Summarize CpG-wise Gaussian GLM fits for one-timepoint analyses — summarizeMethylationGLM

Extract phenotype-specific CpG coefficient tables from the fitted model object returned by fitMethylationGLM_T1Models().

summarizeMethylationGLM_T1Models(
  modelResults,
  preparedData,
  summaryResidualSD = TRUE,
  summaryPval = NA,
  nCores = 1L,
  libPath = NULL,
  glmLibs = "glm2",
  chunkSize = NULL,
  verbose = FALSE,
  logs = FALSE,
  log_dir = NULL,
  log_file = "log_methylationGLM_T1.txt"
)

Arguments

modelResults: Object returned by fitMethylationGLM_T1Models().
preparedData: Object returned by prepareMethylationGLM_T1Data().
summaryResidualSD: Logical. If TRUE, add residual standard deviations to each CpG summary row.
summaryPval: Numeric or NA. Optional p-value filter applied to the returned summary tables. NA keeps all rows.
nCores: Integer. Number of worker processes to use while extracting summary rows.
libPath: Character vector or NULL. Optional library paths forwarded to worker processes.
glmLibs: Character vector or comma-separated string of package names to check on worker processes. The default is "glm2".
chunkSize: Integer or NULL. Number of CpGs to process per parallel chunk. NULL chooses a value automatically.
verbose: Logical. If TRUE, emit progress messages with message().
logs: Logical. If TRUE, write the same messages to a log file.
log_dir: Character or NULL. Directory used for the log file when logs = TRUE.
log_file: Character. File name used when logs = TRUE.

Value

A list with class "dnaEPICO_methylationGLM_T1_summaries" containing one CpG-level summary data frame per phenotype.

Examples

ex <- dnaEPICO:::exampleMethylationGLMStateDnaEpico()
summary_results <- summarizeMethylationGLM_T1Models(
  modelResults = ex$modelResults,
  preparedData = ex$preparedData,
  summaryResidualSD = TRUE,
  summaryPval = NA,
  nCores = 1,
  verbose = FALSE,
  logs = FALSE
)
names(summary_results$summaries)
#> [1] "status"