1 Introduction

This page introduces users to the complete catalogue of panels provided by iSEEtree. Each panel is presented individually and visualised as it appears in the app. This catalogue is divided into four sections:

  • compositional analysis: abundance plot, abundance density plot, prevalence plot and complex heatmap plot
  • ordination analysis: RDA plot, Scree plot, loading plot and reduced dimension plot
  • structural analysis: row/column tree plots and row/column graph plots
  • other: row/column tile plots, mediation plot and row/column data plots

2 Compositional Analysis

Panel name Panel class Information
Abundance plot AbundancePlot Feature abundance by sample
Abundance density plot AbundanceDensityPlot Feature distribution across samples
Prevalence plot PrevalencePlot Feature prevalence across samples
Feature assay plot FeatureAssayPlot Feature counts by column variable
Complex heatmap plot ComplexHeatmapPlot Counts by features and samples

2.1 Abundance plot

The Abundance plot illustrates the feature composition of each sample with a barplot of the relative or absolute feature abundace. This panel is based on the miaViz function plotAbundance.

Supported operations:

  • Selecting taxonomic rank of the composition
  • Choosing either absolute or relative abundance
  • Ordering samples by feature or sample metadata
  • Customising aesthetics

2.2 Abundance density plot

The Abundance density plot provides an alternative way to visualise abundance. In this panel, each row represents the feature distribution across the samples. It is based on the miaViz function plotAbundanceDensity.

Supported operations:

  • selecting layout (jitter, density or dot plot)
  • specifying the number of top features to show
  • customising aesthetics

2.3 Prevalence plot

The Prevalence plot provides an way to visualise feature prevalence across samples. In this panel, each line represents the feature abundance across the samples and prevalence is encoded by the colour. It is based on the miaViz function plotPrevalence.

Supported operations:

  • adjusting prevalence and detection thresholds
  • selecting the taxonomic rank to show

2.4 Feature assay plot

The Feature assay plot is inherited from iSEE. It is based on the scater function plotRowData and can be used to visualise the counts of a specific feature across samples grouped by a column variable.

See its image in the iSEE panel catalogue: FeatureAssayPlot

2.5 Complex heatmap plot

The Reduced dimension plot is inherited from iSEE. It is based on the ComplexHeatmap function Heatmap, which shows an entire assay as a heatmap where rows and columns represent features and samples, respectively. Hierarchical clustering as well as grouping by a variable can be performed across both dimensions.

See its image in the iSEE panel catalogue: ComplexHeatmapPlot

3 Ordination Analysis

Panel name Panel class Information
RDA plot RDAPlot Supervised ordination
Scree plot ScreePlot Explained variance by component
Loading plot LoadingPlot Feature loadings by component
Reduced dimension plot ReducedDimensionPlot Any ordination result

3.1 RDA plot

The RDA plot visualises results for a distance-based Redundance Analysis (dbRDA) performed on a TreeSE object with the mia function runRDA. It is based on the miaViz function plotRDA.

Supported operations:

  • selecting reduced dimension
  • adjusting statistical parameters
  • customising aesthetics

3.2 Scree plot

The Scree plot shows the proportion of variance explained by each component of a dimensionality reduction analysis by means of a line plot or barplot. It is based on the miaViz function plotScree.

Supported operations:

  • selecting reduced dimension
  • changing number of components
  • showing individual or cumulative variance
  • adding components labels and names

3.3 Loading plot

The Loading plot visualises the contributions of each feature to the components of a reduced dimension of choice. It is based on the miaViz function plotLoadings.

Supported operations:

  • selecting layout (barplot, heatmap or lollipop)
  • changing number of components
  • adding feature tree

3.4 Reduced dimension plot

The Reduced dimension plot is inherited from iSEE. It is based on the scater function plotReducedDim and can be used to visualise the results of an ordination analysis with both supervised and unsupervised methods as dot plot with reduced dimensions as coordinate axes.

See its image in the iSEE panel catalogue: ReducedDimensionPlot

4 Structural Analysis

Panel name Panel class Information
Row tree plot RowTreePlot Hierarchical structure of features
Column tree plot ColumnTreePlot Hierarchical structure of samples
Row graph plot RowGraphPlot Network structure of features
Column graph plot ColumnGraphPlot Network structure of samples

4.1 Row/Column tree plots

Row and column tree plots belong to the TreePlot family. They can be used to visualise the hierarchical organisation of the features or samples by means of a tree. They are based on the miaViz functions plotRowTree and plotColTree.

