LIGER integration function

Usage

LIGER_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",
  liger_dims_use = NULL,
  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,
  optimizeALS_params = list(),
  quantilenorm_params = list(),
  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".
liger_dims_use: Dimensions returned by LIGER that will be utilized for downstream cell cluster finding and nonlinear reduction. If set to NULL, all the returned dimensions will be used by default.
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.
optimizeALS_params: A list of parameters for the rliger::runIntegration function.
quantilenorm_params: A list of parameters for the rliger::quantileNorm function.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed.

Examples

data(panc8_sub)
panc8_sub <- LIGER_integrate(
  panc8_sub,
  batch = "tech"
)
#> ℹ [2026-08-30 04:33:28] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-08-30 04:33:28] Checking a list of <Seurat>...
#> ! [2026-08-30 04:33:28] Data 1/5 of the `srt_list` is "unknown"
#> Warning: Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:33:28] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:28] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ! [2026-08-30 04:33:28] Data 2/5 of the `srt_list` is "unknown"
#> Warning: Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:33:28] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 2/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:28] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ! [2026-08-30 04:33:28] Data 3/5 of the `srt_list` is "unknown"
#> Warning: Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:33:28] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 3/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:28] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ! [2026-08-30 04:33:29] Data 4/5 of the `srt_list` is "unknown"
#> Warning: Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:33:29] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 4/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:29] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ! [2026-08-30 04:33:29] Data 5/5 of the `srt_list` is "unknown"
#> Warning: Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:33:29] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:29] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-08-30 04:33:29] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:33:29] Number of available HVF: 2000
#> ℹ [2026-08-30 04:33:29] Finished check
#> Warning: No layers found matching search pattern provided
#> Warning: Layer ‘ligerScaleData’ is empty
#> ℹ [2026-08-30 04:33:31] Prepare rliger layer "ligerScaleData" ...
#> ℹ [2026-08-30 04:33:32] Perform LIGER integration
#> ℹ [2026-08-30 04:33:41] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-08-30 04:33:42] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-08-30 04:33:42] Reorder clusters...
#> ℹ [2026-08-30 04:33:43] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:33:43] Perform umap nonlinear dimension reduction using LIGER (1:20)
#> ℹ [2026-08-30 04:33:49] Perform umap nonlinear dimension reduction using LIGER (1:20)
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype")
)