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ActivationGate

Source: R/ActivationGate.R
ActivationGate.Rd

Two-signal activation gate implementing the immunological principle that immune cell activation requires both antigen-specific recognition (Signal 1) AND costimulatory context (Signal 2).

Details

Prevents spurious activation on isolated outliers. An antibody is only allowed to clone if both signals exceed their thresholds. This is biologically-principled regularization.

Signal 2 options:

  • "density": Local data density around the antibody

  • "danger": User-provided danger signal vector

  • "entropy": Local label entropy (classification only)

Public fields

signal2_type

Type of costimulatory signal.

threshold1

Minimum affinity for Signal 1 (antigen recognition).

threshold2

Minimum costimulatory signal for Signal 2.

danger_signals

User-provided danger signal vector (for "danger" type).

Methods

Public methods

Method new()

Create a new ActivationGate.

Usage

ActivationGate$new(
  signal2_type = "density",
  threshold1 = 0.1,
  threshold2 = 0.3,
  danger_signals = NULL
)

Arguments

signal2_type

Character. "density", "danger", or "entropy".

threshold1

Numeric. Minimum affinity threshold.

threshold2

Numeric. Minimum Signal 2 threshold.

danger_signals

Numeric vector. Per-data-point danger scores.

Method evaluate()

Evaluate which antibody-data interactions pass the two-signal gate.

Usage

ActivationGate$evaluate(affinity_matrix, X, A, y = NULL, task = "clustering")

Arguments

affinity_matrix

Numeric matrix (n x m) of affinities.

X

Numeric matrix of data (n x d).

A

Numeric matrix of antibodies (m x d).

y

Target vector or NULL.

task

Character. Task type.

Returns

Logical matrix (n x m) where TRUE means the interaction is activated.

Method print()

Print summary.

Usage

ActivationGate$print(...)

Arguments

...

Not used.

Method clone()

The objects of this class are cloneable with this method.

Usage

ActivationGate$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Two-signal activation gate
data(iris)
X <- as.matrix(iris[, 1:4])
A <- X[sample(150, 10), ]
rep <- ImmuneRepertoire$new(A)
gate <- ActivationGate$new(signal2_type = "density", threshold2 = 0.3)
aff <- rep$affinity_matrix(X, "gaussian")
activated <- gate$evaluate(aff, X, A)
sum(activated)  # number of activated interactions
#> [1] 411