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Simulate dropouts via different functions on synthetic bulk data

simulate_dropouts.Rd

This function induces expression-level dependent sparsity on the data. The two possible functions are:

Logistic function:

With dropout probability defined as:

P(dropout) = clamp(1 / (1 + exp(shape * (ln(exp+1) - ln(midpoint+1)))), 0.3, 0.8) * (1 - global_sparsity) + global_sparsity

with the following characteristics:

  • Plateaus at global_sparsity dropout for high expression genes

  • Partial dropout preserves count structure via binomial thinning

  • Good for preserving variance-mean relationships

Power Decay function:

With dropout probability defined as:

P(dropout) = (midpoint / (exp + midpoint))^power * scale_factor * (1 - global_sparsity) + global_sparsity

with the following characteristics:

  • No plateau - high expression genes get substantial dropout

  • Complete dropout only (no partial dropout)

  • More uniform dropout across expression range

Usage

simulate_dropouts(
  object,
  dropout_function = c("log", "powerdecay"),
  dropout_midpoint = 5,
  dropout_shape = 0.5,
  power_factor = 0.3,
  global_sparsity = 0.25,
  seed = 123L
)

Arguments

object

The synthetic_bulk_data class.

dropout_function

String. One of c("log", "powerdecay"). These are the two options to cause dropout in the bulk data.

dropout_midpoint

Numeric. Controls the midpoint parameter of the logistic and power decay function.

dropout_shape

Numeric. Controls the shape parameter of the logistic function.

power_factor

Numeric. Controls the power factor of the power decay function.

global_sparsity

Numeric. The global sparsity parameter.

seed

Integer. Random seed for reproducibility purposes.

Value

synthetic_bulk_data with added sparse data.