phoenics (version 0.3)

test_pathway: Pathway differential analysis based on longitudinal metabolomics data

Description

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method relies on a PCA step.

Usage

test_pathway(
  quantif,
  design,
  pathways = "auto",
  fixed,
  random,
  npc = 1L,
  model = c("lmer", "blmer"),
  organism = NULL,
  min_size = 2
)

Value

an object of class pathwayRes which is a list of pathway results. Each element of the list contain the following entries:

pathway_name

a character corresponding to the pathway name

pathway_code

a character corresponding to the pathway code

metabolites

a data.frame with the names and codes of the quantified metabolites in the pathway

PCA

the result of the pathway PCA (a PCA object as obtained from PCA)

model

the output of the mixed model fit

test_pathway

a data.frame with the p-values for each tested fixed effect

Arguments

quantif

data.frame or matrix of the metabolite quantification with samples in rows (sample identifiers must be row names) and metabolites in columns (metabolite codes must be column names)

design

data.frame or matrix with samples in rows (sample identifiers must be row names) and the different effects to be included in the model in columns. Column names must be provided and are used as arguments for fixed and random

pathways

data.frame or matrix with metabolites in rows (all metabolites in columns of quantif must have a row in this input) and the following information in columns:

• metabolite_code metabolite code

• metabolite_name metabolite name

• pathway_code pathway code (identifier)

• pathway_name name of the pathway

fixed

character vector of fixed effects to be included in the model. They must correspond to column names of design

random

character vector of random effects to be included in the model. They must correspond to column names of design

npc

number of PCs for the PCA step

model

a character string indicating if the model has to be fitted using lmer or blmer. Default to "lmer"

organism

organism code in KEGG database. Required if pathways = "auto" and ignored otherwise

min_size

minimal number of metabolites in a pathway. Required if pathways = "auto" and ignored otherwise. Default to 2

Author

Camille Guilmineau <[e-mail]>
Remi Servien <[e-mail]>
Nathalie Vialaneix <[e-mail]>

Details

If pathways = "auto", information on pathways in which metabolites are present is automatically obtained by the function pathway_search using KEGGREST that queries KEGG database. Results are specific to a given organism (passed in organism). Pathways containing less than min_size metabolites are filtered out.

See Also

pathway_search for the automatic annotation of metabolites in KEGG pathways.

Examples

data("MTBLS422")
quantif <- from_ASICS_to_PHOENICS(quantif)
out_test <- test_pathway(quantif, design, pathways, 
                         fixed = c("Age", "Treatment"), random = "Mouse", 
                         npc = 2, model = "blmer")
out_test

if (requireNamespace("KEGGREST", quietly = TRUE)) { 
# \donttest{
  out_test2 <- test_pathway(quantif, design, pathways = "auto", 
                            fixed = c("Age", "Treatment"), random = "Mouse", 
                            npc = 2, model = "blmer", organism = "mmu")
  out_test2
 # }
}