '/home/runner/work/_temp/Library/harmony/R/harmony.rdb' is corrupt
CellDimPlot(
panc8_sub,
group.by = c("tech", "celltype"),
reduction = "HarmonyUMAP2D"
)
#> Error in DefaultReduction(srt, pattern = reduction): Unable to find any reductions
IntegrationBenchmarkPlot(panc8_sub, plot_type = "box")
#> ! [2026-08-30 05:02:44] No per-cell LISI columns were found for the box plot
#> Warning: No per-cell LISI columns were found for the box plot
#> Error in IntegrationBenchmarkPlot(panc8_sub, plot_type = "box"): No integration benchmark plots could be drawn
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "LIGER"
)
#> ◌ [2026-08-30 05:02:44] Run integration workflow...
#> ℹ [2026-08-30 05:02:44] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:02:45] Checking a list of <Seurat>...
#> ! [2026-08-30 05:02:45] Data 1/5 of the `srt_list` is "unknown"
#> Warning: Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:45] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ! [2026-08-30 05:02:45] Data 2/5 of the `srt_list` is "unknown"
#> Warning: Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:45] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ! [2026-08-30 05:02:45] Data 3/5 of the `srt_list` is "unknown"
#> Warning: Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:45] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ! [2026-08-30 05:02:45] Data 4/5 of the `srt_list` is "unknown"
#> Warning: Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:45] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ! [2026-08-30 05:02:46] Data 5/5 of the `srt_list` is "unknown"
#> Warning: Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:02:46] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:46] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:02:46] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:02:46] Number of available HVF: 2000
#> ℹ [2026-08-30 05:02:46] Finished check
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘ligerScaleData’ is empty
#> ℹ [2026-08-30 05:02:48] Prepare rliger layer "ligerScaleData" ...
#> ℹ [2026-08-30 05:02:49] Perform LIGER integration
#> ℹ [2026-08-30 05:02:55] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-08-30 05:02:56] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:02:56] Reorder clusters...
#> ℹ [2026-08-30 05:02:57] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:02:57] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ℹ [2026-08-30 05:03:04] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ℹ [2026-08-30 05:03:10] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ✔ [2026-08-30 05:03:18] LIGER integration completed
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "Harmony",
compute_lisi = TRUE,
lisi_label_colnames = "tech"
)
#> ◌ [2026-08-30 05:03:18] Run integration workflow...
#> ℹ [2026-08-30 05:03:19] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:03:20] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:03:20] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:20] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:20] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:20] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:20] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:20] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:20] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:20] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:21] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:21] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:21] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:03:21] Number of available HVF: 2000
#> ℹ [2026-08-30 05:03:21] Finished check
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:03:22] Perform `Seurat::ScaleData()`
#> ℹ [2026-08-30 05:03:22] Perform linear dimension reduction("pca")
#> ℹ [2026-08-30 05:03:22] Perform Harmony integration
#> ℹ [2026-08-30 05:03:22] Using "Harmonypca" (1:20) as input
#> Warning: restarting interrupted promise evaluation
#> Warning: internal error 1 in R_decompress1 with libdeflate
#> Error in run_harmony(data_mat = data_use[, dims.use, drop = FALSE], meta_data = metavars_df, vars_use = group.by.vars, verbose = verbose, return_object = TRUE, ...): lazy-load database '/home/runner/work/_temp/Library/harmony/R/harmony.rdb' is corrupt
IntegrationBenchmarkPlot(panc8_sub, plot_type = "box")
#> ! [2026-08-30 05:03:22] No per-cell LISI columns were found for the box plot
#> Warning: No per-cell LISI columns were found for the box plot
#> Error in IntegrationBenchmarkPlot(panc8_sub, plot_type = "box"): No integration benchmark plots could be drawn
data("pbmcmultiome_sub", package = "scop")
pbmcmultiome_sub$batch <- rep(c("batch1", "batch2"), length.out = ncol(pbmcmultiome_sub))
pbmcmultiome_sub <- RunIntegration(
pbmcmultiome_sub,
batch = "batch",
assay = "peaks",
integration_methods = "Harmony5",
normalization_method = "TFIDF"
)
#> ◌ [2026-08-30 05:03:23] Run integration workflow...
#> ! [2026-08-30 05:03:23] `integration_method = 'Harmony5'` is not compatible with <ChromatinAssay> in current Seurat v5 workflow. Automatically switch to "Harmony"
#> Warning: `integration_method = 'Harmony5'` is not compatible with <ChromatinAssay> in current Seurat v5 workflow. Automatically switch to "Harmony"
#> ℹ [2026-08-30 05:03:23] Split `srt_merge` into `srt_list` by "batch"
#> ℹ [2026-08-30 05:03:23] Checking a list of <Seurat>...
#> ! [2026-08-30 05:03:23] Data 1/2 of the `srt_list` is "raw_counts"
#> Warning: Data 1/2 of the `srt_list` is "raw_counts"
#> ℹ [2026-08-30 05:03:23] Perform `RunTFIDF()` on 1/2 of `srt_list`...
#> ℹ [2026-08-30 05:03:23] Perform `FindTopFeatures()` on 1/2 of `srt_list`...
