From 8e38f068be8246eeb2734e16731aa122007e64e3 Mon Sep 17 00:00:00 2001
From: oliviaAB 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.
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.
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:
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:
plot_screeplot_pca(pca_runs_list)
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.
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):
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)
diff --git a/docs/overview_files/figure-html/show-features-weight-comp-1.png b/docs/overview_files/figure-html/show-features-weight-comp-1.png
index fddbdbbc4c9b26162a35f963a92577aaa1db0305..a9cfbeea22646fa109c33c76c93952b99deaca28 100644
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