GLUE integration function

Integrate paired RNA and ATAC data using scglue. The current implementation supports one Seurat object containing one RNA assay and one ChromatinAssay, and folds the modality embeddings back to the original cells by averaging the paired RNA/ATAC embeddings.

Usage

GLUE_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,
  gene_annotation_by = c("auto", "gene_name", "gene_id"),
  GLUE_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".
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.
gene_annotation_by: How RNA feature names are matched to gene annotation. One of "auto", "gene_name", or "gene_id".
GLUE_params: A list of parameters passed to scglue.models.fit_SCGLUE(). Reserved keys are "adatas" and "graph".
verbose: Whether to print the message. Default is TRUE.
seed: Random seed.

Value

A Seurat object.

Examples

if (FALSE) { # \dontrun{
data("pbmcmultiome_sub", package = "scop")
pbmcmultiome_sub$batch <- rep(c("batch1", "batch2"), length.out = ncol(pbmcmultiome_sub))
pbmcmultiome_sub <- GLUE_integrate(
  srt_merge = pbmcmultiome_sub,
  batch = "batch",
  GLUE_params = list(
    skip_balance = TRUE,
    init_kws = list(latent_dim = 20L),
    fit_kws = list(max_epochs = 10L, patience = 2L, reduce_lr_patience = 1L)
  )
)
} # }

Usage

Arguments

Value

See also

Examples