:rocket: The purpose of this document is to help you become productive as quickly as possible with the multiSight package.
You could use this tool with a graphical interface or only with script functions (see Vignette and manual for detailed examples).
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 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:
multiSight enables you to get better biological insights for each omic dataset helping by four analytic modules which content:
:point_right: Run the application
run_app()
| :memo: Home | :dart: Classification | :books: Biological Insights | :seedling: Assumption | | ------------------------------------ | --------------------------------------------- | -------------------------------------------- | ---------------------------------------- | | | | | |
All types of omic data respecting input format is supported to build classification models, biosignatures selection and network inference.
:point_right: In fact all numeric matrices.
You have to provide two types of data: numeric matrices and classes vector as csv tables for all same samples.
| | SIGIRR | MAOA | MANSC1 | | | ---- | ------ | ---- | ------ | - | | AOFJ | 0 | 150 | 1004 | … | | A13E | 34 | 0 | 0 | | | | | … | | |
| | ENSG00000139618 | ENSG00000226023 | ENSG00000198695 | | | ---- | --------------- | --------------- | --------------- | - | | AOFJ | 25 | 42 | 423 | … | | A13E | 0 | 154 | 4900 | | | | | … | | |
… :point_right: unlimited number of omic datasets.
| | 4292 | 5254 | 7432 | | | ---- | ---- | ---- | ---- | - | | AOFJ | 25 | 42 | 423 | … | | A13E | 0 | 154 | 4900 | | | | | … | | |
| | Y | | ---- | ----- | | AOFJ | condA | | A13E | condB | | | … |
Two types of models have been implemented so far to answer different questions: biosigner & sPLS-DA (DIABLO) .
| Features selected | Performances | | --------------------------------------------- | --------------------------------------------- | | | |
Biological Insight tab is dedicated to give biological sense to your data.
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.
Two types of result visualization are given:
| DESeq2 & DIABLO features | Enrichment tables | Enrichment Map | | -------------------------------------------- | -------------------------------------------- | -------------------------------------------- | | | | |
: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:
| Network Inference | PubMed query | | ---------------------------------------- | ---------------------------------------- | | | |
You could retrieve different results computed by multiSight in Home tab by:
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.
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