`filterCombinations`¶

filterCombinations

Description¶

Shows how many genes and cells are lost with combinations of thresholds.

Usage¶

filterCombinations(
  gobject,
  expression_values = c("raw", "normalized", "scaled", "custom"),
  expression_thresholds = c(1, 2),
  gene_det_in_min_cells = c(5, 50),
  min_det_genes_per_cell = c(200, 400),
  scale_x_axis = "identity",
  x_axis_offset = 0,
  scale_y_axis = "identity",
  y_axis_offset = 0,
  show_plot = TRUE,
  return_plot = FALSE,
  save_plot = NA,
  save_param = list(),
  default_save_name = "filterCombinations"
)

Arguments¶

Argument	Description
`gobject`	giotto object
`expression_values`	expression values to use
`expression_thresholds`	all thresholds to consider a gene expressed
`gene_det_in_min_cells`	minimum number of cells that should express a gene to consider that gene further
`min_det_genes_per_cell`	minimum number of expressed genes per cell to consider that cell further
`scale_x_axis`	ggplot transformation for x-axis (e.g. log2)
`x_axis_offset`	x-axis offset to be used together with the scaling transformation
`scale_y_axis`	ggplot transformation for y-axis (e.g. log2)
`y_axis_offset`	y-axis offset to be used together with the scaling transformation
`show_plot`	show plot
`return_plot`	return only ggplot object
`save_plot`	directly save the plot [boolean]
`save_param`	list of saving parameters from ``all_plots_save_function` <#allplotssavefunction>`_
`default_save_name`	default save name for saving, don’t change, change save_name in save_param

Details¶

Creates a scatterplot that visualizes the number of genes and cells that are: lost with a specific combination of a gene and cell threshold given an arbitrary cutoff to call a gene expressed. This function can be used to make an informed decision at the filtering step with filterGiotto.

Value¶

list of data.table and ggplot object

Examples¶

data(mini_giotto_single_cell)

# assess the effect of multiple filter criteria
filterCombinations(mini_giotto_single_cell,
gene_det_in_min_cells = c(2, 4, 8),
min_det_genes_per_cell = c(5, 10, 20))