Output TreeSE

DESCRIPTION

@@ -18,7 +18,7 @@ License: Artistic-2.0 | file LICENSE
 Encoding: UTF-8
 Depends:
     R(>= 4.4.0),
-    SummarizedExperiment,
+    TreeSummarizedExperiment,
     MultiAssayExperiment
 Imports:
     dplyr,
@@ -29,8 +29,7 @@ Suggests:
     testthat,
     knitr,
     rmarkdown,
-    BiocStyle,
-    TreeSummarizedExperiment
+    BiocStyle
 URL: https://github.com/microbiome/HoloFoodR
 BugReports: https://github.com/microbiome/HoloFoodR/issues
 VignetteBuilder: knitr

NAMESPACE

@@ -5,7 +5,7 @@ export(getData)
 export(getMetaboLights)
 export(getResult)
 import(MultiAssayExperiment)
-import(SummarizedExperiment)
+import(TreeSummarizedExperiment)
 importFrom(S4Vectors,SimpleList)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,full_join)

R/HoloFoodR.R

@@ -10,10 +10,10 @@
 #' @name HoloFoodR
 #' @docType package
 #' @seealso
-#' \link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}
-#' \link[MultiAssayExperiment:MultiAssayExperiment-class]{MultiAssaySummarizedExperiment}
+#' \link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}
+#' \link[MultiAssayExperiment:MultiAssayExperiment-class]{MultiAssayExperiment}
 NULL
 
-#' @import SummarizedExperiment
+#' @import TreeSummarizedExperiment
 #' @import MultiAssayExperiment
 NULL

R/getResult.R

@@ -5,10 +5,10 @@
 #' database. Compared to \code{getData}, this function is more convenient for
 #' fetching the samples data because it converts the data to
 #' \code{MultiAssayExperiment} where different omics are stored as
-#' \code{SummarizedExperiment} objects which are optimized for downstream
+#' \code{TreeSummarizedExperiment} objects which are optimized for downstream
 #' analytics. Columns of returned \code{MultiAssayExperiment} are individual
 #' animals. These columns are linked with individual samples that are stored in
-#' \code{SummarizedExperiment} objects.
+#' \code{TreeSummarizedExperiment} objects.
 #' 
 #' The HoloFood database lacks non-targeted metabolomic data but fetched from
 #' MetaboLights resource. The function \code{getResult} facilitates the
@@ -46,7 +46,7 @@
 #'   use.cache (Default: \code{FALSE})
 #'   
 #'   \item \strong{assay.type} \code{Character scalar} specifying the name of
-#'   assay in resulting \code{SummarizedExperiment} object.
+#'   assay in resulting \code{TreeSummarizedExperiment} object.
 #'   (Default: \code{"counts"}) 
 #'   
 #' }
@@ -64,7 +64,7 @@
 #'
 #' @seealso
 #' \code{\link[HoloFoodR:getData]{getData}}
-#' \code{\link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}}
+#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}
 #' \code{\link[MultiAssayExperiment:MultiAssayExperiment-class]{MultiAssayExperiment}}
 #' \code{\link[MGnifyR:getResult]{MGnifyR:getResult}}
 #'
@@ -198,7 +198,7 @@
         rownames(temp) <- accessions
         temp <- DataFrame(temp, check.names = FALSE)
         # Create empty SE
-        se <- SummarizedExperiment(colData = temp)
+        se <- TreeSummarizedExperiment(colData = temp)
         return(se)
     })
     names(ses) <- type_names
@@ -266,8 +266,8 @@
     names(assays) <- assay.type
     feat_meta <- DataFrame(feat_meta, check.names = FALSE)
     sample_meta <- DataFrame(sample_meta, check.names = FALSE)
-    # Create SummarizedExperiment
-    se <- SummarizedExperiment(
+    # Create TreeSummarizedExperiment
+    se <- TreeSummarizedExperiment(
         assays = assays, rowData = feat_meta, colData = sample_meta)
 
