In Fjeanneret/multiSight: Multi-omics Classification, Functional Enrichment and Network Inference analysis

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multiSight

:rocket: 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.

:newspaper: App

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

• :memo: Data input & results;
• :dart: Classification models building;
• :books: Biological databases querying;
• :seedling: Network Inference & PubMed querying.

:point_right: Run the application

run_app()

| :memo: Home | :dart: Classification | :books: Biological Insights | :seedling: 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;

:point_right: 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 | | | | | ... | | |

... :point_right: 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 | | | ... |

:dart: 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 | |-----------------------------------------------|-----------------------------------------------| | | |

:books: 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 | |---------------------------------------------|----------------------------------------------|-----------------------------------------------| | | | |

:seedling: Assumption tab

:point_right: 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 | |-----------------------------------------|------------------------------------------| | | |

:checkered_flag: 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 documentation built on April 6, 2022, 7:59 a.m.