Improve vignettes

.gitignore

@@ -10,3 +10,4 @@ inst/doc
 vignettes/*.html
 vignettes/*.R
 iSEEtree.BiocCheck/
+tests/testthat/*.pdf

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: iSEEtree
-Version: 0.99.3
+Version: 0.99.4
 Authors@R: 
     c(person(given = "Giulio", family = "Benedetti", role = c("aut", "cre"),
            email = "[email protected]",

R/class-RowTreePlot.R

@@ -122,7 +122,7 @@ RowTreePlot <- function(...) {
 #' @importFrom methods slot
 setMethod(".defineDataInterface", "RowTreePlot", function(x, se, select_info) {
   panel_name <- .getEncodedName(x)
-
+  print(select_info)
   list(.checkboxInput.iSEE(x, field="order_tree", label="Order tree",
                            value=slot(x, "order_tree")))
 })

README.md

@@ -6,9 +6,26 @@
 [![codecov](https://codecov.io/gh/microbiome/iSEEtree/branch/devel/graph/badge.svg)](https://app.codecov.io/gh/microbiome/iSEEtree?branch=devel)
 [![codefactor](https://www.codefactor.io/repository/github/microbiome/iseetree/badge)](https://www.codefactor.io/repository/github/microbiome/iseetree)
 
-The goal of iSEEtree is to provide panels related to TreeSummarizedExperiment
-objects which facilitate the interactive visualisation of microbiome data in
-[_iSEE_](https://isee.github.io/).
+## Introduction
+
+iSEEtree is a Bioconductor package for the interactive visualisation of
+microbiome data stored in a TreeSummarizedExperiment (TreeSE) container. On the
+one side, it leverages and extends the graphics of the iSEE package, which is
+designed for the generic SummarizedExperiment class. On the other side, it
+employs the statistical and visual tools for microbiome data science provided by
+the mia family of packages. Thus, iSEE and mia represent the two building blocks
+of iSEEtree. Detailed introductory material on these two frameworks is available
+in the [iSEE-verse website](https://isee.github.io/) and the
+[OMA Bioconductor book](https://microbiome.github.io/OMA/docs/devel/),
+respectively.
+
+iSEEtree is meant for new and experienced users alike, who desire to create and
+interact with several graphics for microbiome data, without an in-depth
+knowledge of the underlying mia functionality. Current microbiome-specific
+panels include phylogenetic trees, ordination plots and compositional plots,
+which can be further explored below in this article. Other more generic panels
+are also reused from the iSEE package and can be experimented in
+[this article](https://isee.github.io/iSEE/articles/basic.html).
 
 ## Installation instructions
 The release version of iSEEtree can be installed from Bioconductor as follows:
@@ -30,7 +47,7 @@ if (!requireNamespace("BiocManager", quietly = TRUE))
 # The following initializes usage of Bioc devel
 BiocManager::install(version='devel')
 
-BiocManager::install("mia")
+BiocManager::install("iSEEtree")
 ```
 
 ## Example
@@ -57,13 +74,65 @@ if (interactive()) {
 }
 ```
 