#> ! [2026-08-30 05:03:23] Data 2/2 of the `srt_list` is "raw_counts"
#> Warning: Data 2/2 of the `srt_list` is "raw_counts"
#> ℹ [2026-08-30 05:03:23] Perform `RunTFIDF()` on 2/2 of `srt_list`...
#> ℹ [2026-08-30 05:03:23] Perform `FindTopFeatures()` on 2/2 of `srt_list`...
#> ℹ [2026-08-30 05:03:23] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:03:23] Number of available HVF: 2000
#> ℹ [2026-08-30 05:03:23] Finished check
#> ℹ [2026-08-30 05:03:24] `normalization_method` is "TFIDF". Use lsi workflow...
#> ℹ [2026-08-30 05:03:24] Perform linear dimension reduction("svd")
#> Running SVD
#> Scaling cell embeddings
#> ℹ [2026-08-30 05:03:24] Perform Harmony integration
#> ℹ [2026-08-30 05:03:24] Using "Harmonysvd" (2:10) as input
#> Warning: restarting interrupted promise evaluation
#> Warning: internal error 1 in R_decompress1 with libdeflate
#> Error in run_harmony(data_mat = data_use[, dims.use, drop = FALSE], meta_data = metavars_df, vars_use = group.by.vars, verbose = verbose, return_object = TRUE, ...): lazy-load database '/home/runner/work/_temp/Library/harmony/R/harmony.rdb' is corrupt
integration_methods <- c(
"Uncorrected", "Seurat", "CCA", "RPCA",
"MNN", "fastMNN", "Harmony", "Harmony5",
"Coralysis", "LIGER", "Conos", "ComBat"
)
p_list <- list()
for (method in integration_methods) {
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = 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"
)
}
#> ◌ [2026-08-30 05:03:24] Run integration workflow...
#> ℹ [2026-08-30 05:03:25] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:03:25] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:03:26] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:26] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:26] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:26] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:26] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:26] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:26] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:26] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:27] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:27] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:27] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:03:27] Number of available HVF: 2000
#> ℹ [2026-08-30 05:03:27] Finished check
#> ℹ [2026-08-30 05:03:27] Perform Uncorrected integration
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:03:27] Perform `Seurat::ScaleData()`
#> ℹ [2026-08-30 05:03:27] Perform "pca" linear dimension reduction
#> ℹ [2026-08-30 05:03:28] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-08-30 05:03:28] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:03:29] Reorder clusters...
#> ℹ [2026-08-30 05:03:29] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:03:29] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ℹ [2026-08-30 05:03:35] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ℹ [2026-08-30 05:03:42] Perform umap nonlinear dimension reduction using Uncorrectedpca (1:50)
#> ✔ [2026-08-30 05:03:49] Uncorrected integration completed
#> ◌ [2026-08-30 05:03:49] Run integration workflow...
#> ℹ [2026-08-30 05:03:49] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:03:50] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:03:50] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:50] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:50] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:50] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:51] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:51] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:51] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:51] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:51] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:03:51] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:03:51] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:03:52] Number of available HVF: 2000
#> ℹ [2026-08-30 05:03:52] Finished check
#> ℹ [2026-08-30 05:03:52] Perform FindIntegrationAnchors
#> ℹ [2026-08-30 05:04:15] Perform Seurat integration
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> ℹ [2026-08-30 05:04:30] Perform ScaleData on `srt_integrated`
#> ℹ [2026-08-30 05:04:30] Perform "pca" linear dimension reduction
#> ℹ [2026-08-30 05:04:31] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:04:31] Reorder clusters...
#> ℹ [2026-08-30 05:04:31] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:04:31] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:04:38] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:04:45] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ✔ [2026-08-30 05:04:54] Seurat integration completed
#> ◌ [2026-08-30 05:04:54] Run integration workflow...
#> ℹ [2026-08-30 05:04:55] Data appears already log-normalized. Skipping `Seurat::NormalizeData()`
#> ℹ [2026-08-30 05:04:55] Perform `Seurat::FindVariableFeatures()` per batch (`HVF_source = 'separate'`)
#> ℹ [2026-08-30 05:04:55] Number of available HVF: 2000
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:04:56] Perform `Seurat::ScaleData()` on split layers for Seurat v5 integration
#> ℹ [2026-08-30 05:04:56] Perform PCA on split layers before `Seurat::IntegrateLayers()`
#> ℹ [2026-08-30 05:04:57] Perform Seurat v5 integration with `CCAIntegration()`
#> ℹ [2026-08-30 05:05:04] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-08-30 05:05:05] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:05:05] Reorder clusters...
#> ℹ [2026-08-30 05:05:05] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:05:05] Perform umap nonlinear dimension reduction using CCA (1:50)
#> ℹ [2026-08-30 05:05:12] Perform umap nonlinear dimension reduction using CCA (1:50)
#> ℹ [2026-08-30 05:05:18] Perform umap nonlinear dimension reduction using pca (1:50)
#> ✔ [2026-08-30 05:05:26] CCA integration completed
#> ◌ [2026-08-30 05:05:26] Run integration workflow...
