normalizeGiotto

normalizeGiotto

Description

fast normalize and/or scale expresion values of Giotto object

Usage

normalizeGiotto(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = "raw",
  norm_methods = c("standard", "pearson_resid", "osmFISH"),
  library_size_norm = TRUE,
  scalefactor = 6000,
  log_norm = TRUE,
  log_offset = 1,
  logbase = 2,
  scale_feats = TRUE,
  scale_genes = NULL,
  scale_cells = TRUE,
  scale_order = c("first_feats", "first_cells"),
  theta = 100,
  update_slot = "scaled",
  verbose = TRUE
)

Arguments

Argument	Description
gobject	giotto object
spat_unit	spatial unit
feat_type	feature type
expression_values	expression values to use
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_feats	z-score genes over all cells
scale_genes	deprecated, use scale_feats
scale_cells	z-score cells over all genes
scale_order	order to scale feats and cells
theta	theta parameter for the pearson residual normalization step
update_slot	slot or name to use for the results from osmFISH and pearson residual normalization
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: list()

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

• 1. Log transformation of data.

• 1. Z-scoring of data by genes and/or cells.
     B. The normalization method as provided by the osmFISH paper is also implemented: list()

• list(“1. First normalize genes, for each gene divide the counts by the total gene count andn”, “multiply by the total number of genes.”)

• list(“2. Next normalize cells, for each cell divide the normalized gene counts by the totaln”, “counts per cell and multiply by the total number of cells.”)
  C. The normalization method as provided by Lause/Kobak et al is also implemented: list()

• 1. First calculate expected values based on Pearson correlations.

• 1. Next calculate z-scores based on observed and expected values.
     By default the latter two results will be saved in the Giotto slot for scaled expression, this can be changed by changing the update_slot parameters

Value

giotto object