+## Panels
+
+iSEEtree derives its microbiome-related visualisation methods from the
+[miaViz](https://microbiome.github.io/miaViz/) package, which is code-based and
+requires basic knowledge of R programming and microbiome data structures. The
+following panels represent an easy-to-use interactive version of the miaViz
+plotting functions:
+
+- [AbundanceDensityPlot](https://microbiome.github.io/iSEEtree/reference/AbundanceDensityPlot.html):
+  a density plot of the top features, where every line is a feature and the x
+  axis shows its abundance for different samples. Its interpretation is
+  explained in the OMA chapter on
+  [Exploration](https://microbiome.github.io/OMA/docs/devel/pages/12_quality_control.html).
+- [AbundancePlot](https://microbiome.github.io/iSEEtree/reference/AbundancePlot.html):
+  a compositional barplot of the samples, where every bar is a sample composed
+  by different features in different colours. Its interpretation is explained
+  in the OMA chapter on
+  [Community Composition](https://microbiome.github.io/OMA/docs/devel/pages/21_microbiome_community.html).
+- [RDAPlot](https://microbiome.github.io/iSEEtree/reference/RDAPlot.html): an
+  supervised ordination plot of the samples, where every dot is a sample on a
+  reduced dimensional space and every arrow reflects the contribution of a
+  sample variable. Its interpretation is explained in the OMA chapter on
+  [Community Similarity](https://microbiome.github.io/OMA/docs/devel/pages/20_beta_diversity.html).
+- [RowTreePlot](https://microbiome.github.io/iSEEtree/reference/RowTreePlot.html):
+  a phylogenetic tree of the features, where every tip is a feature and two
+  neighbouring tips are more closely related than two far apart from each other.
+
+By default, the iSEEtree layout also includes the following panels inherited by
+iSEE:
+
+- [RowDataTable](https://isee.github.io/iSEE/articles/basic.html#row-data-tables)
+- [ColumnDataTable](https://isee.github.io/iSEE/articles/basic.html#column-data-tables)
+- [ReducedDimensionPlot](https://isee.github.io/iSEE/articles/basic.html#reduced-dimension-plots)
+- [ComplexHeatmapPlot](https://isee.github.io/iSEE/articles/basic.html#heat-maps)
+
+The [ColumnDataPlot](https://isee.github.io/iSEE/articles/basic.html#coldataplot)
+could also prove useful for the visualisation of column variables such as
+alpha diversity indices. Its interpretation is explained in the OMA chapter on
+[Community Diversity](https://microbiome.github.io/OMA/docs/devel/pages/14_alpha_diversity.html).
+
+## Code of Conduct
+Please note that the iSEEtree project is released with a
+[Contributor Code of Conduct](https://bioconductor.org/about/code-of-conduct/).
+By contributing to this project, you agree to abide by its terms. Contributions
+are welcome in the form of feedback, issues and pull requests. You can find the
+contributor guidelines of the miaverse
+[here](https://github.com/microbiome/mia/blob/devel/CONTRIBUTING.md).
+
+## Acknowledgements
 Please note that iSEEtree was only made possible thanks to many other R and
 bioinformatics software authors, which are cited either in the vignettes and/or
 the paper(s) describing this package. In particular, iSEEtree implements the [_miaViz_](https://microbiome.github.io/miaViz/) package for microbiome data
 visualisation to create panels that are specific for TreeSummarizedExperiment
 objects. Not surprisingly, it also depends on the generic panels from iSEE.
 
-## Development tools
+This package was developed using the following resources:
+
+- [_usethis_](https://cran.r-project.org/web/packages/usethis/) to generate an
+  initial template.
 - Continuous code testing is performed on
   [GitHub actions](https://github.com/features/actions) and include R CMD check,
   [_BiocCheck_](https://bioconductor.org/packages/3.16/bioc/html/BiocCheck.html)
@@ -76,15 +145,4 @@ objects. Not surprisingly, it also depends on the generic panels from iSEE.
   [_devtools_](https://cran.r-project.org/web/packages/devtools/) and
   [_roxygen2_](https://cran.r-project.org/web/packages/roxygen2/).
 - All the actions above are made reproducible by
-  [_rworkflows_](https://neurogenomics.github.io/rworkflows/)
-
-This package was developed using
-[_usethis_](https://cran.r-project.org/web/packages/usethis/).
-
-## Code of Conduct
-Please note that the iSEEtree project is released with a
-[Contributor Code of Conduct](https://bioconductor.org/about/code-of-conduct/).
-By contributing to this project, you agree to abide by its terms. Contributions
-are welcome in the form of feedback, issues and pull requests. You can find the
-contributor guidelines of the miaverse
-[here](https://github.com/microbiome/mia/blob/devel/CONTRIBUTING.md).
+  [_rworkflows_](https://neurogenomics.github.io/rworkflows/)

tests/testthat/test-AbundanceDensityPlot.R

@@ -1,5 +1,11 @@
 test_that("AbundanceDensityPlot", {
 
+  output <- new.env()
+  pObjects <- new.env()
+  rObjects <- new.env()
+  select_info <- list(single = list(feature = "---", sample = "---"),
+                      multi = list(row = "---", column = "---"))
+
   data("Tengeler2020", package = "mia")
   tse <- Tengeler2020
   panel <- AbundanceDensityPlot()
@@ -8,6 +14,9 @@ test_that("AbundanceDensityPlot", {
   expect_identical(.fullName(panel), "Abundance density plot")
   expect_identical(.panelColor(panel), "#8B5A2B")
 
+  expect_s3_class(.defineInterface(panel, tse, select_info)[[1]][[1]], "shiny.tag.list")
+  expect_length(.defineDataInterface(panel, tse, select_info), 6)
+
   expect_s3_class(.defineOutput(panel), "shiny.tag.list")
   expect_match(.generateOutput(panel, tse)[["commands"]][["fun"]],
     'p <- miaViz::plotAbundanceDensity(se, layout="jitter", add_legend=TRUE,\n',
@@ -27,16 +36,13 @@ test_that("AbundanceDensityPlot", {
 
   expect_s3_class(.create_visual_box_for_abunddens_plot(panel, tse),
                   "shiny.tag.list")
-
-  output <- new.env()
-  pObjects <- new.env()
-  rObjects <- new.env()
 
   expect_null(.renderOutput(panel, tse, output = output, pObjects = pObjects, rObjects = rObjects))
   expect_s3_class(output$AbundanceDensityPlotNA, "shiny.render.function")
   expect_s3_class(output$AbundanceDensityPlotNA_INTERNAL_PanelMultiSelectInfo, "shiny.render.function")
   expect_s3_class(output$AbundanceDensityPlotNA_INTERNAL_PanelSelectLinkInfo, "shiny.render.function")
 