#> ℹ [2026-08-30 05:05:27] Data appears already log-normalized. Skipping `Seurat::NormalizeData()`
#> ℹ [2026-08-30 05:05:27] Perform `Seurat::FindVariableFeatures()` per batch (`HVF_source = 'separate'`)
#> ℹ [2026-08-30 05:05:27] Number of available HVF: 2000
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:05:28] Perform `Seurat::ScaleData()` on split layers for Seurat v5 integration
#> ℹ [2026-08-30 05:05:28] Perform PCA on split layers before `Seurat::IntegrateLayers()`
#> ℹ [2026-08-30 05:05:29] Perform Seurat v5 integration with `RPCAIntegration()`
#> ℹ [2026-08-30 05:05:39] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-08-30 05:05:40] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:05:40] Reorder clusters...
#> ℹ [2026-08-30 05:05:40] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:05:40] Perform umap nonlinear dimension reduction using RPCA (1:50)
#> ℹ [2026-08-30 05:05:47] Perform umap nonlinear dimension reduction using RPCA (1:50)
#> ℹ [2026-08-30 05:05:53] Perform umap nonlinear dimension reduction using pca (1:50)
#> ✔ [2026-08-30 05:06:01] RPCA integration completed
#> ◌ [2026-08-30 05:06:01] Run integration workflow...
#> ℹ [2026-08-30 05:06:02] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:06:02] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:02] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:03] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:03] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:03] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:03] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:03] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:03] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:03] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:03] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:03] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:06:04] Number of available HVF: 2000
#> ℹ [2026-08-30 05:06:04] Finished check
#> ℹ [2026-08-30 05:06:04] Perform MNN integration
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> Warning: 'sumCountsAcrossCells' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' instead.
#> See help("Deprecated")
#> Warning: 'summarizeAssayByGroup' is deprecated.
#> Use 'scrapper::aggregateAcrossCells' or 'beachmat::tatami.sums.by.group' instead.
#> ℹ [2026-08-30 05:06:33] Perform ScaleData
#> ℹ [2026-08-30 05:06:33] Perform "pca" linear dimension reduction
#> ℹ [2026-08-30 05:06:34] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:06:34] Reorder clusters...
#> ℹ [2026-08-30 05:06:34] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:06:34] Perform umap nonlinear dimension reduction using MNNpca (1:50)
#> ℹ [2026-08-30 05:06:41] Perform umap nonlinear dimension reduction using MNNpca (1:50)
#> ℹ [2026-08-30 05:06:48] Perform umap nonlinear dimension reduction using MNNpca (1:50)
#> ✔ [2026-08-30 05:06:56] MNN integration completed
#> ◌ [2026-08-30 05:06:56] Run integration workflow...
#> ℹ [2026-08-30 05:06:57] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:06:57] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:57] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:57] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:57] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:58] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:58] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:58] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:58] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:58] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:06:58] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:06:58] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:06:59] Number of available HVF: 2000
#> ℹ [2026-08-30 05:06:59] Finished check
#> ℹ [2026-08-30 05:06:59] Perform fastMNN integration
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> ℹ [2026-08-30 05:07:01] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:07:01] Reorder clusters...
#> ℹ [2026-08-30 05:07:01] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:07:01] Perform umap nonlinear dimension reduction using fastMNN (1:50)
#> ℹ [2026-08-30 05:07:08] Perform umap nonlinear dimension reduction using fastMNN (1:50)
#> ℹ [2026-08-30 05:07:15] Perform umap nonlinear dimension reduction using fastMNN (1:50)
#> ✔ [2026-08-30 05:07:23] fastMNN integration completed
#> ◌ [2026-08-30 05:07:23] Run integration workflow...
#> ℹ [2026-08-30 05:07:24] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:07:24] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:07:25] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:25] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:25] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:25] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:25] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:25] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:25] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:25] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:26] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:26] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:26] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:07:26] Number of available HVF: 2000
#> ℹ [2026-08-30 05:07:26] Finished check
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘scale.data’ is empty
#> ℹ [2026-08-30 05:07:26] Perform `Seurat::ScaleData()`
#> ℹ [2026-08-30 05:07:26] Perform linear dimension reduction("pca")
#> ℹ [2026-08-30 05:07:27] Perform Harmony integration
#> ℹ [2026-08-30 05:07:27] Using "Harmonypca" (1:50) as input
#> Warning: restarting interrupted promise evaluation
#> Warning: internal error 1 in R_decompress1 with libdeflate
#> Error in run_harmony(data_mat = data_use[, dims.use, drop = FALSE], meta_data = metavars_df, vars_use = group.by.vars, verbose = verbose, return_object = TRUE, ...): lazy-load database '/home/runner/work/_temp/Library/harmony/R/harmony.rdb' is corrupt
# Python-backed methods prepare a scVI/scvi-tools environment and run model
# training, so keep them separate from ordinary example checks.
if (FALSE) { # \dontrun{
if (reticulate::py_module_available("scvi")) {
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "scVI",
train_params = list(max_epochs = 2L),
nonlinear_reduction = "umap"
)
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "scVI5",
IntegrateLayers_params = list(max_epochs = 2L),
nonlinear_reduction = "umap"
)
}
} # }
nonlinear_reductions <- c(
"umap", "tsne", "dm", "phate",
"pacmap", "trimap", "largevis", "fr"
)
panc8_sub <- RunIntegration(
panc8_sub,
batch = "tech",
integration_methods = "Seurat",
linear_reduction_dims_use = 1:50,
nonlinear_reduction = nonlinear_reductions
)
#> ◌ [2026-08-30 05:07:29] Run integration workflow...
