Calculate Influence Scores Using Python Implementation

Calculate influence scores for seed neurons using the Python implementation.

Usage

calculate_influence_py(
  ic,
  seed_ids,
  silenced_neurons = numeric(0),
  seed_name = NULL,
  const = 24
)

Arguments

ic

InfluenceCalculator Python object created by influence_calculator_py().

seed_ids

Character vector. Root IDs of seed neurons.

silenced_neurons

Character vector. Root IDs of neurons to silence (default: empty vector).

seed_name

Optional, a seed name for the seed you are running. Will be added as a column value.

const

Constant value added to log(influence) to ensure non-negative adjusted influence scores. Should be set to -log(minimum_accepted_influence) where minimum_accepted_influence is the smallest influence value considered meaningful. Default 24 corresponds to minimum_accepted_influence = exp(-24) ~ 3.78e-11.

Value

Data frame with columns: matrix_index, id, is_seed, influence score, and adjusted_influence (sign-preserving in signed mode).

Examples

if (FALSE) { # \dontrun{
ic <- influence_calculator_py("connectome.sqlite")
results <- calculate_influence_py(ic, seed_ids = c(123, 456))
} # }