Supported operations:

  • collapsing and expanding clades
  • rotating and opening trees
  • ordering trees
  • labeling nodes and tips
  • selecting tree layout
  • customising aesthetics

4.2 Row/Column graph plots

Row and column graph plots belong to the GraphPlot family. They can be used to visualise the network organisation of the features or samples by means of a graph. They are based on the miaViz functions plotRowGraph and plotColGraph.

Supported operations:

  • selecting graph and assay type
  • labelling nodes
  • selecting graph layout
  • selecting edge type
  • customising aesthetics

5 Other panels

Panel name Panel class Information
Row tile plot RowTilePlot Variable distribution across feature groups
Column tile plot ColumnTilePlot Variable distribution across sample groups
Mediation plot MediationPlot Results of mediation analysis
Row data table RowDataTable Table of feature metadata
Column data table ColumnDataTable Table of sample metadata
Row data plot RowDataPlot Feature variable distribution
Column data plot ColumnDataPlot Sample variable distribution

5.1 Row/Column tile plots

Coming soon!

5.2 Mediation plot

Coming soon!

5.3 Row/Column data tables

The Row and column data tables are inherited from iSEE. They are rendered as a tidy table of feeature or sample variables, respectively. From those, it is possible to select a subset of the observations and transmit it to one or many other panels.

See their images in the iSEE panel catalogue: RowDataPlot and ColumnDataPlot

5.4 Row/Column data plots

The Row and column data plots are inherited from iSEE. They are based on the scater functions plotRowData and plotColData and can be used to visualise feature or sample metadata as scatter plots when the x variable is continuous or boxplots when the x variable is discrete.

See their images in the iSEE panel catalogue: RowDataTable and ColumnDataTable

6 Reproducibility

R session information:

