fast normalize and/or scale expresion values of Giotto object

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

Value

giotto object

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.

Examples


data(mini_giotto_single_cell)

norm_gobject = normalizeGiotto(mini_giotto_single_cell)