normalizeGiotto: normalizeGiotto

In bernard2012/Giotto: Spatial single-cell transcriptomics toolbox

Description

fast normalize and/or scale expresion values of Giotto object

Usage

normalizeGiotto(
  gobject,
  norm_methods = c("standard", "osmFISH"),
  library_size_norm = TRUE,
  scalefactor = 6000,
  log_norm = TRUE,
  log_offset = 1,
  logbase = 2,
  scale_genes = T,
  scale_cells = T,
  scale_order = c("first_genes", "first_cells"),
  verbose = F
)

Arguments

gobject	giotto object
norm_methods	normalization method to use
library_size_norm	normalize cells by library size
scalefactor	scale factor to use after library size normalization
log_norm	transform values to log-scale
log_offset	offset value to add to expression matrix, default = 1
logbase	log base to use to log normalize expression values
scale_genes	z-score genes over all cells
scale_cells	z-score cells over all genes
scale_order	order to scale genes and cells
verbose	be verbose

Details

Currently there are two 'methods' to normalize your raw counts data.

A. The standard method follows the standard protocol which can be adjusted using the provided parameters and follows the following order:

• 1. Data normalization for total library size and scaling by a custom scale-factor.

• 2. Log transformation of data.

• 3. Z-scoring of data by genes and/or cells.

B. The normalization method as provided by the osmFISH paper is also implemented:

• 1. First normalize genes, for each gene divide the counts by the total gene count and multiply by the total number of genes.

• 2. Next normalize cells, for each cell divide the normalized gene counts by the total counts per cell and multiply by the total number of cells.

This data will be saved in the Giotto slot for custom expression.

Value

giotto object

Examples

data(mini_giotto_single_cell)

norm_gobject = normalizeGiotto(mini_giotto_single_cell)

bernard2012/Giotto documentation built on Sept. 22, 2020, 10:29 a.m.