BBKNN_integrate — BBKNN_integrate • scop

BBKNN_integrate

Usage

BBKNN_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,
  cluster_algorithm = "louvain",
  cluster_resolution = 0.6,
  bbknn_params = list(),
  verbose = TRUE,
  seed = 11
)

Arguments

srt_merge: A merged `Seurat` object that includes the batch information.
batch: A character string specifying the batch variable name.
append: 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: The name of the assay to be used for downstream analysis.
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 highly variable feature (HVF) finding should be performed. Default is TRUE.
HVF_source: The source of highly variable features. Possible values are "global" and "separate". Default is "separate".
HVF_method: The method for selecting highly variable features. Default is "vst".
nHVF: The number of highly variable features to select. 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. The default value is 1.
HVF: A vector of highly variable features. Default is NULL.
do_scaling: Whether to perform scaling. If TRUE, the function will force to scale the data using the 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, all dimensions will be used.
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.
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.
bbknn_params: A list of parameters for the bbknn.matrix.bbknn function, default is an empty list.
verbose: Whether to print the message. Default is TRUE.
seed: An integer specifying the random seed for reproducibility. Default is 11.