#> ℹ [2026-08-30 05:07:30] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 05:07:31] Checking a list of <Seurat>...
#> ℹ [2026-08-30 05:07:31] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:31] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:31] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:31] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:31] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:31] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:31] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:31] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:32] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-08-30 05:07:32] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 05:07:32] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:07:32] Number of available HVF: 2000
#> ℹ [2026-08-30 05:07:32] Finished check
#> ℹ [2026-08-30 05:07:32] Perform FindIntegrationAnchors
#> ℹ [2026-08-30 05:08:04] Perform Seurat integration
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> Warning: Different cells in new layer data than already exists for scale.data
#> Warning: Layer counts isn't present in the assay object; returning NULL
#> ℹ [2026-08-30 05:08:46] Perform ScaleData on `srt_integrated`
#> ℹ [2026-08-30 05:08:46] Perform "pca" linear dimension reduction
#> ℹ [2026-08-30 05:08:48] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 05:08:48] Reorder clusters...
#> ℹ [2026-08-30 05:08:48] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:08:48] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:08:55] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:09:01] Perform tsne nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:09:04] Perform tsne nonlinear dimension reduction using Seuratpca (1:50)
#> ℹ [2026-08-30 05:09:11] Perform dm nonlinear dimension reduction using Seuratpca (1:50)
#> Warning: In (function (data = stopifnot_distmatrix(distance), sigma = "local",
#> k = find_dm_k(dataset_n_observations(data, distance) - 1),
#> n_eigs = min(20, dataset_n_observations(data, distance) -
#> 2), density_norm = TRUE, ..., distance = c("euclidean",
#> "cosine", "rankcor", "l2"), n_pcs = NULL, n_local = seq(to = min(k,
#> 7), length.out = min(k, 3)), rotate = FALSE, censor_val = NULL,
#> censor_range = NULL, missing_range = NULL, vars = NULL, knn_params = list(),
#> verbose = !is.null(censor_range), suppress_dpt = FALSE)
#> {
#> force(k)
#> force(n_eigs)
#> chkDots(...)
#> if (is.null(sigma) || !is(sigma, "Sigmas") && isTRUE(is.na(sigma)))
#> sigma <- "local"
#> if (!is(sigma, "Sigmas") && !(length(sigma) == 1 && sigma %in%
#> c("local", "global")) && !is.numeric(sigma))
#> stop(sigma_msg(sigma))
#> if (identical(sigma, "local") && any(n_local > k))
#> stop("For local sigma, All entries of n_local (", paste(n_local,
#> collapse = ","), ") have to be ≤ k (", k, ")")
#> data_env <- new.env(parent = .GlobalEnv)
#> if (is_distmatrix(distance)) {
#> if (!(is.data.frame(data) || is.null(data)))
#> stop("If you provide a matrix for `distance`, `data` has to be NULL or a covariate `data.frame` is of class",
#> class(data))
#> if (!is.null(n_pcs))
#> stop("If you provide a matrix for `distance`, `n_pcs` has to be NULL")
#> data_env$data <- if (is.null(data))
#> distance
#> else data
#> dists <- as(distance, "symmetricMatrix")
#> if (!is.null(rownames(data_env$data)))
#> rownames(dists) <- colnames(dists) <- rownames(data)
#> distance <- "custom"
#> imputed_data <- data_or_pca <- NULL
#> n <- nrow(dists)
#> }
#> else {
#> dists <- NULL
#> distance <- match.arg(distance)
#> data_env$data <- data
#> data <- dataset_extract_doublematrix(data, vars)
#> imputed_data <- data
#> if (anyNA(imputed_data))
#> imputed_data <- as.matrix(hotdeck(data, imp_var = FALSE))
#> n <- nrow(imputed_data)
#> pca <- get_pca(imputed_data, data_env$data, n_pcs, verbose)
#> if (is.null(pca) && ncol(imputed_data) > 500) {
#> warning("You have ", ncol(imputed_data), " genes. Consider passing e.g. n_pcs = 50 to speed up computation.")
