calculateHVF: calculateHVF
In drieslab/Giotto_site_suite: Spatial Single-Cell Transcriptomics Toolbox

calculateHVF

R Documentation

calculateHVF

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

`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_function`
`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:

1. high coeff of variance (COV) within groups:
First genes are binned (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 (zscore_threshold) are considered highly variable.

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