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WNN integration function

Source: R/integration-wnn.R
WNN_integrate.Rd

WNN integration function

Usage

WNN_integrate(
  srt_merge = NULL,
  batch = NULL,
  append = TRUE,
  srt_list = NULL,
  assay = NULL,
  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,
  verbose = TRUE,
  seed = 11
)

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 Seurat objects to be checked and preprocessed.

assay

Assay to use. NULL uses the default assay.

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_method is "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 = NULL uses 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"). Larger cluster_resolution yields fewer clusters.

verbose

Whether to print the message. Default is TRUE.

seed

Random seed.

Examples

data("pbmcmultiome_sub", package = "scop")
pbmcmultiome_sub$batch <- rep(c("batch1", "batch2"), length.out = ncol(pbmcmultiome_sub))
pbmcmultiome_sub <- WNN_integrate(
  srt_merge = pbmcmultiome_sub,
  batch = "batch",
  linear_reduction_dims = 20,
  linear_reduction_dims_use = 1:10
)
#> ℹ [2026-10-12 00:01:17] Start standard processing workflow...
#> ℹ [2026-10-12 00:01:17] Auto preprocess assays: "RNA" and "peaks"
#> ℹ [2026-10-12 00:01:17] Start standard processing workflow...
#> ℹ [2026-10-12 00:01:17] Checking a list of <Seurat>...
#> ! [2026-10-12 00:01:17] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-12 00:01:17] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-12 00:01:17] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-12 00:01:17] Use the separate HVF from `srt_list`
#> ℹ [2026-10-12 00:01:17] Number of available HVF: 2000
#> ℹ [2026-10-12 00:01:17] Finished check
#> ℹ [2026-10-12 00:01:17] Perform `ScaleData()`
#> ℹ [2026-10-12 00:01:17] Perform pca linear dimension reduction
#> ℹ [2026-10-12 00:01:17] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-12 00:01:17] Reorder clusters...
#> ℹ [2026-10-12 00:01:17] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-12 00:01:17] Perform umap nonlinear dimension reduction
#> ℹ [2026-10-12 00:01:25] Perform umap nonlinear dimension reduction using RNApca (1:10)
#> ✔ [2026-10-12 00:01:31] Standard processing workflow completed
#> ℹ [2026-10-12 00:01:31] Start standard processing workflow...
#> ℹ [2026-10-12 00:01:31] Checking a list of <Seurat>...
#> ! [2026-10-12 00:01:31] Data 1/1 of the `srt_list` is "raw_counts"
#> ℹ [2026-10-12 00:01:31] Perform `RunTFIDF()` on 1/1 of `srt_list`...
#> ℹ [2026-10-12 00:01:31] Perform `FindTopFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-12 00:01:31] Use the separate HVF from `srt_list`
#> ℹ [2026-10-12 00:01:31] Number of available HVF: 11413
#> ℹ [2026-10-12 00:01:31] Finished check
#> ℹ [2026-10-12 00:01:31] `normalization_method` is TFIDF. Use lsi workflow
#> ℹ [2026-10-12 00:01:31] Perform svd linear dimension reduction
#> Running SVD
#> Scaling cell embeddings
#> ℹ [2026-10-12 00:01:32] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-12 00:01:32] Reorder clusters...
#> ℹ [2026-10-12 00:01:32] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-12 00:01:32] Perform umap nonlinear dimension reduction
#> ℹ [2026-10-12 00:01:40] Perform umap nonlinear dimension reduction using ATACsvd (1:10)
#> ✔ [2026-10-12 00:01:45] Standard processing workflow completed
#> ℹ [2026-10-12 00:01:45] Adjust neighbor k from 20 to 20 for small-sample WNN graph construction
#> ℹ [2026-10-12 00:01:45] Adjust WNN knn.range to 80 for small-sample graph construction
#> ℹ [2026-10-12 00:01:45] Perform WNN integration using RNApca and ATAClsi
#> Calculating cell-specific modality weights
#> Finding 20 nearest neighbors for each modality.
#> Calculating kernel bandwidths
#> Finding multimodal neighbors
#> Constructing multimodal KNN graph
#> Constructing multimodal SNN graph
#> ℹ [2026-10-12 00:01:46] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-10-12 00:01:46] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-10-12 00:01:46] Reorder clusters...
#> ℹ [2026-10-12 00:01:46] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-12 00:01:46] Perform umap nonlinear dimension reduction using WNN
#> ℹ [2026-10-12 00:01:52] Perform umap nonlinear dimension reduction using WNN