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ImmuneRepertoire

Source: R/ImmuneRepertoire.R
ImmuneRepertoire.Rd

R6 class representing a collection of antibodies (immune cells) with associated metadata. Core data structure for bHIVE algorithms.

Details

An ImmuneRepertoire holds a matrix of antibody vectors (each row is one antibody in feature space) plus per-antibody metadata (isotype, state, age, lineage). All heavy computation is dispatched to C++ via RcppArmadillo.

Public fields

cells

Numeric matrix (nAntibodies x nFeatures).

metadata

Data frame with per-antibody attributes.

Methods

Public methods

Method new()

Create a new ImmuneRepertoire.

Usage

ImmuneRepertoire$new(cells, metadata = NULL)

Arguments

cells

Numeric matrix (nAntibodies x nFeatures).

metadata

Optional data frame with columns: isotype, state, age, lineage.

Method affinity_matrix()

Compute affinity matrix between data X and antibodies.

Usage

ImmuneRepertoire$affinity_matrix(
  X,
  method = "gaussian",
  params = list(alpha = 1, c = 1, p = 2)
)

Arguments

X

Numeric matrix (n x d) of data points.

method

Affinity function: "gaussian", "laplace", "polynomial", "cosine", "hamming".

params

List with alpha, c, p parameters.

Returns

Numeric matrix (n x m) of affinity values.

Method distance_matrix()

Compute distance matrix between data X and antibodies.

Usage

ImmuneRepertoire$distance_matrix(
  X,
  method = "euclidean",
  params = list(p = 2, Sigma = NULL)
)

Arguments

X

Numeric matrix (n x d).

method

Distance function: "euclidean", "manhattan", "minkowski", "cosine", "mahalanobis", "hamming".

params

List with p, Sigma parameters.

Returns

Numeric matrix (n x m) of distances.

Method suppress()

Network suppression: remove redundant antibodies.

Usage

ImmuneRepertoire$suppress(
  epsilon,
  method = "euclidean",
  params = list(p = 2, Sigma = NULL)
)

Arguments

epsilon

Distance threshold for suppression.

method

Distance function for suppression.

params

List with p, Sigma parameters.

Returns

Invisible self (modified in place).

Method size()

Get number of antibodies.

Usage

ImmuneRepertoire$size()

Returns

Integer.

Method n_features()

Get number of features.

Usage

ImmuneRepertoire$n_features()

Returns

Integer.

Method subset()

Subset the repertoire.

Usage

ImmuneRepertoire$subset(idx)

Arguments

idx

Integer vector of row indices to keep.

Returns

Invisible self (modified in place).

Method add()

Add antibodies to the repertoire.

Usage

ImmuneRepertoire$add(new_cells, new_metadata = NULL)

Arguments

new_cells

Numeric matrix (k x d) of new antibodies.

new_metadata

Optional data frame of metadata for new antibodies.

Returns

Invisible self (modified in place).

Method age_all()

Increment age of all antibodies.

Usage

ImmuneRepertoire$age_all()

Returns

Invisible self (modified in place).

Method as_matrix()

Convert to plain matrix.

Usage

ImmuneRepertoire$as_matrix()

Returns

Numeric matrix (nAntibodies x nFeatures).

Method print()

Print summary.

Usage

ImmuneRepertoire$print(...)

Arguments

...

Not used.

Method clone()

The objects of this class are cloneable with this method.

Usage

ImmuneRepertoire$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a repertoire from random antibodies
A <- matrix(rnorm(50), nrow = 10, ncol = 5)
rep <- ImmuneRepertoire$new(A)
print(rep)
#> <ImmuneRepertoire> 10 antibodies x 5 features
#>   Isotypes: 10 
#>   States:   10 

# Compute affinity to data
X <- matrix(rnorm(100), nrow = 20, ncol = 5)
aff <- rep$affinity_matrix(X, "gaussian", list(alpha = 1))
dim(aff)  # 20 x 10
#> [1] 20 10

# Network suppression
rep$suppress(epsilon = 1.5, method = "euclidean")
rep$size()  # fewer antibodies after suppression
#> [1] 8