preprocessSVGs: preprocessSVGs

In lmweber/spatzli: Methods for analyzing spatially resolved transcriptomics data

Description

Preprocessing steps to run SVGs methods

Usage

preprocessSVGs(
  spe,
  in_tissue = TRUE,
  filter_genes = 10,
  filter_mito = TRUE,
  seed = 123
)

Arguments

spe	SpatialExperiment Input data, assumed to be a SpatialExperiment object.
in_tissue	logical Whether to keep only spots over tissue, identified by column in_tissue in spatialData. Default = TRUE.
filter_genes	integer Filtering on UMI counts per gene. Genes with less than or equal to this number of total UMI counts across spots will be filtered out. Default = 10.
filter_mito	logical Whether to filter out mitochondrial genes. Assumes rowData contains a column named gene_name. Default = TRUE.
seed	integer Random seed for steps requiring random seed. Default = 123.

Details

Convenience function to run several preprocessing steps to prepare data for input to SVGs methods. Mainly intended for development work, but may also be useful for others. The steps are intended for 10x Genomics Visium data. Code is sourced from our online book OSTA, and makes use of the SpatialExperiment structure.

Value

Returns a SpatialExperiment object that is ready to be provided as the input to SVGs methods.

Examples

paste0("to do")

lmweber/spatzli documentation built on Feb. 2, 2022, 1:09 p.m.