Package {PONG2}

Internal PONG2 Functions

Description

Internal functions used by PONG2 for KIR genotype imputation. These functions are adapted from the HIBAG package (Zheng et al. 2014, <doi:10.1016/j.ajhg.2013.12.015>) and are not intended to be called directly by users.

Value

No return value, called for side effects or internal use only.

PONG2 Example Dataset

Description

A small example dataset for demonstrating PONG2 functions. Contains 50 samples and 200 SNPs in the KIR region (chr19), along with a pre-trained KIR3DL1 model with 10 classifiers.

Usage

data(PONG2_example)

Format

Three objects are loaded:

example_snp

A hlaSNPGenoClass object with 50 samples and 200 SNPs in the KIR region (chr19, hg19 assembly)

example_kir

A hlaAlleleClass object with KIR3DL1 allele calls for 50 samples

example_mobj

A hlaAttrBagObj object — a pre-trained KIR3DL1 model with 10 ensemble classifiers

Examples

data(PONG2_example)
# SNP data
cat("Samples:", ncol(example_snp$genotype), "\n")
cat("SNPs:   ", nrow(example_snp$genotype), "\n")
# KIR allele table
cat("Locus:  ", example_kir$locus, "\n")
# Model
cat("Classifiers:", length(example_mobj$classifiers), "\n")

Train KIR prediction models in parallel

Description

Train KIR genotype prediction models using parallel attribute bagging across multiple CPU cores. This is the core training function used by the pong2 train CLI command.

Usage

kirParallelAttrBagging(
  cl,
  hla,
  snp,
  auto.save = "",
  nclassifier = 100,
  mtry = c("sqrt", "all", "one"),
  prune = TRUE,
  rm.na = TRUE,
  stop.cluster = FALSE,
  verbose = TRUE
)

Arguments

Value

An object of class hlaAttrBagClass representing the trained PONG2 KIR prediction model. The object contains:

n.samp

integer; number of training samples

n.snp

integer; number of SNP predictors used

hla.locus

character; the KIR locus name

hla.allele

character vector; KIR alleles in the model

classifiers

list; individual ensemble classifiers

out.of.bag.acc

numeric; out-of-bag accuracy estimate

Use kirPredict() to apply the model to new samples.

Examples

# Load example data
data(PONG2_example)

# Set up parallel cluster
cl <- parallel::makeCluster(2)

# Train a small model
model <- kirParallelAttrBagging(
  cl          = cl,
  hla         = example_kir,
  snp         = example_snp,
  nclassifier = 20,
  verbose     = FALSE
)

parallel::stopCluster(cl)

# View model summary
print(model)

# Clean up
hlaClose(model)

Predict KIR genotypes from SNP data

Description

Predict KIR genotypes for a set of samples using a trained PONG2 attribute bagging model. This is the core prediction function used by the pong2 impute CLI command.

Usage

kirPredict(
  object,
  snp,
  cl = FALSE,
  type = c("response+dosage", "response", "prob", "response+prob"),
  vote = c("prob", "majority"),
  allele.check = TRUE,
  match.type = c("Position", "Pos+Allele", "RefSNP+Position", "RefSNP"),
  same.strand = FALSE,
  verbose = TRUE,
  verbose.match = TRUE
)

Arguments

Value

An object of class hlaAlleleClass containing KIR imputation results. The object includes:

value

data frame with columns sample.id, allele1, allele2, and prob (posterior probability of the best call)

dosage

numeric matrix of allele dosage scores (samples x alleles); NULL if type = "response"

postprob

numeric matrix of posterior probabilities (alleles x samples); NULL unless type = "response+prob" or "prob"

Samples with posterior probability below the call threshold (CT) are assigned NA for both alleles.

Examples

# Load example data
data(PONG2_example)

# Load model from object
model <- hlaModelFromObj(example_mobj)

# Predict KIR genotypes
pred <- kirPredict(
  object  = model,
  snp     = example_snp,
  type    = "response+prob",
  verbose = FALSE
)

# View results
head(pred$value)

# Clean up
hlaClose(model)