GEM: General Effect Modelling

Description

General Effect Modelling

Usage

GEM(formula, data, contrasts = "contr.sum", add_residuals = TRUE, ...)
# S3 method for GEM
print(x, ...)
# S3 method for GEM
plot(
  x,
  y = 1,
  what = "raw",
  col = NULL,
  pch = NULL,
  model.line = (what %in% c("raw")),
  ylim = NULL,
  ylab = "",
  xlab = "",
  main = NULL,
  ...
)
tableGEM(object, variable)
# S3 method for GEM
summary(object, extended = TRUE, df = FALSE, ...)
# S3 method for summary.GEM
print(x, digits = 2, ...)

Value

GEM returns an object of class GEM containing effects, ER values (effect + residuals), fitted values, residuals, features, coefficients, dummy design, symbolic design, dimensions, highest level interaction and feature names.

Arguments

formula: a model formula specifying features and effects.
data: a data.frame containing response variables (features) and design factors or other groupings/continuous variables.
contrasts: a character containing the primary contrasts for use with mixlm::lm (default = "contr.sum").
add_residuals: Logical indicating if residuals should be added to the ER values (default = TRUE).
...: Additional arguments to plot
x: Object of class GEM.
y: Response name or number.
what: What part of GEM to plot; raw data (default), fits, residuals or a named model effect (can be combined with 'effect', see Examples).
col: Color of points, defaults to grouping. Usually set to a factor name or a column name in the input data with custom colours.
pch: Plot character of points, defaults to 1. Usually set to a factor name or a column name in the input data with custom symbols
model.line: Include line indicating estimates, default = TRUE. Can be an effect name.
ylim: Y axis limits (numeric, but defaults to NULL)
ylab: Y label (character)
xlab: X label (character)
main: Main title, defaults to y with description from what.
object: GEM object.
variable: Numeric for selecting a variable for extraction.
extended: Extended output in summary (default = TRUE).
df: Show degrees of freedom in summary (default = FALSE).
digits: integer number of digits for printing.

References

* Mosleth et al. (2021) Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis. Scientific Report, 11,4087. <doi:10.1038/s41598-021-82388-w>

* E.F. Mosleth et al. (2020). Comprehensive Chemometrics, 2nd edition; Brown, S., Tauler, R., & Walczak, B. (Eds.). Chapter 4.22. Analysis of Megavariate Data in Functional Omics. Elsevier. <doi:10.1016/B978-0-12-409547-2.14882-6>

Examples

Run this code

## Multiple Sclerosis
data(MS, package = "gemR")
# Subset to reduce runtime in example
MS$proteins <- MS$proteins[,20:70]

gem <- GEM(proteins ~ group * MS, data = MS)
print(gem)
summary(gem)                                    # Summary of GEM
plot(gem)                                       # Raw data, first feature
plot(gem,2)                                     # Raw data, numbered feature
plot(gem,'Q76L83', col='MS', pch='group')       # Selected colour and plot character
plot(gem,'Q76L83', what='effect MS',
     model.line='effect group')                 # Comparison of factors (points and lines)
print(effs <- colnames(gem$symbolicDesign))     # Inspect factor names
eeffs <- paste0("effect ", effs)
# Example compound plot
old.par <- par(mfrow = c(3,3), mar = c(2,4,4,1))
plot(gem,'Q76L83')                         # Raw data, named feature
plot(gem,'Q76L83', what='fits')            # Fitted values
plot(gem,'Q76L83', what='residuals')       # Residuals
plot(gem,'Q76L83', what=eeffs[1])           # Effect levels
plot(gem,'Q76L83', what=eeffs[2])           # ----||----
plot(gem,'Q76L83', what=eeffs[3])           # ----||----
plot(gem,'Q76L83', what=effs[1])            # ER values
plot(gem,'Q76L83', what=effs[2])            # --------||---------
plot(gem,'Q76L83', what=effs[3])            # --------||---------
par(old.par)

# Complete overview of GEM
tab <- tableGEM(gem, 1)

# In general there can be more than two, effects, more than two levels, and continuous effects:
MS$three <- factor(c(rep(1:3,33),1:2))
gem3    <- GEM(proteins ~ MS * group + three, data = MS)


## Candy assessment
data(candies, package = "HDANOVA")
gemC <- GEM(assessment ~ assessor*candy, data=candies)

# Permutation testing
gemC <- permutation(gemC)
summary(gemC)

# GEM-SCA with ellipsoids in score plots
gemSCA <- sca(gemC)
scoreplot(gemSCA, factor="candy", ellipsoids="confidence")

# GEM-PCA with group colours
gemPCA <- pca(gemC)
scoreplot(gemPCA, factor="candy",
  gr.col=gemPCA$symbolicDesign$candy)


## Lactobacillus
data(Lactobacillus, package = "gemR")
# Subset to reduce runtime in example
Lactobacillus$proteome <- Lactobacillus$proteome[,50:100]

gemLac <- GEM(proteome ~ strain * growthrate, data = Lactobacillus)
print(gemLac)
plot(gemLac)                            # Raw data, first feature
plot(gemLac,2)                          # Raw data, numbered feature
plot(gemLac,'P.LSA0316', col='strain',
    pch='growthrate')                   # Selected colour and plot character
plot(gemLac,'P.LSA0316', what='strain',
    model.line='growthrate')            # Selected model.line


 # Don't run this example, it takes too long
  ## Diabetes
  data(Diabetes, package = "gemR")
  gemDia <- GEM(transcriptome ~ surgery * T2D, data = Diabetes)
  print(gemDia)
  plot(gemDia)                            # Raw data, first feature
  plot(gemDia,2)                          # Raw data, numbered feature
  plot(gemDia,'ILMN_1720829', col='surgery',
      pch='T2D')                          # Selected colour and plot character