     # The data still has sample names from MetaboLights and not from HoloFood
@@ -508,8 +508,8 @@
 
 # This function cretaes a MAE object from list of tables.
 .construct_omics_data_from_markers <- function(res, metadata){
-    # Convert each table to SummarizedExperiment
-    res <- lapply(res, .convert_type_to_SummarizedExperiment, metadata)
+    # Convert each table to TreeSummarizedExperiment
+    res <- lapply(res, .convert_type_to_TreeSummarizedExperiment, metadata)
     # Create MultiAssayExperiment
     res <- ExperimentList(res)
     res <- MultiAssayExperiment(res)
@@ -520,7 +520,7 @@
 # to SE.
 #' @importFrom stats reshape
 #' @importFrom S4Vectors SimpleList
-.convert_type_to_SummarizedExperiment <- function(
+.convert_type_to_TreeSummarizedExperiment <- function(
         type, metadata, assay.type = "counts", ...){
     # Check assay.type
     temp <- .check_input(assay.type, list("character scalar"))
@@ -581,10 +581,10 @@
     # Convert to DF
     col_data <- DataFrame(col_data, check.names = FALSE)
 
-    # Create SummarizedExperiment
+    # Create TreeSummarizedExperiment
     assays <- SimpleList(assay)
     names(assays) <- assay.type
-    se <- SummarizedExperiment(
+    se <- TreeSummarizedExperiment(
         assays = assays, rowData = row_data,
         colData = col_data
         )

README.md

@@ -7,7 +7,7 @@ With this tool, users can efficiently search and retrieve data from the EBI
 HoloFood resource.
 
 The retrieved data is available in `MultiAssayExperiment` /
-`SummarizedExperiment` format similarly to the data acquired with the
+`TreeSummarizedExperiment` format similarly to the data acquired with the
 `MGnifyR` package from the MGnify database. This compatibility ensures users
 can seamlessly combine and analyze datasets from both sources, leading to
 valuable insights into intricate biological systems.

tests/testthat/test-getResult.R

@@ -15,7 +15,7 @@ test_that("getResult", {
     res <- getResult(samples)
     # The data must be MAE that have SEs inside
     expect_s4_class(res, "MultiAssayExperiment")
-    expect_s4_class(res[[1]], "SummarizedExperiment")
+    expect_s4_class(res[[1]], "TreeSummarizedExperiment")
 
     # This function fetches corresponding values from MAE (getResult) and list of
     # data.frames (getData)
@@ -95,7 +95,7 @@ test_that("getResult", {
     res <- getResult(samples)
     # The data must be MAE that have SEs inside
     expect_s4_class(res, "MultiAssayExperiment")
-    expect_s4_class(res[[1]], "SummarizedExperiment")
+    expect_s4_class(res[[1]], "TreeSummarizedExperiment")
 
     ref <- getMetaboLights(
         "https://www.ebi.ac.uk/metabolights/ws/studies/MTBLS4381")

vignettes/HoloFoodR.Rmd

@@ -130,13 +130,13 @@ mae
 ```
 
 `MAE` object stores individual omics as
-`r BiocStyle::Biocpkg("SummarizedExperiment")` (`SE`) objects.
+`r BiocStyle::Biocpkg("TreeSummarizedExperiment")` (`TreeSE`) objects.
 
 ```{r show_mae}
 mae[[1]]
 ```
 
-In `SE`, each column represents a sample and rows represent features.
+In `TreeSE`, each column represents a sample and rows represent features.
 
 ### Incorporate with MGnify data
 
@@ -175,8 +175,6 @@ mae_metagenomic <- MGnifyR::getResult(mg, analysis_ids)
 ```
 
 ```{r get_metagenomic_data2, include = FALSE}
-library(TreeSummarizedExperiment)
-
 path <- system.file("extdata", "mae_metagenomic.rds", package = "HoloFoodR")
 mae_metagenomic <- readRDS(path)
 ```