From 8e38f068be8246eeb2734e16731aa122007e64e3 Mon Sep 17 00:00:00 2001 From: oliviaAB Date: Fri, 15 Mar 2024 09:20:40 +1300 Subject: [PATCH] Finished overview chapter --- docs/overview.html | 21 +++++---- .../show-features-weight-comp-1.png | Bin 176918 -> 176003 bytes docs/sitemap.xml | 2 +- overview.qmd | 44 ++++++++++-------- 4 files changed, 36 insertions(+), 31 deletions(-) diff --git a/docs/overview.html b/docs/overview.html index 72bf60b..102a3b9 100644 --- a/docs/overview.html +++ b/docs/overview.html @@ -309,8 +309,11 @@

- +

In this chapter, we provide an overview of the capabilities of moiraine. Some code is presented for illustration, but for more details readers will be directed towards the corresponding chapter in the manual.

+
+
Loading packages
library(moiraine)
 
 ## For custom colour palettes
 library(ggplot2)
@@ -321,9 +324,7 @@ 

## For visualising sO2PLS summary library(OmicsPLS)

-
-
- +

1.1 Input data

@@ -395,7 +396,7 @@

plot_samples_upset()), or generating a density plot for each omics dataset (plot_density_data()). See Chapter 4 for more details about the different visualisations and summary functions implemented.

1.2 Data pre-processing

-

Target factories have been implemented to facilitate the application of similar tasks across the different omics datasets. For example, the transformation_datasets_factory() function generates a sequence of targets to apply one of many possible transformations (from the vsn, DESeq2, or bestNormalize packages, for example) on each omics dataset, store information about each transformation performed, and generate a new MultiDataSet object in which the omics measurements have been transformed:

+

Target factories have been implemented to facilitate the application of similar tasks across the different omics datasets. For example, the transformation_datasets_factory() function generates a sequence of targets to apply one of many possible transformations (from the vsn, DESeq2, or bestNormalize packages, for example) on each omics dataset, store information about each transformation performed, and generate a new MultiDataSet object in which the omics measurements have been transformed:

Code
@@ -412,7 +413,7 @@

Note that there is also the option for users to apply their own custom transformations to the datasets (see Chapter 5).

-

Similarly, the pca_complete_data_factory generates a list of targets to run a PCA on each omics dataset via the pcaMethods package, and if necessary imputes missing values through NIPALS-PCA. The PCA results can be easily visualised for all or specific omics datasets:

+

Similarly, the pca_complete_data_factory generates a list of targets to run a PCA on each omics dataset via the pcaMethods package, and if necessary imputes missing values through NIPALS-PCA. The PCA results can be easily visualised for all or specific omics datasets:

Code
plot_screeplot_pca(pca_runs_list)
@@ -438,7 +439,7 @@

Chapter 6.

1.3 Data pre-filtering

-

The created MultiDataSet object can be filtered, both in terms of samples and features, by passing a list of sample or feature IDs to retain, or by using logical tests on samples or features metadata. In addition, we implement target factories to retain only the most variable features in each omics dataset –unsupervised filtering–, or to retain the features most associated with an outcome of interest, via sPLS-DA from mixOmics –supervised filtering– (see Chapter 7). This pre-filtering step is essential to reduce the size of the datasets prior to multi-omics integration.

+

The created MultiDataSet object can be filtered, both in terms of samples and features, by passing a list of sample or feature IDs to retain, or by using logical tests on samples or features metadata. In addition, we implement target factories to retain only the most variable features in each omics dataset –unsupervised filtering–, or to retain the features most associated with an outcome of interest, via sPLS-DA from mixOmics –supervised filtering– (see Chapter 7). This pre-filtering step is essential to reduce the size of the datasets prior to multi-omics integration.

Code
@@ -475,7 +476,7 @@

1.4 Multi-omics data integration

-

Currently, moiraine provides functions and target factories to facilitate the use of five integration methods: sPLS and DIABLO from the mixOmics package, sO2PLS from OmicsPLS, as well as MOFA and MEFISTO from MOFA2.

+

Currently, moiraine provides functions and target factories to facilitate the use of five integration methods: sPLS and DIABLO from the mixOmics package, sO2PLS from OmicsPLS, as well as MOFA and MEFISTO from MOFA2.

This includes functions that transform a MultiDataSet object into the required input format for each integration method; for example for sPLS (only top of the matrices shown):