#> }
#> if (is.null(pca)) {
#> n_pcs <- NA
#> data_or_pca <- imputed_data
#> }
#> else {
#> n_pcs <- ncol(pca)
#> data_or_pca <- pca
#> if (is(data_env$data, "SingleCellExperiment") &&
#> !("pca" %in% reducedDimNames(data_env$data)))
#> reducedDim(data_env$data, "pca") <- pca
#> }
#> }
#> if (n <= n_eigs + 1)
#> stop("Eigen decomposition not possible if n ≤ n_eigs+1 (And ",
#> n, " ≤ ", n_eigs + 1, ")")
#> if (is.null(k) || is.na(k))
#> k <- n - 1
#> if (k >= n)
#> stop(sprintf("k has to be < nrow(data) (And %s ≥ nrow(data))",
#> k))
#> censor <- test_censoring(censor_val, censor_range, imputed_data,
#> missing_range)
#> if (censor && !identical(distance, "euclidean"))
#> stop("censoring model only valid with euclidean distance")
#> knn <- get_knn(data_or_pca, dists, k, distance, knn_params,
#> verbose)
#> sigmas <- get_sigmas(imputed_data, knn$dist, sigma, n_local,
#> distance, censor_val, censor_range, missing_range, vars,
#> verbose)
#> sigma <- optimal_sigma(sigmas)
#> trans_p <- transition_probabilities(imputed_data, sigma,
#> knn$dist_mat, censor, censor_val, censor_range, missing_range,
#> verbose)
#> rm(knn)
#> d <- rowSums(trans_p, na.rm = TRUE) + 1
#> norm_p <- get_norm_p(trans_p, d, d, density_norm)
#> rm(trans_p)
#> d_norm <- rowSums(norm_p)
#> d_rot <- Diagonal(x = d_norm^-0.5)
#> transitions <- as(d_rot %*% norm_p %*% d_rot, "symmetricMatrix")
#> rm(norm_p)
#> eig_transitions <- decomp_transitions(transitions, n_eigs +
#> 1, verbose)
#> eig_vec <- eig_transitions$vectors
#> eig_val <- eig_transitions$values
#> if (rotate)
#> eig_vec <- as.matrix(t(t(eig_vec) %*% d_rot))
#> rownames(eig_vec) <- rownames(transitions) <- colnames(transitions) <- rownames(if (!is.null(rownames(dists)))
#> dists
#> else data)
#> colnames(eig_vec) <- names(eig_val) <- paste0("DC", seq(0,
#> n_eigs))
#> new("DiffusionMap", eigenvalues = eig_val[-1], eigenvectors = eig_vec[,
#> -1, drop = FALSE], sigmas = sigmas, data_env = data_env,
#> eigenvec0 = eig_vec[, 1], transitions = if (suppress_dpt)
#> NULL
#> else transitions, d = d, d_norm = d_norm, k = as.integer(k),
#> n_pcs = as.integer(n_pcs), n_local = as.integer(n_local),
#> rotate = rotate, density_norm = density_norm, distance = distance,
#> censor_val = censor_val, censor_range = censor_range,
#> missing_range = missing_range, knn_params = knn_params)
#> })(data = c(4.60693838317857, -8.10698366067959, 23.9032748672998,
#> -4.63573871812011, 22.6439101837089, 19.2240780555402, -7.79832761182944,
#> -9.43085314028482, -9.34011415888237, -10.1807386362533, 11.0302100178563,
#> 13.5291948641072, -9.40429636252656, 23.6806429456594, 20.1467379094998,
#> 7.99892371828489, 21.6624949729874, 19.9701296846188, 15.8455892952553,
#> 18.0340959245011, 12.5731704997856, 19.9682493557107, 21.0698744456024,
#> 27.406570466361, 20.1193701194716, 23.5856170913484, 20.2862554148868,
#> 21.765967849254, 20.5770683008491, 11.7837127161383, 18.6658060389243,
#> 23.7928359994144, 22.2254707260225, 9.2685761396646, -5.92715064223805,
#> 21.1639046071237, 19.970927569096, -8.0570097181941, -9.31188468890733,
#> -8.87421615658299, -8.45460356765253, -7.66567393821353, -8.24823787244548,
#> -9.7043803242525, -10.0210065786525, -7.00743674788501, -9.38681282292304,
#> -8.57788539469571, -9.9246714407518, -6.72925908390409, -6.17333037882947,
#> -9.18850986016682, -8.00695238669521, -9.53298637937369, -9.28560627665082,
#> -9.21543856070423, -8.24134721327092, -9.06034793242023, -6.31471851324184,
#> -9.87919008484223, 20.6407082963446, -7.62386373634133, -6.91625087254477,
#> 17.8030556688244, -5.19081604211356, -7.95848333429701, 16.9162242973511,
#> -8.47422875037821, -7.53956168029715, -8.47861870185701, 21.1423730707595,
#> -8.12205185856396, -10.500870656529, 11.3641221025991, -9.19852878303805,
#> 9.77728529561995, -7.51002141938948, -10.0554758650261, -9.79944653765605,
#> -9.00303303452685, -8.59705412808084, 20.4987279989813, 18.3261764533445,
#> -11.1717055397857, 17.4757860480279, 25.2630019417959, -10.3602526576172,
#> 23.0466724292989, -6.60969159526085, 16.227520287964, 15.5673760949471,
#> 4.98668254598383, 5.79410479523022, 13.4244058264259, -5.31840363598903,
#> 13.1888575956713, 9.67522976066889, 7.74512617282054, 11.0151052288888,
#> 10.4096072034946, 29.6723210476554, -7.45271248691798, 26.2482005051897,
