analyze: Analyze

In bhagwataditya/autonomics: Unified statistal Modeling of Omics Data

Analyze

Description

Analyze

Usage

analyze(
  object,
  pca = TRUE,
  pls = TRUE,
  fit = "limma",
  formula = default_formula(object),
  drop = varlevels_dont_clash(object, all.vars(formula)),
  codingfun = contr.treatment,
  contrasts = NULL,
  coefs = colnames(create_design(object, formula = formula, drop = drop)),
  block = NULL,
  weightvar = if ("weights" %in% assayNames(object)) "weights" else NULL,
  plot = pca & !is.null(fit),
  label = "feature_id",
  palette = NULL,
  verbose = TRUE
)

Arguments

object	SummarizedExperiment
pca	TRUE / FALSE: perform pca ?
pls	TRUE / FALSE: perform pls ?
fit	linmod engine: 'limma', 'lm', 'lme(r)', 'lmer', 'wilcoxon'
formula	model formula
drop	TRUE / FALSE : drop varname in designmat ?
codingfun	factor coding function contr.treatment: intercept = y0, coefi = yi - y0 contr.treatment.explicit: intercept = y0, coefi = yi - y0 code_control: intercept = ymean, coefi = yi - y0 contr.diff: intercept = y0, coefi = yi - y(i-1) code_diff: intercept = ymean, coefi = yi - y(i-1) code_diff_forward: intercept = ymean, coefi = yi - y(i+) code_deviation: intercept = ymean, coefi = yi - ymean (drop last) code_deviation_first: intercept = ymean, coefi = yi - ymean (drop first) code_helmert: intercept = ymean, coefi = yi - mean(y0:(yi-1)) code_helmert_forward: intercept = ymean, coefi = yi - mean(y(i+1):yp)
contrasts	model coefficient contrasts of interest: string vector or NULL
coefs	model coefficients of interest: string vector or NULL
block	model blockvar
weightvar	NULL or name of weight matrix in assays(object)
plot	TRUE / FALSE
label	fvar
palette	NULL or colorvector
verbose	TRUE / FALSE: message?

Value

SummarizedExperiment

Examples

file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
object <- read_metabolon(file)
object %<>% analyze()

bhagwataditya/autonomics documentation built on May 8, 2024, 8:16 p.m.