structuralEnrichment: Structural enrichment

In jasenfinch/riches: Strucutral and Functional Enrichment Analyses

Structural enrichment

Description

Perform structural enrichment using over-representation analysis of explanatory m/z features from random forest.

Usage

structuralEnrichment(
  x,
  structural_classifications,
  p_adjust_method = "bonferroni",
  split = c("none", "trends"),
  ...
)

## S4 method for signature 'RandomForest,tbl_df'
structuralEnrichment(
  x,
  structural_classifications,
  p_adjust_method = "bonferroni",
  split = c("none", "trends"),
  ...
)

## S4 method for signature 'RandomForest,Construction'
structuralEnrichment(
  x,
  structural_classifications,
  p_adjust_method = "bonferroni",
  split = c("none", "trends"),
  ...
)

Arguments

x	an object of S4 class RandomForest
structural_classifications	the structral classifications corresponding to the m/z features present in the object specified for argument x. This should either be a tibble as returned by construction::classifications() or an object of S4 class Construction.
p_adjust_method	the p-value adjustment method. One of those returned from p.adjust.methods.
split	split the explanatory features into further groups based on their trends. See details.
...	arguments to pass to metabolyseR::explanatoryFeatures()

Details

Over-representation analysis is performed on the explanatory m/z features for each structural class within each experimental class comparison using the Fisher's Exact Test.

For argument split = 'trends', the explanatory features can be split into further groups based on their trends. This is not supported for unsupervised random forest.

For random forest classification, this is for binary comparisons only. Functional enrichment is performed seperately on the up and down regulated explanatory features for each comparison. The ⁠up regulated⁠ and ⁠down regulated⁠ groups are based on the trends of log2 ratios between the comparison classes. ⁠up regulated⁠ explanatory features have a higher median intensity in the right-hand class compared to the left-hand class of the comparison. The opposite is true for the ⁠down regulated⁠ explanatory features.

For random forest regression, the explanatory features are split based on their Spearman's correlation coefficient with the response variable prior to functional enrichment analysis giving ⁠positively correlated⁠ and ⁠negatively correlated⁠ subgroups.

Value

An object of S4 class StructuralEnrichment.

Examples

## Perform random forest on the example data 
random_forest <- assigned_data %>% 
  metabolyseR::randomForest(
    cls = 'class'
  )

## Perform structural enrichment analysis using the example structural classifications
structuralEnrichment(
  random_forest,
  structural_classifications
)

## An example using split trends
## Perform random forest on the example data 
random_forest <- assigned_data %>% 
  metabolyseR::randomForest(
    cls = 'class',
    binary = TRUE
  )

## Perform structural enrichment analysis using the example structural classifications
structuralEnrichment(
  random_forest,
  structural_classifications,
  split = 'trends'
)

jasenfinch/riches documentation built on April 24, 2023, 9:45 a.m.