Standard_SCP: Standard SCP
In zh542370159/SCP: Single Cell Pipeline

Standard_SCP

R Documentation

Standard SCP

Description

This function performs a standard single-cell analysis workflow.

Usage

Standard_SCP(
  srt,
  prefix = "Standard",
  assay = NULL,
  do_normalization = NULL,
  normalization_method = "LogNormalize",
  do_HVF_finding = TRUE,
  HVF_method = "vst",
  nHVF = 2000,
  HVF = NULL,
  do_scaling = TRUE,
  vars_to_regress = NULL,
  regression_model = "linear",
  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,
  seed = 11
)

Arguments

`srt`	A Seurat object.
`prefix`	A prefix to add to the names of intermediate objects created by the function (default is "Standard").
`assay`	The name of the assay to use for the analysis. If NULL, the default assay of the Seurat object will be used.
`do_normalization`	A logical value indicating whether to perform normalization. If NULL, normalization will be performed if the specified assay does not have scaled data.
`normalization_method`	The method to use for normalization. Options are "LogNormalize", "SCT", or "TFIDF" (default is "LogNormalize").
`do_HVF_finding`	A logical value indicating 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_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.
`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`	A logical value indicating whether to perform scaling. If TRUE, the function will force to scale the data using the ScaleData function.
`vars_to_regress`	A vector of feature names to use as regressors in the scaling step. If NULL, no regressors will be used.
`regression_model`	The regression model to use for scaling. Options are "linear", "poisson", or "negativebinomial" (default is "linear").
`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`	A logical value indicating 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`	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).
`seed`	The random seed to use for reproducibility (default is 11).

Value

A Seurat object.

Examples

data("pancreas_sub")
pancreas_sub <- Standard_SCP(pancreas_sub)
CellDimPlot(pancreas_sub, group.by = "SubCellType")

# Use a combination of different linear or non-linear dimension reduction methods
linear_reductions <- c("pca", "ica", "nmf", "mds", "glmpca")
pancreas_sub <- Standard_SCP(
  pancreas_sub,
  linear_reduction = linear_reductions,
  nonlinear_reduction = "umap"
)
plist1 <- lapply(linear_reductions, function(lr) {
  CellDimPlot(pancreas_sub,
    group.by = "SubCellType",
    reduction = paste0("Standard", lr, "UMAP2D"),
    xlab = "", ylab = "", title = lr,
    legend.position = "none",
    theme_use = "theme_blank"
  )
})
patchwork::wrap_plots(plotlist = plist1)

nonlinear_reductions <- c("umap", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", "fr")
pancreas_sub <- Standard_SCP(
  pancreas_sub,
  linear_reduction = "pca",
  nonlinear_reduction = nonlinear_reductions
)
plist2 <- lapply(nonlinear_reductions, function(nr) {
  CellDimPlot(pancreas_sub,
    group.by = "SubCellType",
    reduction = paste0("Standardpca", toupper(nr), "2D"),
    xlab = "", ylab = "", title = nr,
    legend.position = "none",
    theme_use = "theme_blank"
  )
})
patchwork::wrap_plots(plotlist = plist2)

zh542370159/SCP documentation built on Nov. 22, 2023, 2:34 a.m.