processSeuratObject: Process Seurat Objects in Parallel
In vertesy/Seurat.utils: Seurat.utils - utility functions for Seurat

processSeuratObject

R Documentation

Process Seurat Objects in Parallel

Description

Applies a series of Seurat processing steps to each Seurat object in a list. The operations include scaling data, running PCA, UMAP, finding neighbors, and finding clusters. This is done in parallel using multiple cores.

Usage

processSeuratObject(
  obj,
  param.list = p,
  update_gene_symbols = FALSE,
  add.meta.fractions = FALSE,
  precompute = TRUE,
  compute = TRUE,
  save = TRUE,
  plot = TRUE,
  nfeatures = param.list$n.var.genes,
  variables.2.regress = param.list$variables.2.regress.combined,
  harmony.covariates = variables.2.regress,
  n.PC = param.list$n.PC,
  resolutions = param.list$snn_res,
  reduction_input = "pca",
  WorkingDir = getwd(),
  harmony.seurat.implementation = FALSE,
  ...
)

Arguments

`obj`	A Seurat object to be processed.
`param.list`	A list of parameters used in the processing steps.
`update_gene_symbols`	A boolean indicating whether to update gene symbols from HGNC. Default: `FALSE`.
`add.meta.fractions`	A boolean indicating whether to add meta data for fractions of cells in each cluster. Default: `FALSE`.
`precompute`	A boolean indicating whether to compute steps: `FindVariableFeatures()`, `calc.q99.Expression.and.set.all.genes()`, `ScaleData()` and `RunPCA()` Default: `TRUE`.
`compute`	A boolean indicating whether to compute the steps: `IntegrateLayers() / RunHarmony()`, `RunUMAP()`, `FindNeighbors()`, and `FindClusters()`. Default: `TRUE`.
`save`	A boolean indicating whether to save the results. Default: `TRUE`.
`plot`	A boolean indicating whether to plot the results. Default: `TRUE`.
`nfeatures`	The number of variable genes to use. Default: 2000.
`variables.2.regress`	A list of variables to regress out. Default: NULL.
`harmony.covariates`	A list of covariates to use for Harmony. Default: variables.2.regress.
`n.PC`	The number of principal components to use. Default: 30.
`resolutions`	A list of resolutions to use for clustering. Default: c(0.1, 0.2, 0.3, 0.4, 0.5).
`reduction_input`	The reduction method to use as input for clustering & UMAP. Default: "pca".
`WorkingDir`	The working directory to save the results. Default: getwd().
`harmony.seurat.implementation`	A boolean indicating whether to use the Seurat implementation of Harmony. Default: `FALSE`.
`...`	Additional parameters to be passed to `ScaleData()`.

Value

A Seurat object after applying scaling, PCA, UMAP, neighbor finding, and clustering.

Examples

# Assuming ls.Seurat is a list of Seurat objects and params is a list of parameters
# results <- mclapply(ls.Seurat, processSeuratObject, params, mc.cores = 4)

vertesy/Seurat.utils documentation built on Dec. 4, 2024, 5:20 p.m.