calculateHVG

compute highly variable genes

calculateHVG(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  method = c("cov_groups", "cov_loess"),
  reverse_log_scale = FALSE,
  logbase = 2,
  expression_threshold = 0,
  nr_expression_groups = 20,
  zscore_threshold = 1.5,
  HVGname = "hvg",
  difference_in_cov = 0.1,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "HVGplot",
  return_gobject = TRUE
)

Arguments

gobject

giotto object

expression_values

expression values to use

method

method to calculate highly variable genes

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

number of expression groups for cov_groups

zscore_threshold

zscore to select hvg for cov_groups

HVGname

name for highly variable genes in cell metadata

difference_in_cov

minimum difference in coefficient of variance required

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)

Value

giotto object highly variable genes appended to gene metadata (fDataDT)

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 gene is converted into a z-score within each bin. Genes 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 gene using loess regression (COV~log(mean expression)) Genes that show a higher than predicted COV (difference_in_cov) are considered highly variable.

Examples

data(mini_giotto_single_cell) # loads existing Giotto object

# update a giotto object
mini_giotto_single_cell <- calculateHVG(gobject = mini_giotto_single_cell,
                                        zscore_threshold = 0.1,
                                        nr_expression_groups = 3)

# return a data.table with the high variable genes annotated
hvg_dt <- calculateHVG(gobject = mini_giotto_single_cell,
                       zscore_threshold = 0.1, nr_expression_groups = 3,
                       return_plot = FALSE, return_gobject = FALSE)

# return the ggplot object
hvg_plot <- calculateHVG(gobject = mini_giotto_single_cell,
                       zscore_threshold = 0.1, nr_expression_groups = 3,
                       return_plot = TRUE, return_gobject = FALSE)