normalizeGiotto#

Date

2022-10-06

https://github.com/drieslab/Giotto/tree/suite/R/auxiliary_giotto.R#L2143

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 and:raw-latex:n?, “multiply by the total number of genes.?)

  • list(“2. Next normalize cells, for each cell divide the normalized gene counts by the total:raw-latex:n?, “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