calculateHVF#

Date

2022-10-06

https://github.com/drieslab/Giotto/tree/suite/R/variable_genes.R#L190

Description#

compute highly variable features

Usage#

calculateHVF(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  method = c("cov_groups", "cov_loess", "var_p_resid"),
  reverse_log_scale = FALSE,
  logbase = 2,
  expression_threshold = 0,
  nr_expression_groups = 20,
  zscore_threshold = 1.5,
  HVFname = "hvf",
  difference_in_cov = 0.1,
  var_threshold = 1.5,
  var_number = NULL,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "HVFplot",
  return_gobject = TRUE
)

Arguments#

Argument

Description

gobject

giotto object

spat_unit

spatial unit

feat_type

feature type

expression_values

expression values to use

method

method to calculate highly variable features

reverse_log_scale

reverse log-scale of expression values (default = FALSE)

logbase

if reverse_log_scale is TRUE, which log base was used?

expression_threshold

expression threshold to consider a gene detected

nr_expression_groups

[cov_groups] number of expression groups for cov_groups

zscore_threshold

[cov_groups] zscore to select hvg for cov_groups

HVFname

name for highly variable features in cell metadata

difference_in_cov

[cov_loess] minimum difference in coefficient of variance required

var_threshold

[var_p_resid] variance threshold for features for var_p_resid method

var_number

[var_p_resid] number of top variance features for var_p_resid method

show_plot

show plot

return_plot

return ggplot object

save_plot

directly save the plot [boolean]

save_param

list of saving parameters from `all_plots_save_f unction <#allplotssavefunction>`__

default_save_name

default save name for saving, don’t change, change save_name in save_param

return_gobject

boolean: return giotto object (default = TRUE)

Details#

Currently we provide 2 ways to calculate highly variable genes:

list(“1. high coeff of variance (COV) within groups:?) list() First genes are binned ( list(“nr_expression_groups?) ) into average expression groups and the COV for each feature is converted into a z-score within each bin. Features with a z-score higher than the threshold ( list(“zscore_threshold?) ) are considered highly variable. list()

list(“2. high COV based on loess regression prediction:?) list() A predicted COV is calculated for each feature using loess regression (COV~log(mean expression)) Features that show a higher than predicted COV ( list(“difference_in_cov?) ) are considered highly variable. list()

Value#

giotto object highly variable features appended to feature metadata (fDataDT)