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fastMNN_integrate

Source: R/SCP-workflow.R
fastMNN_integrate.Rd

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.