+  expect_identical(.exportOutput(panel, tse), "AbundanceDensityPlotNA.pdf")
 
 })
 

tests/testthat/test-AbundancePlot.R

@@ -1,5 +1,11 @@
 test_that("AbundancePlot", {
 
+  output <- new.env()
+  pObjects <- new.env()
+  rObjects <- new.env()
+  select_info <- list(single = list(feature = "---", sample = "---"),
+                      multi = list(row = "---", column = "---"))
+
   data("Tengeler2020", package = "mia")
   tse <- Tengeler2020
   panel <- AbundancePlot()
@@ -10,6 +16,9 @@ test_that("AbundancePlot", {
   expect_identical(.fullName(panel), "Abundance plot")
   expect_identical(.panelColor(panel), "#00E5EE")
 
+  expect_s3_class(.defineInterface(panel, tse, select_info)[[1]][[1]], "shiny.tag.list")
+  expect_length(.defineDataInterface(panel, tse, select_info), 4)
+
   expect_s3_class(.defineOutput(panel), "shiny.tag.list")
   expect_match(.generateOutput(panel, tse)[["commands"]][["fun"]],
       'p <- miaViz::plotAbundance(se, rank="Kingdom", add_legend=TRUE,
@@ -28,13 +37,11 @@ test_that("AbundancePlot", {
   expect_s3_class(.create_visual_box_for_abund_plot(panel, tse),
                   "shiny.tag.list")
 
-  output <- new.env()
-  pObjects <- new.env()
-  rObjects <- new.env()
-
   expect_null(.renderOutput(panel, tse, output = output, pObjects = pObjects, rObjects = rObjects))
   expect_s3_class(output$AbundancePlotNA, "shiny.render.function")
   expect_s3_class(output$AbundancePlotNA_INTERNAL_PanelMultiSelectInfo, "shiny.render.function")
   expect_s3_class(output$AbundancePlotNA_INTERNAL_PanelSelectLinkInfo, "shiny.render.function")
 
+  expect_identical(.exportOutput(panel, tse), "AbundancePlotNA.pdf")
+
 })

tests/testthat/test-RDAPlot.R

@@ -1,5 +1,11 @@
 test_that("RDAPlot", {
 
+  output <- new.env()
+  pObjects <- new.env()
+  rObjects <- new.env()
+  select_info <- list(single = list(feature = "---", sample = "---"),
+                      multi = list(row = "---", column = "---"))
+
   data("enterotype", package = "mia")
   tse <- enterotype
   panel <- RDAPlot()
@@ -14,6 +20,9 @@ test_that("RDAPlot", {
   expect_identical(.fullName(panel), "RDA plot")
   expect_identical(.panelColor(panel), "#CD5B45")
 
+  expect_s3_class(.defineInterface(panel, tse, select_info)[[1]][[1]], "shiny.tag.list")
+  expect_length(.defineDataInterface(panel, tse, select_info), 4)
+
   expect_s3_class(.defineOutput(panel), "shiny.tag.list")
   expect_match(.generateOutput(panel, tse)[["commands"]][["fun"]],
   'p <- miaViz::plotRDA(se, dimred="RDA", colour_by="ClinicalStatus",\n    ',
@@ -35,13 +44,9 @@ test_that("RDAPlot", {
 
   expect_s3_class(.create_visual_box_for_rda(panel, tse), "shiny.tag.list")
 
-  output <- new.env()
-  pObjects <- new.env()
-  rObjects <- new.env()
-
   expect_null(.renderOutput(panel, tse, output = output, pObjects = pObjects, rObjects = rObjects))
   expect_s3_class(output$RDAPlotNA, "shiny.render.function")
   expect_s3_class(output$RDAPlotNA_INTERNAL_PanelMultiSelectInfo, "shiny.render.function")
   expect_s3_class(output$RDAPlotNA_INTERNAL_PanelSelectLinkInfo, "shiny.render.function")
-
+  
 })

tests/testthat/test-RowTreePlot.R

@@ -1,5 +1,11 @@
 test_that("RowTreePlot", {
 
+  output <- new.env()
+  pObjects <- new.env()
+  rObjects <- new.env()
+  select_info <- list(single = list(feature = "---", sample = "---"),
+                      multi = list(row = "---", column = "---"))
+
   data("Tengeler2020", package = "mia")
   tse <- Tengeler2020
   panel <- RowTreePlot()
@@ -10,6 +16,9 @@ test_that("RowTreePlot", {
   expect_identical(.fullName(panel), "Row tree plot")
   expect_identical(.panelColor(panel), "#4EEE94")
 
