diff --git a/_quarto.yml b/_quarto.yml
index 65ce731..019933e 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -3,7 +3,7 @@ project:
output-dir: docs
book:
- title: "The moiraine R package user manual"
+ title: "The `moiraine` R package user manual"
author: "Olivia Angelin-Bonnet"
search: true
repo-url: https://github.com/PlantandFoodResearch/moiraine
diff --git a/data_import.qmd b/data_import.qmd
index 3062e0e..7004184 100644
--- a/data_import.qmd
+++ b/data_import.qmd
@@ -158,6 +158,49 @@ tar_read(data_metabo) |> dim()
With this factory function, it is not possible to pass arguments to `read_csv()`. If you want to control how the files are read, please use the `import_dataset_csv()` function directly instead, as shown in @sec-import-dataset-manual.
+
+
+Converting targets factory to R script
+
+There is no simple way to convert this target factory to regular R script using loops, so we can instead write the code separately for each omics dataset.
+
+```{r import-dataset-csv-factory-to-script}
+#| eval: false
+
+dataset_file_geno <- system.file(
+ "extdata/genomics_dataset.csv",
+ package = "moiraine"
+)
+data_geno <- import_dataset_csv(
+ dataset_file_geno,
+ col_id = "marker",
+ features_as_rows = TRUE
+)
+
+dataset_file_transcripto <- system.file(
+ "extdata/transcriptomics_dataset.csv",
+ package = "moiraine"
+)
+data_transcripto <- import_dataset_csv(
+ dataset_file_transcripto,
+ col_id = "gene_id",
+ features_as_rows = TRUE
+)
+
+dataset_file_metabo <- system.file(
+ "extdata/metabolomics_dataset.csv",
+ package = "moiraine"
+)
+data_metabo <- import_dataset_csv(
+ dataset_file_metabo,
+ col_id = "sample_id",
+ features_as_rows = FALSE
+)
+```
+
+
+
+
## Importing the features metadata
Similarly to how we imported the datasets, there are two ways of importing features metadata: either manually, or using a target factory function. The two options are illustrated below.
@@ -223,6 +266,25 @@ tar_read(fmetadata_metabo) |> head()
Again, the targets factory function does not allow to pass arguments to `read_csv()` (if you need them, please use `import_fmetadata_csv()` directly as we have done in @sec-import-fmeta-manual).
+
+
+Converting targets factory to R script
+
+```{r import-fmetadata-csv-factory-to-script}
+#| eval: false
+
+fmetadata_file_metabo <- system.file(
+ "extdata/metabolomics_features_info.csv",
+ package = "moiraine"
+)
+fmetadata_metabo <- import_fmetadata_csv(
+ fmetadata_file_metabo,
+ col_id = "feature_id"
+)
+```
+
+
+
### Importing features metadata from a GTF/GFF file {#sec-import-fmeta-gff}
The `moiraine` package can also extract features metadata from a genome annotation file (`.gtf` or `.gff`). We'll demonstrate that for the transcriptomics dataset, for which information about the position and name of the transcripts can be found in the genome annotation used to map the reads. The function is called `import_fmetadata_gff()` (it is also the function you would use to read in information from a `.gtf` file). The type of information to extract from the annotation file is specified through the `feature_type` argument, which can be either `'genes'` or `'transcripts'`. In addition, if the function does not extract certain fields from the annotation file, these can be explicitly called using the `add_fields` parameter.
@@ -271,6 +333,26 @@ As with `import_fmetadata`, the function returns a data-frame of features inform
tar_read(fmetadata_transcripto) |> head()
```
+
+
+Converting targets factory to R script
+
+```{r import-fmetadata-gff-factory-to-script}
+#| eval: false
+
+fmetadata_file_transcripto <- system.file(
+ "extdata/bos_taurus_gene_model.gff3",
+ package = "moiraine"
+)
+fmetadata_transcripto <- import_fmetadata_gff(
+ fmetadata_file_transcripto,
+ feature_type = "genes",
+ add_fields = c("Name", "description")
+)
+```
+
+
+
## Importing the samples metadata
As for importing datasets or features metadata, the `import_smetadata_csv()` function reads in a csv file that contains information about the samples measured. Similarly to `import_fmetadata_csv()`, this function assumes that the csv file contains samples as rows. In this example, we have one samples information file for all of our omics datasets, but it is possible to have one separate samples metadata csv file for each omics dataset (if there are some omics-specific information such as batch, technology specifications, etc).
