fastMNN_integrate: fastMNN_integrate
In zh542370159/SCP: Single Cell Pipeline

fastMNN_integrate

R Documentation

fastMNN_integrate

Description

fastMNN_integrate

Usage

fastMNN_integrate(
  srtMerge = NULL,
  batch = NULL,
  append = TRUE,
  srtList = 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,
  fastMNN_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,
  fastMNN_params = list(),
  seed = 11
)

Arguments

`srtMerge`	A merged Seurat object that includes the batch information.
`batch`	A character string specifying the batch variable name.
`append`	Logical, if TRUE, the integrated data will be appended to the original Seurat object (srtMerge).
`srtList`	A list of Seurat objects to be checked and preprocessed.
`assay`	The name of the assay to be used for downstream analysis.
`do_normalization`	A logical value indicating whether data normalization should be performed.
`normalization_method`	The normalization method to be used. Possible values are "LogNormalize", "SCT", and "TFIDF". Default is "LogNormalize".
`do_HVF_finding`	A logical value indicating 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.
`fastMNN_dims_use`	A vector specifying the dimensions returned by fastMNN that will be utilized for downstream cell cluster finding and non-linear 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`	A logical value indicating whether to force nonlinear dimensionality reduction even if the specified reduction is already present in the Seurat object.
`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).
`fastMNN_params`	A list of parameters for the batchelor::fastMNN function, default is an empty list.
`seed`	An integer specifying the random seed for reproducibility. Default is 11.