meffil.ewas: Epigenome-wide association study
In perishky/meffil: Efficient algorithms for DNA methylation

meffil.ewas

R Documentation

Epigenome-wide association study

Description

Test association with each CpG site.

Usage

meffil.ewas(
  beta,
  variable,
  covariates = NULL,
  batch = NULL,
  weights = NULL,
  sites = NULL,
  samples = NULL,
  cell.counts = NULL,
  isva = F,
  sva = T,
  smartsva = F,
  smartsva.alpha = 0.5,
  n.sv = NULL,
  winsorize.pct = 0.05,
  robust = FALSE,
  rlm = FALSE,
  outlier.iqr.factor = NA,
  most.variable = 50000,
  featureset = NA,
  random.seed = 20161123,
  lmfit.safer = F,
  verbose = F
)

Arguments

`beta`	Methylation levels matrix, one row per CpG site, one column per sample or the filename of GDS (Genomic Data Structure) output from `meffil.normalize.samples`.
`variable`	Independent variable vector.
`covariates`	Covariates data frame to include in regression model, one row per sample, one column per covariate (Default: NULL).
`batch`	Batch vector to be included as a random effect (Default: NULL). Ignored if `beta` is a GDS filename.
`weights`	Non-negative observation weights. Can be a numeric matrix of individual weights of same dimension as `beta`, or a numeric vector of weights with length `ncol(beta)`, or a numeric vector of weights with length `nrow(beta)`.
`sites`	Restrict the EWAS to the given CpG sites – must match row names of `beta` (Default: NULL).
`samples`	Restrict the EWAS to the given samples – must match column names of `beta` (Default: NULL).
`cell.counts`	Proportion of cell counts for one cell type in cases where the samples are mainly composed of two cell types (e.g. saliva) (Default: NULL). Ignored if `beta` is a GDS filename.
`isva`	Apply Independent Surrogate Variable Analysis (ISVA) to the methylation levels and include the resulting variables as covariates in a regression model (Default: FALSE).
`sva`	Apply Surrogate Variable Analysis (SVA) to the methylation levels and covariates and include the resulting variables as covariates in a regression model (Default: TRUE).
`smartsva`	Apply the SmartSVA algorithm to the methylation levels and include the resulting variables as covariates in a regression model (Default: FALSE).
`smartsva.alpha`	alpha argument to SmartSVA providing the initial point for optimization. Smaller values reduce the number of iterations needed to reach convergence. Setting this 1 will produce exactly the outputs as SVA. (Default: 0.5).
`n.sv`	Number of surrogate variables to calculate (Default: NULL).
`winsorize.pct`	Apply all regression models to methylation levels winsorized to the given level. Set to NA to avoid winsorizing (Default: 0.05).
`robust`	Test associations with the 'robust' option when `limma::eBayes` is called (Default: TRUE). Ignored if `beta` is a GDS filename.
`rlm`	If `beta` is a matrix, then test associations with the 'robust' option when `limma:lmFit` is called. If `beta` is a GDS filename, then test associations using robust regression using `MASS::rlm` and calculate statistical significance using `lmtest::coeftest` with `vcov=sandwich::vcovHC(fit, type="HC0")` (Default: FALSE).
`outlier.iqr.factor`	For each CpG site, prior to fitting regression models, set methylation levels less than `Q1 - outlier.iqr.factor * IQR` or more than `Q3 + outlier.iqr.factor * IQR` to NA. Here IQR is the inter-quartile range of the methylation levels at the CpG site, i.e. Q3-Q1. Set to NA to skip this step (Default: NA).
`most.variable`	Apply (Independent) Surrogate Variable Analysis to the given most variable CpG sites (Default: 50000).
`featureset`	No longer used (Default: NA).
`verbose`	Set to TRUE if status updates to be printed (Default: FALSE).