#> 4.92656172832931, -4.69874477773212, 5.62975047253644, 12.3376940111154,
#> 17.045784582059, 14.2265879158509, 10.933873017842, 22.3023871132082,
#> 19.8060398085007, 20.9060586697858, -9.00968549343174, 7.44134851024383,
#> 12.7659970282695, 14.7466309953289, 16.8419998434441, 19.5369227382964,
#> 22.6431658730114, 11.972698783566, 19.8758440353482, 23.3857840916004,
#> 14.5003977704761, 16.4647274646626, 3.16549252900248, 26.1028713389551,
#> 14.7166510858319, 9.71205868771568, 11.5323134352939, -10.4493018176396,
#> 16.9709076588332, 0.571185202647976, -8.92640116120307, 13.1993080849176,
#> 17.0725821355944, -5.71621079359775, 10.7288308297993, -11.2762229260296,
#> -7.41844801238508, 26.4151373240259, 18.9585341510253, 16.8668699883544,
#> -10.2294220362178, -8.81507414686983, -8.29177594600688, -8.40143468535772,
#> 16.3117823633794, -8.75971570477717, -9.9325417841685, -7.15019287582515,
#> -8.31972119481848, -10.3211790679083, 14.0108173811975, 17.6435518684861,
#> -7.60024835882008, -10.3992620024894, 25.538598904949, -7.88313163425615,
#> 14.1110900947988, 11.366901715567, 14.8042918675335, 18.8129199012817,
#> 20.5039879369002, -9.2250467614386, -8.70821343607147, -8.11500475315087,
#> -8.93128436292888, 21.65955433943, -6.47406667513204, 22.8820358698412,
#> 19.9030603570467, -9.9652864776538, -8.5136339830988, 20.9453968918457,
#> 18.9499144339713, -8.15967505064173, 17.1842228507861, 19.3710135191799,
#> 11.7531466567272, 18.077656077045, -9.06818522462199, -9.00923950339972,
#> 19.3760758430392, -10.9436822889309, -9.24060150655049, 9.23883657116654,
#> 23.9289874412462, -9.07811372128569, 19.0627850993387, -9.02863069867608,
#> -8.13738705280062, 17.209783988313, -10.3933623637861, -7.99825730274585,
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#> 25.8526679887514, -7.03623284695749, 23.0197018362335, -4.50248792767628,
#> 18.9109680477, 24.8124502661174, 7.04224960198276, 16.8111002376099,
#> 18.3227560244616, -7.21118021690087, 25.7052739747989, 17.6022929407471,
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#> -8.32211436719019, 15.7944880231141, 18.8534578208924, 26.617725595479,
#> 25.7858048062341, -7.72156546169079, 25.7622782099931, -7.44450538091111,
#> 18.5426231288983, -9.96233811677343, -8.44048551431893, -10.8120703145923,
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#> -8.50604148923995, -3.93973542760025, -9.95777708381447, 22.5816539482273,
#> -9.18035252185716, 12.2752699081521, 5.78657848499383, -9.96805398891745,
#> -6.48380726678558, -10.4767203377469, 22.0691857035225, -8.27031185961466,
#> -9.1042176267767, -8.85629895534121, -8.86107857849956, -8.39516351656824,
#> -8.95680367866406, -8.94546438562867, 30.1555954893937, -9.68639472403808,
#> 21.3057818252155, -7.96861651734973, 16.9785529562258, -6.90057130157876,
#> 22.3952660982488, 13.6753680346579, 24.4656174101966, -9.95731195560828,
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#> 26.8097711580473, -8.70146922556003, -9.3955499842215, -9.03452403460179,
#> 23.3764241898993, -10.2557090491484, -8.74646064912105, -9.61469622555495,
#> -10.5494640045685, -9.06801712709113, 5.7086838255026, -10.4318398344469,
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#> -8.90867608219993, -8.60836668366678, -9.13680857932837, -7.80501082497068,
#> 26.0725202399007, 6.54973441771275, -9.36679063694922, -10.6951517111017,
#> -8.596457399845, -8.35010688822422, -8.19051546220217, 22.0440685715697,
#> -8.4440098529585, 27.9339359924983, 18.1452340688108, 23.0774048846081,
#> 19.4028356309741, 24.6510389256939, -7.46112906851053, 25.4888873447365,
#> -9.12426044512115, -8.79244452198783, 21.6511324258384, -8.96222642181309,
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#> 27.3815958016957, 19.290892229441, 15.8608697398163, 22.2004407856695,
#> -2.77997751106761, -7.50234740547863, 28.3485241744011, -7.92739010090516,
#> 24.8204573349541, 13.6578116500727, 16.2450907889607, -3.7302496524835,
#> 22.9431846949994, 12.4916341656611, 8.79313548510302, -9.96485991555906,
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#> -0.0846516164616449, -0.00489214404378563, 1.34698063907868,
#> 0.142879993672251, 0.206257035256533, 0.483637558326693, 0.780899321780614,
#> -0.287736546819803, 0.325533044963359, 0.668326619488751, -2.8551513752261,
