preprocessSVG: preprocessSVG

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

Description

Preprocessing steps to prepare input data for nnSVG

Usage

preprocessSVG(
  spe,
  in_tissue = TRUE,
  filter_genes = 5,
  filter_mito = TRUE,
  residuals = TRUE,
  logcounts = TRUE,
  deconv = TRUE
)

Arguments

spe	SpatialExperiment Input data, assumed to be a SpatialExperiment object containing an assay named counts containing unique molecular identifier (UMI) counts, and spatial coordinates stored in the spatialCoords slot.
in_tissue	logical Whether to keep only spots over tissue, identified by column in_tissue in colData. Default = TRUE.
filter_genes	integer Whether to filter low-expressed genes. If a value is provided, genes with at least 1 unique molecular identifier (UMI) count in at least this percentage of spatial coordinates will be kept. Assumes spe contains an assay named counts containing UMI counts. Default = 5, i.e. keep genes with at least 1 UMI in 5 spatial coordinates. Set to NULL to disable.
filter_mito	logical Whether to filter mitochondrial genes. Assumes the rowData slot of spe contains a column named gene_name, which can be used to identify mitochondrial genes. Default = TRUE. Set to FALSE to disable.
residuals	logical Whether to calculate deviance residuals from an approximate multinomial model using the scry package for input to BRISC. Default = TRUE.
logcounts	logical Whether to calculate log-transformed normalized counts (logcounts) using the scran package. Default = TRUE.
deconv	logical Whether to use deconvolution method to calculate logcounts (see ?scran::computeSumFactors). If FALSE, library size normalization will be used instead. Default = TRUE.

Details

Convenience function to run several preprocessing steps to prepare input data object for nnSVG. This function is designed for data from the 10x Genomics Visium platform, and is used for examples in the nnSVG package.

In general, the code in this function will need to be adapted for a given dataset, so we recommend running the steps individually instead of using this function. The steps are described in more detail in our online book "Orchestrating Spatially Resolved Transcriptomics Analysis with Bioconductor (OSTA)".

Value

Returns a SpatialExperiment object that can be provided to nnSVG.

Examples

library(SpatialExperiment)
library(STexampleData)

spe <- Visium_humanDLPFC()

# subset genes for faster runtime in this example
set.seed(123)
spe <- spe[sample(seq_len(1000)), ]

# set seed for reproducibility
set.seed(123)
spe <- preprocessSVG(spe)

spe

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