@@ -319,6 +401,23 @@ Note that in the samples metadata data-frame, the sample IDs are present both as
As for the other import functions, `import_smetadata_csv()` accepts arguments that will be passed to `read_csv()` in order to specify how the file should be read. The targets factory version does not have this option.
+
+
+
+Converting targets factory to R script
+
+```{r import-smetadata-csv-factory-to-script}
+#| eval: false
+
+smetadata_file_all <- system.file("extdata/samples_info.csv", package = "moiraine")
+smetadata_all <- import_smetadata_csv(
+ smetadata_file_all,
+ col_id = "animal_id"
+)
+```
+
+
+
## Creating the omics sets
Once each dataset and associated features and samples metadata have been imported, we need to combine them into omics sets. In practice, this means that for each omics dataset, we will create an R object that stores the actual dataset alongside its relevant metadata. `moiraine` relies on the `Biobase` containers derived from `Biobase::eSet` to store the different omics datasets; for example, `Biobase::ExpressionSet` objects are used to store transcriptomics measurements. Currently, `moiraine` support four types of omics containers:
@@ -441,6 +540,40 @@ tar_read(set_transcripto)
tar_read(set_metabo)
```
+
+
+Converting targets factory to R script
+
+Again there not an easy way to use loops to convert this targets factory, so instead we'll write the code for each omics dataset.
+
+```{r create-omics-set-factory-to-script}
+#| eval: false
+
+set_geno <- create_omics_set(
+ data_geno,
+ omics_type = "genomics",
+ features_metadata = fmetadata_geno,
+ samples_metadata = smetadata_all
+)
+
+set_transcripto <- create_omics_set(
+ data_transcripto,
+ omics_type = "transcriptomics",
+ features_metadata = fmetadata_transcripto,
+ samples_metadata = smetadata_all
+)
+
+set_metabo <- create_omics_set(
+ data_metabo,
+ omics_type = "metabolomics",
+ features_metadata = fmetadata_metabo,
+ samples_metadata = smetadata_all
+)
+```
+
+
+
+
## Creating the multi-omics set
Finally, we can combine the different omics sets into one multi-omics set object. `moiraine` makes use of the [`MultiDataSet` package](https://bioconductor.org/packages/release/bioc/html/MultiDataSet.html) for that. `MultiDataSet` (@hernandez-ferrer2017) implements a multi-omics data container that collects, in one R object, several omics datasets alongside their associated features and samples metadata. One of the main advantages of using a `MultiDataSet` container is that we can pass all of the information associated with a set of related omics datasets with only one R object. In addition, the `MultiDataSet` package implements a number of very useful functions. For example, it is possible to assess the samples that are common to several omics sets. This is particularly useful for data integration, as the `moiraine` package can automatically discard samples missing from one or more datasets prior to the integration step if needed. Note that sample matching between the different omics datasets is based on sample IDs, so they must be consistent between the different datasets.
diff --git a/diablo.qmd b/diablo.qmd
index c749f39..3d9568c 100644
--- a/diablo.qmd
+++ b/diablo.qmd
@@ -162,6 +162,29 @@ tar_read(diablo_pls_correlation_matrix)
tar_read(diablo_design_matrix)
```
+
+
+Converting targets factory to R script
+
+```{r diablo-pairwise-pls-factory-to-script}
+#| eval: false
+
+diablo_pairs_datasets <- utils::combn(
+ setdiff(names(diablo_input), "Y"),
+ 2,
+ simplify = FALSE
+)
+
+diablo_pls_runs_list <- diablo_pairs_datasets |>
+ map(\(x) run_pairwise_pls(diablo_input, x))
+
+diablo_pls_correlation_matrix <- diablo_get_pairwise_pls_corr(diablo_pls_runs_list)
+
+diablo_design_matrix <- diablo_generate_design_matrix(diablo_pls_correlation_matrix)
+```
+
+
+
## Choosing the number of latent components
One important parameter that must be set when performing a DIABLO analysis is the number of latent components to construct for each dataset. The optimal number of components can be estimated by cross-validation, implemented in the `mixOmics::perf()` function. This function assesses the classification performance (i.e. how well the different outcome groups are separated) achieved by DIABLO for different numbers of latent components.
diff --git a/docs/comparison.html b/docs/comparison.html
index ba7cb2a..7b94adc 100644
--- a/docs/comparison.html
+++ b/docs/comparison.html
@@ -132,7 +132,7 @@ 13&nb