#> -0.162952651057579, 0.712056643674395, -2.27837237920864, -0.894672917840554,
#> 0.852782852086125, 0.0309917429714707, -1.53694672805874, -0.337104011337737,
#> -0.919452557012503, -0.489560777657497, 0.367095556787288, 0.217140064377159,
#> -0.0227938657386024, -0.698800207754024, 0.974741434797113, -0.520119664485111,
#> -0.880144727889859, 0.367535206221594, 0.971363580764983, -0.00407990168514896,
#> 1.72951570655686, 2.02351591420283, 1.02832312947712, -1.53823686800944,
#> 0.341326684655078, -0.782806401841782, -0.900306047266297, 0.0982259577155906,
#> -4.18332011038644, 2.61128368161281, 0.603464675328639, -1.16314830496878,
#> -0.97222992553789, 0.683225669402599, -0.139795211274535, -1.52066025522178,
#> 0.46085361074554, 2.12515697314258, 0.939488537885028, -0.0546556972575286,
#> -0.779602288608015, 0.718834816638313, 0.147098769732642, 1.03833444197253,
#> -0.739392484870387, 0.0581937199948469, -0.491686446749558, -0.999438014108951,
#> 0.779030655328263, 0.621779984408873, -1.70936790148502, 6.2667850120691,
#> -4.63623303924192, -5.33497016871183, 2.3292754361219, 1.16095659012944,
#> -0.992544034650945, 6.64132690902506, 3.07317725313205, -0.502909058739944,
#> 0.838225259569422, -0.938255323484796, 1.82512430761421, 0.695537815251675,
#> -1.82149409289493, -1.8648987811882, 2.77189208883973, -0.296851762947445,
#> -0.44242671431225, -0.737108064533703, 0.585040975854978, 0.601158972912535,
#> -2.38643561192435, -0.341362374845432, 5.54417549222487, 0.338745826108851,
#> 0.738507631624515, -0.273664838451853, 0.0814143190128096, 1.3228657452219,
#> 0.468545654003901, -0.883559963567934, 0.445847155352542, 0.0463888731997468,
#> -1.04226057745493, 0.336109324084414, 2.10467063409321, -0.926084103234178,
#> -1.00152818536441, 0.361804228237403, 0.38277191172811, 1.48660725654013,
#> -0.349985983532515, -0.897522814436845, -4.57955090948332, 0.54754163690217,
#> 1.30182604886713, 1.73453556204389, -0.440598147628535, 2.02919138777021,
#> -0.375091787135584, 1.26021025586, -0.299589212633699, 1.2197289522278,
#> 4.09930772132931, 1.39734035402942, 0.751373292218967, -4.30437944385391,
#> -0.129211709078187, -2.73736472500139, 4.41552292289039, 0.265299129331228,
#> -0.429556013530942, -1.78518183431497, 2.95058519139593, -0.494692297492507,
#> 0.470476112692991, 0.0411382433786427, 0.0124783655135346, -0.0876759366915993,
#> -0.0944892230706359, -1.2030970402226, 1.32584362226408, 0.109125530549328,
#> -0.290713805343136, 0.352282182690705, -0.00538231840340254,
#> -3.67910310766714, 0.415067998343796, 3.50385319915774, -1.48695138155921,
#> 2.94802620258033, -0.096856241189025, -12.3086693898274, 1.2475748555052,
#> -1.27192034632371, -0.100743106546906, 2.76479838143748, 0.61848527970602,
#> 1.04584644873876, -0.859451589763736, -0.856094227847951, -1.0264075683888,
#> 0.104783705537728, -1.01699933896068, -0.949307997088699, -0.783361513163155,
#> 0.202161053456417, -0.471365359723428, -4.16889633733366, -1.44630928212488,
#> -0.285409723268497, 0.358951042104109, 0.932800005871708, 1.89558908719938,
#> -0.679137481316022, -0.179566559639841, 1.44847458043291, 0.219686908840245,
#> -0.947251427775225, 1.23495554447791, -1.12221150664093, -1.83547979102525,
#> 4.04619402010114, 1.56095019309252, 0.341394149800107, 1.80406717552984,
#> 0.749967597168747, 1.46948233634661, -0.743282877891516, -0.497712922100321,
#> 0.98802787154601, 0.0713518007745923, -1.01655738777536, -0.886022850781193,
#> 0.25874328453898, 0.909339699764742, -0.610530542485942, -1.06337412849199,
#> -0.666228016838238, -2.50147423390667, -3.67581230978299, 0.692409701177909,
#> 2.96351333995337, -1.28369212155585, 0.120040661087603, -3.19550296374292,
#> -4.16294994244776, -1.49546841091632, 1.86824609568419, -1.60869483284655,
#> -1.11683030983829, 0.426764922918166, -1.07830815460372, 0.486885513145721,
#> -1.29027303748312, -0.0823296882305597, -0.13895982762895, 0.231201609924205,
#> 0.384464905237051, 0.428655898329636, 0.62372758048077, 1.09390777972995,
#> -0.434584436988072, 0.691397921115952, 1.66053489043515, -3.83093091901061,
#> 2.42369150822073, 0.226088768568341, -4.70894209325036, -1.08436218200064,
#> 0.837759996992165, -1.64152879278524, 1.86360320264737, -1.75197976679841,
#> 0.498765028484052, -2.32633891612215, -1.43661155033154, 0.857825658836742,