+  expect_s3_class(.defineInterface(panel, tse, select_info)[[1]][[1]], "shiny.tag.list")
+  expect_length(.defineDataInterface(panel, tse, select_info), 1)
+
   expect_s3_class(.defineOutput(panel), "shiny.tag.list")
   expect_match(.generateOutput(panel, tse)[["commands"]][["fun"]],
       'p <- miaViz::plotRowTree(se, layout="rectangular", add_legend=TRUE,
@@ -32,13 +41,11 @@ test_that("RowTreePlot", {
 
   expect_s3_class(.create_visual_box_for_rowtree(panel, tse), "shiny.tag.list")
 
-  output <- new.env()
-  pObjects <- new.env()
-  rObjects <- new.env()
-
   expect_null(.renderOutput(panel, tse, output = output, pObjects = pObjects, rObjects = rObjects))
   expect_s3_class(output$RowTreePlotNA, "shiny.render.function")
   expect_s3_class(output$RowTreePlotNA_INTERNAL_PanelMultiSelectInfo, "shiny.render.function")
   expect_s3_class(output$RowTreePlotNA_INTERNAL_PanelSelectLinkInfo, "shiny.render.function")
 
+  expect_identical(.exportOutput(panel, tse), "RowTreePlotNA.pdf")
+
 })

tests/testthat/test-iSEEtree.R

@@ -7,8 +7,11 @@ test_that("iSEEtree", {
               AbundancePlot(), AbundanceDensityPlot(),
               ReducedDimensionPlot(), ComplexHeatmapPlot())
 
-  expect_length(.check_panel(tse, panels, "ReducedDimensionPlot", reducedDims), 6)
+  expect_warning(
+    panels <- .check_panel(tse, panels, "ReducedDimensionPlot", reducedDims),
+    "no valid reducedDims fields for ReducedDimensionPlot"
+  )
 
-  expect_no_error(iSEE(tse))
+  expect_no_error(iSEE(tse, initial = panels))
 
 })

vignettes/iSEEtree.Rmd

@@ -30,9 +30,6 @@ knitr::opts_chunk$set(
 ```
 
 ```{r vignetteSetup, echo=FALSE, message=FALSE, warning=FALSE}
-## Track time spent on making the vignette
-startTime <- Sys.time()
-
 ## Bib setup
 library(RefManageR)
 
@@ -51,15 +48,16 @@ bib <- c(
 
 ## Install iSEEtree
 R is an open-source statistical environment which can be easily modified to
-enhance its functionality via packages. iSEEtree is an R package currently
-available on GitHub. In the future, it may be submitted to the
-[Bioconductor](https://bioconductor.org/) repository for packages. R can be
-installed on any operating system from [CRAN](https://cran.r-project.org/)
-after which you can install iSEEtree by using the following commands in your R
-session:
+enhance its functionality via packages. iSEEtree is an R package available on
+[Bioconductor](https://bioconductor.org/). R can be installed on any operating
+system from [CRAN](https://cran.r-project.org/) after which you can install
+iSEEtree by using the following commands in your R session:
 
 ```{r install, eval=FALSE}
-remotes::install_github("RiboRings/iSEEtree")
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+
+BiocManager::install("iSEEtree")
 ```
 
 ## Required knowledge
@@ -147,28 +145,13 @@ Code for creating the vignette.
 ```{r createVignette, eval=FALSE}
 ## Create the vignette
 library(rmarkdown)
-system.time(render("iSEEtree.Rmd", "BiocStyle::html_document"))
+render("iSEEtree.Rmd", "BiocStyle::html_document")
 
 ## Extract the R code
 library(knitr)
 knit("iSEEtree.Rmd", tangle = TRUE)
 ```
 
-Date the vignette was generated.
-
-```{r reproduce1, echo=FALSE}
-## Date the vignette was generated
-Sys.time()
-```
-
-Wallclock time spent generating the vignette.
-
-```{r reproduce2, echo=FALSE}
-## Processing time in seconds
-totalTime <- diff(c(startTime, Sys.time()))
-round(totalTime, digits = 3)
-```
-
 R session information.
 
 ```{r reproduce3, echo=FALSE}