The CSS integration function

Usage

CSS_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,
  CSS_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,
  CSS_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: Whether 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: Which assay to use. If NULL, the default assay of the Seurat object will be used.
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 to perform high variable feature finding. If TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.
HVF_source: The source of highly variable features. Possible values are "global" and "separate". Default is "separate".
HVF_method: The method to use for finding highly variable features. Options are "vst", "mvp", or "disp". Default is "vst".
nHVF: The number of highly variable features to select. If NULL, all highly variable features will be used. 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. Default is 1.
HVF: A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.
do_scaling: Whether to perform scaling. If TRUE, the function will force to scale the data using the Seurat::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.
CSS_dims_use: A vector specifying the dimensions returned by CSS 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: 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.
neighbor_metric: The distance metric to use for finding neighbors. Options are "euclidean", "cosine", "manhattan", or "hamming". Default is "euclidean".
neighbor_k: The number of nearest neighbors to use for finding neighbors. Default is 20.
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.
CSS_params: A list of parameters for the [simspec::cluster_sim_spectrum](https://github.com/quadbio/simspec) function. Default is `list()`.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed for reproducibility. Default is 11.