Calculate HVG

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)
calculateHVG 
#> return_plot = TRUE and return_gobject = TRUE
#>
#>           plot will not be returned to object, but can still be saved with save_plot = TRUE or manually
#>
#>   hvg  has already been used, will be overwritten
# 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)
calculateHVG 
# 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)
calculateHVG