corrections in test_pathway function: test of inputs and documentation

NAMESPACE

@@ -4,12 +4,18 @@ S3method(head,pathwayRes)
 S3method(plot,pathwayRes)
 S3method(print,pathwayRes)
 S3method(summary,pathwayRes)
-export(PCApath)
 export(from_ASICS_to_PHOENICS)
 export(overlap_coefficient)
-import(blme)
-import(lme4)
+export(test_pathway)
 importFrom(FactoMineR,PCA)
+importFrom(KEGGREST,keggList)
+importFrom(MetaboAnalystR,CalculateOraScore)
+importFrom(MetaboAnalystR,CreateMappingResultTable)
+importFrom(MetaboAnalystR,CrossReferencing)
+importFrom(MetaboAnalystR,InitDataObjects)
+importFrom(MetaboAnalystR,SetKEGG.PathLib)
+importFrom(MetaboAnalystR,SetMetabolomeFilter)
+importFrom(MetaboAnalystR,Setup.MapData)
 importFrom(blme,blmer)
 importFrom(factoextra,fviz_eig)
 importFrom(factoextra,fviz_pca_ind)

R/test_pathway.R

-#' @title Test of metabolic pathways based on longitudinal metabolomics data
+#' @title Pathway differential analysis based on longitudinal metabolomics data
 #' 
-#' @description 
-#' TODO : explain the main steps of the function
+#' @description For each pathway, perform a PCA on the metabolites
+#'  quantification matrix and select a `npc` number of principal components, 
+#'  then fit a mixed model on the new matrix and test the fixed effects
+#'  significance
 #' 
-#' @param quantif dataframe or matrix of the quantification of the metabolites, with the samples in rownames and the metabolites codes in colnames
-#' @param design dataframe or matrix, contains the samples in rownames and the factors to be included in the model in colnames
-#' @param pathways dataframe or matrix, contains a row for each metabolite in a pathway
+#' @param quantif dataframe or matrix of the quantification of the metabolites,
+#'  with the samples in row names and the metabolites codes in column names
+#' @param design dataframe or matrix, contains the samples in rownames and the
+#'  factors to be included in the model in colnames
+#' @param pathways dataframe or matrix, contains the columns "pathway_name",
+#'  "pathway_code", "metabolite_code" and "metabolite_name" and a row for each
+#'   metabolite in a pathway
 #' @param npc number of PC to select
-#' @param model a character string indicating if the model should be fitted using lme or blme package
-#' @param fixed vector of fixed effects to be included in the model, must be in design colnames
-#' @param random vector of random effects to be included in the model, must be in design colnames
-#' @param organism organism code in KEGG database, needed if pathways == "auto"
+#' @param model a character string indicating if the model should be fitted
+#'  using lme or blme package
+#' @param fixed vector of fixed effects to be included in the model, must be in
+#'  design colnames
+#' @param random vector of random effects to be included in the model, must be
+#'  in design colnames
+#' @param organism organism code in KEGG database (three or four letters),
+#'  needed if pathways == "auto"
 #' 
-#' @return an object of class pathwayRes
-#' TODO : describe the returned object
+#' @return an object of class pathwayRes: list of pathways which contains for
+#'  each pathway:
+#' - a dataframe with the pathway name and pathway code
+#' - a dataframe with the metabolites names and metabolites codes in the pathway
+#' - a PCA object
+#' - the output of the model
+#' - a list of dataframes with the p-values (before and after correction) of
+#'  each tested effect
 #' 
 #' @importFrom FactoMineR PCA
 #' @importFrom lme4 lmer
 #' @importFrom blme blmer
 #' @importFrom tibble column_to_rownames
-#' @importFrom tibble rownames_to_column
+#' @importFrom KEGGREST keggList
 #' 
 #' @examples 
 #' load(system.file("data", "example.rda", package = "phoenics"))
@@ -42,7 +58,8 @@
 #' @export
 
 test_pathway <- function(quantif, design, pathways = "auto", fixed, random, 
-                         npc = 1L, model = c("lmer", "blmer"), organism = NULL) {
+                         npc = 1L, model = c("lmer", "blmer"),
+                         organism = NULL) {
   
