From 1ce453d950043e255c8fd312c3fa8ad7ddb65834 Mon Sep 17 00:00:00 2001 From: Tuomas Borman <60338854+TuomasBorman@users.noreply.github.com> Date: Sat, 5 Oct 2024 20:01:35 +0300 Subject: [PATCH 1/2] Fix reference (#624) --- inst/pages/clustering.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inst/pages/clustering.qmd b/inst/pages/clustering.qmd index b7b933b6..e60719ae 100644 --- a/inst/pages/clustering.qmd +++ b/inst/pages/clustering.qmd @@ -136,7 +136,7 @@ dend |> set("labels_cex", 0.8) |> plot() We can also visualize the clusters by projecting the data onto two-dimensional surface that captures the most variability in the data. In this example, we use -multi-dimensional scaling (MDS) (see [@sec-unsupervised-ordinantion]). +multi-dimensional scaling (MDS) (see [@sec-unsupervised-ordination]). ```{r hclust3} library(scater) From d50003409f40a04166606da396651fe71c6d92eb Mon Sep 17 00:00:00 2001 From: Tuomas Borman <60338854+TuomasBorman@users.noreply.github.com> Date: Sun, 6 Oct 2024 22:52:14 +0300 Subject: [PATCH 2/2] Fixes to container chapter (#625) --- inst/pages/containers.qmd | 83 ++++++++++++++++++++++++++++++---- inst/pages/figures/mae.png | Bin 0 -> 14159 bytes inst/pages/figures/treese.png | Bin 0 -> 148968 bytes 3 files changed, 75 insertions(+), 8 deletions(-) create mode 100644 inst/pages/figures/mae.png create mode 100644 inst/pages/figures/treese.png diff --git a/inst/pages/containers.qmd b/inst/pages/containers.qmd index edc7fe97..e064c6f1 100644 --- a/inst/pages/containers.qmd +++ b/inst/pages/containers.qmd @@ -20,6 +20,50 @@ on how to represent different varieties of multi-table data within the The options and recommendations are summarized in [@tbl-options]. +## Structure of `TreeSE` + +`TreeSE` contains several distinct slots, each holding a specific type of data. +The `assays` slot is the core of `TreeSE`, storing abundance tables that +contain the counts or concentrations of features in each sample. +Features can be taxa, metabolites, antimicrobial resistance genes, or other +measured entities, and are represented as rows. The columns correspond to +unique samples. + +Building upon the `assays`, `TreeSE` accommodates various data types for both +features and samples. In `rowData`, the rows correspond to the same features +(rows) as in the abundance tables, while the columns represent variables such as +taxonomy ranks. Similarly, in `colData`, each row matches the samples (columns) +from the abundance tables, with the columns of `colData` containing metadata +like disease status or patient ID and time point if the dataset includes time +series. + +The slots in `TreeSE` are outlined below: + +- `assays`: Stores a list of abundance tables. Each table has consistent rows and columns, where rows represent taxa and columns represent samples. +- `rowData`: Contains metadata about the rows (taxa). For example, this slot can include a taxonomy table. +- `colData`: Holds metadata about the columns (samples), such as patient information or the time points when samples were collected. +- `rowTree`: Stores a hierarchical tree for the rows, such as a phylogenetic tree representing the relationships between taxa. +- `colTree`: Includes a hierarchical tree for the columns, which can represent relationships between samples, for example, indicating whether patients are relatives and the structure of those relationships. +- `rowLinks`: Contains information about the linkages between rows and the nodes in the `rowTree`. +- `colLinks`: Contains information about the linkages between columns and the nodes in the `colTree`. +- `referenceSeq`: Holds reference sequences, i.e., the sequences that correspond to each taxon identified in the rows. +- `metadata`: Contains metadata about the experiment, such as the date it was conducted and the researchers involved. + +These slots are illustrated in the figure below: + +![The structure of TreeSummarizedExperiment (TreeSE) object [@Huang2021].](figures/treese.png){width="80%"} + +Additionally, TreeSE includes: + +- `reducedDim`: Contains reduced dimensionality representations of the samples, such as Principal Component Analysis (PCA) results (see [@sec-community-similarity]. +- `altExp`: Stores alternative experiments, which are `TreeSE` objects sharing the same samples but with different feature sets. + +Among these, `assays`, `rowData`, `colData`, and `metadata` are shared with the +`SummarizedExperiment` (`SE`) data container. `reducedDim` and `altExp` come +from inheriting the `SingleCellExperiment` (`SCE`) class. The `rowTree`, +`colTree`, `rowLinks`, `colLinks`, and `referenceSeq` slots are unique to +`TreeSE`. + ## Rows and columns {#sec-rows-and-cols} Let us load example data and store it in variable `tse`. @@ -152,7 +196,11 @@ A tree can be accessed via `rowTree` as `phylo` object. rowTree(tse) ``` -The links to the individual features are available through `rowLinks`. +Each row in `TreeSE` is linked to a specific node in a tree. This relationship +is stored in the `rowLinks` slot, which has the same rows as `TreeSE`. +The `rowLinks` slot contains information about which tree node corresponds to +each row and whether the node is a leaf (tip) or an internal node, among other +details. ```{r rowlinks} rowLinks(tse) @@ -202,10 +250,10 @@ original data. ```{r altexp_agglomerate2} # Add the new data object to the original data object as an alternative -# experiment with the name "Phylum" +# experiment with the specified name altExp(tse, "subsetted") <- tse_sub -# Check the alternative experiment names available in the data +# Retrieve and display the names of alternative experiments available altExpNames(tse) ``` @@ -237,6 +285,26 @@ samples are defined through a `sampleMap`. Each element on the `matrix`-like objects, including `SE` objects, and the number of samples can differ between the elements. +In a `MAE`, the "subjects" represent patients. The `MAE` has four main slots, +with `experiments` being the core. This slot holds a list of experiments, each +in (`Tree`)`SE` format. To handle complex mappings between samples +(observations) across different experiments, the `sampleMap` slot stores +information about how each +sample in the experiments is linked to a patient. Metadata for each patient is +stored in the `colData` slot. Unlike the `colData` in `TreeSE`, this `colData` +is meant to store only metadata that remains constant throughout the trial. + +- `experiments`: Contains experiments, such as different omics data, in TreeSE format. +- `sampleMap`: Holds linkages between patients (subjects) and samples in the experiments (observations). +- `colData`: Includes patient metadata that remains unchanged throughout the trial. + +These slots are illustrated in the figure below: + +![The structure of MultiAssayExperiment (MAE) object [@Ramos2017].](figures/mae.png){width="60%"} + +Additionally, the object includes a `metadata` slot that contains information +about the dataset, such as the trial period and the creator of the `MAE` object. + The `MAE` object can handle more complex relationships between experiments. It manages the linkages between samples and experiments, ensuring that the data remains consistent and well-organized. @@ -254,8 +322,6 @@ important bookkeeper, maintaining the information about which samples are associated with which experiments. This ensures that data linkages are correctly managed and preserved across different types of experiments. -In fact, we can have - ```{r} #| label: show_mae2 @@ -274,9 +340,10 @@ mae ::: {.callout-note} ## Note -If you have multiple experiments containing multiple measures from same patients, -you can utilize the `MultiAssayExperiment` object to keep track of which -samples belong to which patient. +If you have multiple experiments (e.g., different omics data types like +metagenomics, transcriptomics, proteomics, or metabolomics), the +`MultiAssayExperiment` object allows you to organize and integrate these +datasets, even if the samples across experiments don’t have a perfect 1:1 match. ::: diff --git a/inst/pages/figures/mae.png b/inst/pages/figures/mae.png new file mode 100644 index 0000000000000000000000000000000000000000..30bb237f0b6e5609013bbc23149e5f7228aaf739 GIT binary patch literal 14159 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