#> -1.04700924307008, 1.27041522876979, 0.215191907787972, 0.690146573215742,
#> -1.35600811806418, -1.36679813250061, -0.358950400158188, 1.34864023788197,
#> 0.808985371067699, 1.27104054082365, -0.0528408817974662, -0.724101975786746,
#> 1.20089618058479, 0.714681443363484, -0.0429104026392279, 1.27975505178412,
#> -2.27331935428319, -0.200435167483444, -2.96166059965369, -1.68732529498488,
#> -2.26451668450814, -1.55368897506057, 0.385074753065812, -0.157695216985252,
#> -1.8761789052146, -0.736691518728444, 0.292736941449526, 1.58742667549357,
#> -5.52236297398388, -0.927765794353205, -0.369767409786079, 1.36260702702768,
#> 1.33780438428706, -0.00204343517067175, 1.23295163451549, 2.63508644517237,
#> -2.1161012153262, 0.724258137367243, 1.98758771839231, -0.162422193547655,
#> -2.62914331441808, 0.73562321444072, 2.55110651497423, 0.625695097582344,
#> -1.85459511433423, -1.36602825529822, -2.07639720013966, 3.67775107075576,
#> 0.626134988978182, -1.53415346018717, 2.53444154378605, -0.0678503386196959,
#> -0.278831052238598, -0.3315957064118, -0.461243965584628, -0.823109439104109,
#> -4.77751111139863, -0.778625384370584, -1.96986790066286, 0.951914710217885,
#> -0.663293535480589, 0.545010766728668, 0.36914360870311, 0.684780558997875,
#> 1.94940093318131, 0.221847021308683, -0.701910309651153, -0.361148257124678,
#> 5.16839119576526, -1.82585395163624, -1.06846343790905, 0.930899215448329,
#> -1.72369165184573, 0.120833251271928, -0.488564598535768, 0.539461876619248,
#> -0.369362240811142, -0.557152714877053, -0.475211742270951, -0.307394801725381,
#> -0.595468638374842, 1.50173944193547, -0.346171256950139, -1.0410951810936,
#> 1.9622363449835, 1.77105682893105, 0.0538968538488812, -0.0179804571825629,
#> 1.64238075226425, 2.28853319167371, 5.59380435637963, 0.509956544827438,
#> 1.7643542574618, 0.270694346419727, -0.167395126898469, -0.409192231654404,
#> 8.95403535731164, -1.00653279649594, 0.272951952874043, 1.40189221622621,
#> -0.335523993434503, -3.78990898003123, -2.36959202897081, -2.99789428231121,
#> 5.81936267590209, -0.99007832161709, -2.42470326708954, -0.729242389082497,
#> -1.22605785829339, -2.29488875873113, 1.39517675762884, 1.23119919542082,
#> -1.35640179426768, -0.152184865475531, -0.492423991716019, -1.75463023170151,
#> 0.776618311993994, -0.216639385508412, 0.0835803732018095, -2.97120582435684,
#> -0.817827052923899, -1.29682672962102, 1.07325808315287, 0.953791815311873,
#> 2.37074964329648, -4.07929997860172, -0.545979365896063, 0.69084277687189,
#> 0.262761280919701, -1.34342090790832, -0.84954028431667, -0.143900774462326,
#> 0.791540784430648, -0.873308144443241, 1.49310434077871, -1.43913816263165,
#> -0.672878985147304, 0.948022143501802, 0.948888943700198, -4.71759315975166,
#> -3.93518085684035, -0.594150708050253, 0.0183214374954476, 3.83044700687594,
#> 2.06983090651815, -0.126067521431505, 0.485633267349874, -0.774900644562772,
#> -1.00418976318052, -0.260520435646315, -1.43982117874797, 0.678873140041704,
#> -1.20383554399067, 0.33200288631749, 0.907311998048673, 1.66852606307523,
#> 3.54411513509026, 0.159863298494685, 1.0786835133413, -1.60118889739853,
#> 0.247061288195879, 1.13500540720682, -1.00855627528128, -0.340229381567906,
#> -11.5282933529414, 0.567961491949159, 0.639629219089187, 1.101600133801,
#> -13.9504479748572), n_eigs = 2, sigma = "local", k = 30, distance = "euclidean",
#> verbose = FALSE, graph = "Seurat_SNN") :
#> extra argument ‘graph’ will be disregarded
#> ! [2026-08-30 05:10:08] (converted from warning) 'as(<dsCMatrix>, "dgTMatrix")' is deprecated.
#> ! Use 'as(as(., "generalMatrix"), "TsparseMatrix")' instead.
#> ! See help("Deprecated") and help("Matrix-deprecated").
#> Warning: (converted from warning) 'as(<dsCMatrix>, "dgTMatrix")' is deprecated.
#> Use 'as(as(., "generalMatrix"), "TsparseMatrix")' instead.
#> See help("Deprecated") and help("Matrix-deprecated").
#> ! [2026-08-30 05:10:08] Error when performing nonlinear dimension reduction. Skip this step
#> Warning: Error when performing nonlinear dimension reduction. Skip this step
#> ℹ [2026-08-30 05:10:08] Perform umap nonlinear dimension reduction using Seuratpca (1:50)
#> ✔ [2026-08-30 05:10:22] Seurat integration completed
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"
)
)
}