Code
get_input_spls(
@@ -514,7 +515,7 @@ 

list(
-  tar_target(
+  tar_target(
     diablo_input,                            # DIABLO input object
     get_input_mixomics_supervised(
       mo_presel_supervised,                  # MultiDataSet object (prefiltered)
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2024-03-14T20:08:18.886Z https://solid-lamp-kq546rq.pages.github.io/example_dataset.html diff --git a/overview.qmd b/overview.qmd index 0766663..f8a2378 100644 --- a/overview.qmd +++ b/overview.qmd @@ -15,10 +15,29 @@ library(purrr) library(OmicsPLS) ``` +```{r loading-data} +#| echo: false + +mo_set <- tar_read(mo_set_de) +tar_load(interesting_features) +tar_load(pca_runs_list) +tar_load(mo_presel_supervised) +tar_load(so2pls_final_run) +tar_load(diablo_final_run) +mofa_output <- tar_read(mofa_output) |> + moiraine:::.filter_output_dimensions(paste("Factor", 1:4)) +tar_load(diablo_output) +tar_load(output_list) +``` + +In this chapter, we provide an overview of the capabilities of `moiraine`. Some code is presented for illustration, but for more details readers will be directed towards the corresponding chapter in the manual. + + ```{r setup-visible} +#| code-fold: true +#| code-summary: "Loading packages" #| eval: false -library(targets) library(moiraine) ## For custom colour palettes @@ -32,21 +51,6 @@ library(purrr) library(OmicsPLS) ``` -```{r loading-data} -#| echo: false - -mo_set <- tar_read(mo_set_de) -tar_load(interesting_features) -tar_load(pca_runs_list) -tar_load(mo_presel_supervised) -tar_load(so2pls_final_run) -tar_load(diablo_final_run) -mofa_output <- tar_read(mofa_output) |> - moiraine:::.filter_output_dimensions(paste("Factor", 1:4)) -tar_load(diablo_output) -tar_load(output_list) -``` - ## Input data @@ -109,7 +113,7 @@ Similarly, a number of functions allow to quickly summarise different aspects of ## Data pre-processing -Target factories have been implemented to facilitate the application of similar tasks across the different omics datasets. For example, the `transformation_datasets_factory()` function generates a sequence of targets to apply one of many possible transformations (from the `vsn`, `DESeq2`, or `bestNormalize` packages, for example) on each omics dataset, store information about each transformation performed, and generate a new `MultiDataSet` object in which the omics measurements have been transformed: +Target factories have been implemented to facilitate the application of similar tasks across the different omics datasets. For example, the `transformation_datasets_factory()` function generates a sequence of targets to apply one of many possible transformations (from the [`vsn`](https://bioconductor.org/packages/release/bioc/html/vsn.html), [`DESeq2`](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), or [`bestNormalize`](https://cran.r-project.org/web/packages/bestNormalize/index.html) packages, for example) on each omics dataset, store information about each transformation performed, and generate a new `MultiDataSet` object in which the omics measurements have been transformed:
@@ -136,7 +140,7 @@ transformation_datasets_factory( Note that there is also the option for users to apply their own custom transformations to the datasets (see @sec-modifying-multidataset). -Similarly, the `pca_complete_data_factory` generates a list of targets to run a PCA on each omics dataset via the `pcaMethods` package, and if necessary imputes missing values through NIPALS-PCA. The PCA results can be easily visualised for all or specific omics datasets: +Similarly, the `pca_complete_data_factory` generates a list of targets to run a PCA on each omics dataset via the [`pcaMethods` package](https://bioconductor.org/packages/release/bioc/html/pcaMethods.html), and if necessary imputes missing values through NIPALS-PCA. The PCA results can be easily visualised for all or specific omics datasets: ```{r pca-screeplot} #| code-fold: true @@ -169,7 +173,7 @@ More information about data pre-processing can be found in @sec-preprocessing. ## Data pre-filtering -The created `MultiDataSet` object can be filtered, both in terms of samples and features, by passing a list of sample or feature IDs to retain, or by using logical tests on samples or features metadata. In addition, we implement target factories to retain only the most variable features in each omics dataset --unsupervised filtering--, or to retain the features most associated with an outcome of interest, via sPLS-DA from `mixOmics` --supervised filtering-- (see @sec-prefiltering). This pre-filtering step is essential to reduce the size of the datasets prior to multi-omics integration. +The created `MultiDataSet` object can be filtered, both in terms of samples and features, by passing a list of sample or feature IDs to retain, or by using logical tests on samples or features metadata. In addition, we implement target factories to retain only the most variable features in each omics dataset --unsupervised filtering--, or to retain the features most associated with an outcome of interest, via sPLS-DA from [`mixOmics`](http://mixomics.org/) --supervised filtering-- (see @sec-prefiltering). This pre-filtering step is essential to reduce the size of the datasets prior to multi-omics integration.
@@ -201,7 +205,7 @@ mo_presel_supervised ## Multi-omics data integration -Currently, `moiraine` provides functions and target factories to facilitate the use of five integration methods: sPLS and DIABLO from the `mixOmics` package, sO2PLS from `OmicsPLS`, as well as `MOFA` and `MEFISTO` from `MOFA2`. +Currently, `moiraine` provides functions and target factories to facilitate the use of five integration methods: sPLS and DIABLO from the `mixOmics` package, sO2PLS from [`OmicsPLS`](https://cran.r-project.org/web/packages/OmicsPLS/index.html), as well as `MOFA` and `MEFISTO` from [`MOFA2`](https://biofam.github.io/MOFA2/). This includes functions that transform a `MultiDataSet` object into the required input format for each integration method; for example for sPLS (only top of the matrices shown):