#> R Under development (unstable) (2025-03-13 r87965)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
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#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
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#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
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#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
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#> other attached packages:
#>  [1] scater_1.35.4                   ggplot2_3.5.1                   scuttle_1.17.0                 
#>  [4] mia_1.15.32                     TreeSummarizedExperiment_2.15.0 Biostrings_2.75.4              
#>  [7] XVector_0.47.2                  MultiAssayExperiment_1.33.9     iSEEtree_1.1.4                 
#> [10] iSEE_2.19.3                     SingleCellExperiment_1.29.2     SummarizedExperiment_1.37.0    
#> [13] Biobase_2.67.0                  GenomicRanges_1.59.1            GenomeInfoDb_1.43.4            
#> [16] IRanges_2.41.3                  S4Vectors_0.45.4                BiocGenerics_0.53.6            
#> [19] generics_0.1.3                  MatrixGenerics_1.19.1           matrixStats_1.5.0              
#> [22] BiocStyle_2.35.0               
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#>   [1] splines_4.6.0               later_1.4.1                 ggplotify_0.1.2             cellranger_1.1.0           
#>   [5] tibble_3.2.1                polyclip_1.10-7             DirichletMultinomial_1.49.0 lifecycle_1.0.4            
#>   [9] doParallel_1.0.17           miaViz_1.15.12              lattice_0.22-6              MASS_7.3-65                
#>  [13] SnowballC_0.7.1             magrittr_2.0.3              sass_0.4.9                  rmarkdown_2.29             
#>  [17] jquerylib_0.1.4             yaml_2.3.10                 httpuv_1.6.15               DBI_1.2.3                  
#>  [21] RColorBrewer_1.1-3          multcomp_1.4-28             abind_1.4-8                 purrr_1.0.4                
#>  [25] fillpattern_1.0.2           ggraph_2.2.1                yulab.utils_0.2.0           TH.data_1.1-3              
#>  [29] tweenr_2.0.3                sandwich_3.1-1              circlize_0.4.16             GenomeInfoDbData_1.2.14    
#>  [33] ggrepel_0.9.6               tokenizers_0.3.0            irlba_2.3.5.1               tidytree_0.4.6             
#>  [37] vegan_2.6-10                rbiom_2.1.2                 parallelly_1.42.0           permute_0.9-7              
#>  [41] DelayedMatrixStats_1.29.1   codetools_0.2-20            DelayedArray_0.33.6         ggforce_0.4.2              
#>  [45] DT_0.33                     ggtext_0.1.2                xml2_1.3.8                  tidyselect_1.2.1           
#>  [49] shape_1.4.6.1               aplot_0.2.5                 UCSC.utils_1.3.1            farver_2.1.2               
#>  [53] ScaledMatrix_1.15.0         viridis_0.6.5               shinyWidgets_0.9.0          jsonlite_1.9.1             
#>  [57] GetoptLong_1.0.5            BiocNeighbors_2.1.3         tidygraph_1.3.1             decontam_1.27.0            
#>  [61] survival_3.8-3              iterators_1.0.14            emmeans_1.10.7              systemfonts_1.2.1          
#>  [65] foreach_1.5.2               tools_4.6.0                 ggnewscale_0.5.1            ragg_1.3.3                 
#>  [69] treeio_1.31.0               Rcpp_1.0.14                 glue_1.8.0                  gridExtra_2.3              
#>  [73] SparseArray_1.7.7           BiocBaseUtils_1.9.0         xfun_0.51                   mgcv_1.9-1                 
#>  [77] dplyr_1.1.4                 withr_3.0.2                 shinydashboard_0.7.2        BiocManager_1.30.25        
#>  [81] fastmap_1.2.0               rhdf5filters_1.19.2         bluster_1.17.0              shinyjs_2.1.0              
#>  [85] digest_0.6.37               rsvd_1.0.5                  R6_2.6.1                    mime_0.13                  
#>  [89] gridGraphics_0.5-1          estimability_1.5.1          textshaping_1.0.0           colorspace_2.1-1           
#>  [93] listviewer_4.0.0            tidyr_1.3.1                 DECIPHER_3.3.4              graphlayouts_1.2.2         
#>  [97] httr_1.4.7                  htmlwidgets_1.6.4           S4Arrays_1.7.3              pkgconfig_2.0.3            
#> [101] gtable_0.3.6                ComplexHeatmap_2.23.0       janeaustenr_1.0.0           htmltools_0.5.8.1          
#> [105] bookdown_0.42               rintrojs_0.3.4              clue_0.3-66                 scales_1.3.0               
#> [109] png_0.1-8                   ggfun_0.1.8                 knitr_1.50                  tzdb_0.5.0                 
#> [113] reshape2_1.4.4              rjson_0.2.23                coda_0.19-4.1               nlme_3.1-167               
#> [117] shinyAce_0.4.4              rhdf5_2.51.2                cachem_1.1.0                zoo_1.8-13                 
#> [121] GlobalOptions_0.1.2         stringr_1.5.1               parallel_4.6.0              miniUI_0.1.1.1             
#> [125] vipor_0.4.7                 pillar_1.10.1               grid_4.6.0                  vctrs_0.6.5                
#> [129] slam_0.1-55                 promises_1.3.2              BiocSingular_1.23.0         beachmat_2.23.7            
#> [133] xtable_1.8-4                cluster_2.1.8.1             beeswarm_0.4.0              evaluate_1.0.3             
#> [137] readr_2.1.5                 mvtnorm_1.3-3               cli_3.6.4                   compiler_4.6.0             
#> [141] rlang_1.1.5                 crayon_1.5.3                tidytext_0.4.2              plyr_1.8.9                 
#> [145] fs_1.6.5                    ggbeeswarm_0.7.2            stringi_1.8.4               viridisLite_0.4.2          
#> [149] BiocParallel_1.41.2         munsell_0.5.1               lazyeval_0.2.2              colourpicker_1.3.0         
#> [153] Matrix_1.7-3                hms_1.1.3                   patchwork_1.3.0             sparseMatrixStats_1.19.0   
#> [157] Rhdf5lib_1.29.1             shiny_1.10.0                gridtext_0.1.5              memoise_2.0.1              
#> [161] igraph_2.1.4                bslib_0.9.0                 ggtree_3.15.0               readxl_1.4.5               
#> [165] ape_5.8-1

7 References