   # Inputs tests
   if (!is.character(fixed)) {
@@ -76,7 +93,16 @@ test_pathway <- function(quantif, design, pathways = "auto", fixed, random,
       if (is.null(organism)) {
         stop("`organism` is missing.")
       }
-      # TODO: pathway_search(quantif, organism)
+      
+      kegg_org <- keggList("organism")
+      kegg_org <- as.data.frame(kegg_org)
+      kegg_org <- unique(kegg_org$organism)
+      
+      if (organism %in% kegg_org) {
+        # TODO: pathway_search(quantif, organism)
+      } else {
+        stop("`organism` must be KEGG organism code.")
+      }
     } else {
       stop("`pathways` must be \"auto\" or a dataframe or a matrix.")
     }
@@ -173,8 +199,7 @@ test_pathway <- function(quantif, design, pathways = "auto", fixed, random,
         return(out)
       })
       simes <- as.data.frame(simes)
-      simes <- tibble::rownames_to_column(simes, "pathway")
-      colnames(simes)[2] <- "pval"
+      colnames(simes)[1] <- "pval"
       simes$FDR <- p.adjust(simes$pval)
       return(simes)
     })

man/test_pathway.Rd

@@ -2,54 +2,67 @@
 % Please edit documentation in R/test_pathway.R
 \name{test_pathway}
 \alias{test_pathway}
-\title{Test of metabolic pathways based on longitudinal metabolomics data}
+\title{Pathway differential analysis based on longitudinal metabolomics data}
 \usage{
 test_pathway(
   quantif,
   design,
-  pathways,
-  npc,
-  model = c("lme", "blme"),
+  pathways = "auto",
   fixed,
   random,
-  organism
+  npc = 1L,
+  model = c("lmer", "blmer"),
+  organism = NULL
 )
 }
 \arguments{
-\item{quantif}{dataframe or matrix of the quantification of the metabolites, with the samples in rownames and the metabolites codes in colnames}
+\item{quantif}{dataframe or matrix of the quantification of the metabolites,
+with the samples in row names and the metabolites codes in column names}
 
-\item{design}{dataframe or matrix, contains the samples in rownames and the factors to be included in the model in colnames}
+\item{design}{dataframe or matrix, contains the samples in rownames and the
+factors to be included in the model in colnames}
 
-\item{pathways}{dataframe or matrix, contains a row for each metabolite in a pathway}
+\item{pathways}{dataframe or matrix, contains the columns "pathway_name",
+"pathway_code", "metabolite_code" and "metabolite_name" and a row for each
+ metabolite in a pathway}
 
-\item{npc}{number of PC to select}
+\item{fixed}{vector of fixed effects to be included in the model, must be in
+design colnames}
 
-\item{model}{a character string indicating if the model should be fitted using lme or blme package}
+\item{random}{vector of random effects to be included in the model, must be
+in design colnames}
 
-\item{fixed}{vector of fixed effects to be included in the model, must be in design colnames}
+\item{npc}{number of PC to select}
 
-\item{random}{vector of random effects to be included in the model, must be in design colnames}
+\item{model}{a character string indicating if the model should be fitted
+using lme or blme package}
 
-\item{organism}{organism code in KEGG database, needed if pathways == "auto"}
+\item{organism}{organism code in KEGG database (three or four letters),
+needed if pathways == "auto"}
 }
 \value{
-an object of class pathwayRes
-TODO : describe the returned object
+an object of class pathwayRes: list of pathways which contains for
+ each pathway:
+- a dataframe with the pathway name and pathway code
+- a dataframe with the metabolites names and metabolites codes in the pathway
+- a PCA object
+- the output of the model
+- a list of dataframes with the p-values (before and after correction) of
+ each tested effect
 }
 \description{
-TODO : explain the main steps of the function
+For each pathway, perform a PCA on the metabolites
+ quantification matrix and select a `npc` number of principal components, 
+ then fit a mixed model on the new matrix and test the fixed effects
+ significance
 }
 \examples{
-load(file = "data/design_example.rda")
-load(file = "data/pathways_example.rda")
-load(file = "data/quantif_example.rda")
-quantif <- format_ASICS_quantif(quantif)
-object <- test_pathway(quantif, design, pathways, npc = 2, model = "blme", 
-                       fixed = c("Age", "Treatment"), random = c("Mouse"))
-print.pathwayRes(object)
-summary.pathwayRes(object)
-plot.pathwayRes(object)
-head.pathwayRes(object)
+load(system.file("data", "example.rda", package = "phoenics"))
+quantif <- from_ASICS_to_PHOENICS(quantif)
+out_test <- test_pathway(quantif, design, pathways,
+ fixed = c("Age", "Treatment"), random = "Mouse", npc = 2L,
+ model = "blmer")
+out_test
 
 }
 \references{