process_from_count: Count Matrix To Seurat Object

In scMappR: Single Cell Mapper

Count Matrix To Seurat Object

Description

This function processes a list of count matrices (same species/gene symbols in each list) and converts them to a Seurat object.

Usage

process_from_count(
  countmat_list,
  name,
  theSpecies = -9,
  haveUmap = FALSE,
  saveALL = FALSE,
  panglao_set = FALSE,
  toSave = FALSE,
  path = NULL,
  use_sctransform = FALSE,
  genes_integrate = 2000,
  genes_include = FALSE
)

Arguments

countmat_list	A list of count matrices that will be integrated using the IntegrationAnchors features they should have the same rownames. A dgCMatrix or matrix object is also acceptable, and no samples will be integrated.
name	The output of the normalized and fused Seurat object if you choose to keep it.
theSpecies	Gene symbols for human, mouse, or -9 if internal. If your species is not human or mouse gene symbols, make sure that you have "MT-" before your mitochondrial gene names then pick "human".
haveUmap	Write a UMAP (T/F).
saveALL	Save the Seurat object generated (T/F).
panglao_set	If the function is being used from internal (T/F).
toSave	Allows scMappR to print files and make directories locally (T/F).
path	If toSave == TRUE, path to the directory where files will be saved.
use_sctransform	If you should use sctransform or the Normalize/VariableFeatures/ScaleData pipeline (T/F).
genes_integrate	The number of genes to include in the integration anchors feature when combining datasets
genes_include	TRUE or FALSE – include 2000 genes in signature matrix or all matrix.

Details

This function takes a list of count matrices and returns a Seurat object of the count matrices integrated using Seurat v4 (and IntegrationAnchors feature). Different normalization features such as the SCTransform pipeline are also available in this function. Different options are used when the function is being ran internally (i.e. reprocessing count matrices from PanglaoDB) or if it is running from custom scRNA-seq data. Larger scRNA-seq datasets can take considerable amounts of memory and run-time. See Seurat for details.

Value

process_from_count A processed and integrated Seurat object that has been scaled and clustered. It can be returned as an internal object or also stored as an RData object if necessary.

Examples

data(sm)
toProcess <- list(example = sm)
tst1 <- process_from_count(countmat_list = toProcess, name = "testProcess", 
                           theSpecies = "mouse")

scMappR documentation built on July 9, 2023, 6:26 p.m.