multiSight

🚀 The purpose of this document is to help you become productive as quickly as possible with the multiSight package.

The goal of multiSight is to handle multi-omics data and network inference in a easy-to-use R shiny package.

You could use this tool with a graphical interface or only with script functions (see Vignette and manual for detailed examples).

Installation

You can install the released version of multiSight from Bioconductor with:

#To install this package ensure you have BiocManager installed
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

#The following initializes usage of Bioc devel
BiocManager::install("multiSight")

multiSight purpose

multiSight is an R package providing an user-friendly graphical interface to analyze and explore your omic datasets in a multi-omics manner by DESeq2 (see Biological Insights tab), machine learning methods with biosigner and multi-block statistical analysis (see Classification tab) helped by p-values pooling Stouffer’s method.

Classification models are fitted to select few subsets of features, using biosigner or sPLS-DA methods. biosigner provides one model by omic block and one list of features named biosignature. Nevertheless, sPLS-DA biosignatures are based on more features than biosigner.

Biosignatures can be used:

To forecast phenotype (e.g. for diagnostic, histological subtyping);
To design Pathway and gene ontology enrichment (sPLS-DA biosignatures only);
To build Network inference;
To find PubMed references to make assumptions easier and data-driven.

📰 App

multiSight enables you to get better biological insights for each omic dataset helping by four analytic modules which content:

📝 Data input & results;
🎯 Classification models building;
📚 Biological databases querying;
🌱 Network Inference & PubMed querying.

👉 Run the application

run_app()

📝 Home	🎯 Classification	📚 Biological Insights	🌱 Assumption

What kind of data?

All types of omic data respecting input format is supported to build classification models, biosignatures selection and network inference.

Genomics;
Transcriptomics;
Proteomics;
Metabolomics;
Lipidomics;

👉 In fact all numeric matrices.

Data inputs formats

You have to provide two types of data: numeric matrices and classes vector as csv tables for all same samples.

Omic data 1

	SIGIRR	MAOA	MANSC1
AOFJ	0	150	1004	…
A13E	34	0	0
		…

Omic data 2

	ENSG00000139618	ENSG00000226023	ENSG00000198695
AOFJ	25	42	423	…
A13E	0	154	4900
		…

… 👉 unlimited number of omic datasets.

Omic data n

	4292	5254	7432
AOFJ	25	42	423	…
A13E	0	154	4900
		…

Omic classes

	Y
AOFJ	condA
A13E	condB
	…

🎯 Classification tab

Two types of models have been implemented so far to answer different questions: biosigner & sPLS-DA (DIABLO) .

To determine small biosignatures - biosigner.
To build classification models in a multi-omics way - DIABLO.
To select relevant biological features to enrich - DIABLO.

Features selected	Performances

📚 Biological insights tab

Biological Insight tab is dedicated to give biological sense to your data.

You could process 2 analysis in 2 clicks: both DESeq2 and DIABLO features ORAs for functional enrichment.

Biological Annotation Databases

multiSight uses so far several databases to provide a large panel of enrichment analysis, automatically after few clicks:

Pathways and Gene Ontology databases are implemented, helped by clusterProfiler and reactomePA R Bioconductor packages.

Kegg;
Reactome;
wikiPathways;
Molecular Function (GO)
Cellular Component (GO)
Biological Process (GO)

Visualizations

Two types of result visualization are given:

Classical Enrichment tables for each omic and each database (e.g. Pathways id, p-value, padjust columns).
And, when more than one omic enriched: a Multi-omics table and a multi-omics enrichment map for DESeq2 and DIABLO selected features.

DESeq2 & DIABLO features	Enrichment tables	Enrichment Map

🌱 Assumption tab

👉 Some clicks (from 4 to number of PubMed queries)

Assumption tab aims to help biological hypothesis making by network inference from feature relationship values (e.g correlation, partial correlation) and by a PubMed module.

You can find both functions:

To compute network inference and to reveal feature relationships.
To get PubMed articles based on your personalized query without leaving app.

Network Inference	PubMed query

🏁 Results

You could retrieve different results computed by multiSight in Home tab by:

Automatic report with all results in HTML and .doc documents.
.RData with all results obtained by the graphical application.

Note that tables could be downloaded in a separated way in relative tabs.

MODELS: classification models you can use on future data.

DESeq2: differential expression analysis tables.

BIOSIGNATURES: DESeq2 tables thresholding and DIABLO multi-omics features selection method

Functional ENRICHMENTS: 6 databases functional enrichment for all omic datasets you provide enriched by Stouffer’s pooling p-value method giving a multi-omics enrichmentt able easily to discuss.

NETWORKS: network inference analysis with all features selected from all omic datasets according to DESeq2 tables thresholding or multi-omics feature selection (correlation, partial correlation, mutual information).

BIBLIOGRAPHY : a subset of PubMed articles relative to relations you choose in network inference tab.

Fjeanneret/multiSight