Magia Documentation

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MAGIA DOCUMENTATION

MAGIA (MiRNA And Genes Integrated Analysis web tool) allows the user to access a target prediction database (Query option) and to carry out a miRNA and

genes expression profiles integrated analysis, by adopting different statistical measures of profiles relatedness, algorithms for expression profiles combination and

target prediction methods. This tutorial comprises two sections, with examples of settings and results, for both the "Query" and the "Analysis" pipelines.

OUTLINE

QUERY - Browse the target prediction database

QUERY SECTION DESCRIPTION

EXAMPLE 1: Selection of target predictions for three different miRNAs

ANALYSIS - Integrative analysis of target predictions and miRNAs/genes expression profiles.

INTRODUCTION TO THE ANALYSIS SECTION: matched and not-matched expression matrices

EXAMPLE 2: Integrative analysis WITH MATCHED expression matrices

EXAMPLE 3: Integrative analysis WITH NOT MATCHED expression matrices

EXAMPLE 4: HOW TO USE Cytoscape for miRNA-network visualisation from MAGIA results

QUERY - Browse the target prediction database

MAGIA allows querying the miRNA target prediction database, obtained with different algorithms (miRanda, PITA and TargetScan) or Boolean combinations thereof

applied to user-defined selections of up to twenty miRNA and/or targets.

Example 1: Selection of target predictions obtained by miRanda (with score >=500) AND Pita (with score


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Example 1: Results

For the selected miRNA, the complete list of predicted targets is given as list of predicted relationships, each hyperlinked to different databases


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For all selected miRNA, the complete list of predicted target is also given as a downloadable text file.

mirna gene/transcript miranda pita

hsa-let-7e 57462 569.0 -14.34

hsa-let-7e 3360 460.0 -11.28

hsa-let-7e 56886 1251.0 -15.78

hsa-let-7e 55964 487.0 -12.22

hsa-let-7e 11163 558.0 -12.04

hsa-let-7e 6497 553.0 -11.16

hsa-let-7e 6645 785.0 -13.87

hsa-let-7e 11016 493.0 -18.28

hsa-let-7e 651 613.0 -12.4

hsa-let-7e 1641 707.0 -15.81

...

ANALYSIS - Integrative analysis of target predictions and miRNAs/genes expression profiles

INTRODUCTION TO MAGIA ANALYSIS SECTION

The integrated analysis of miRNA and gene/transcripts expression profiles may be applied to exploit expression data to cope with low specificity of target predictions

and high contest-dependency of miRNA-based regulation.

The finding assumption is that, at least for miRNAs acting at post-transcriptional level on mRNAs stability, for a given miRNA, true targets expression profiles are

expected to be anti-correlated with that of the miRNA. Thus, MAGIA combines target predictions with miRNAs and gene expression data analysis to identify, among

predicted target genes for each considered miRNA, those regulatory relationships significantly supported by expression data.

MAGIA allows analysing miRNAs and genes expression profiles by adopting different statistical measures of profiles relatedness and algorithms for expression

profiles combination.

The analysis framework is based on a multi-step procedure:

Target prediction

Integrated analysis of expression profiles

Post-transcriptional regulatory network visualisation and browsing

Functional annotation and enrichment analysis

MATCHED SAMPLES

The study of co-ordinated expression of miRNAs and putative targets may be used effectively to infer the most probably functional relationships by measuring

expression profiles of miRNAs and targets in exactly the same biological samples. This is normally achieved by hybridizing the same RNA samples to two different

platforms, and the resulting expression matrices contains expression vectors of the same length and regarding MATCHED SAMPLES.

Expression matrices

Expression matrices must be tab-delimited text files; the first row must contain sample names; the first column must contain gene/miRNA IDs

Matched data: sample names are sample IDs and must be exactly the same in both matrices!

In the figure below, a schema is shown describing how to prepare the matrices in the MATCHED SAMPLES case.


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Measures

MAGIA provides different measures and methods for the analysis of matched data.

Spearman Correlation: non parametric, rank-based linear correlation measure, suitable for non-normally distributed data and/or small sample size (e.g. 3 to

5).

Pearson Correlation: parametric linear correlation measure, suggested for normally distributed data and medium-large sample size (>5).

Mutual Information: a classic information measure quantifying the mutual dependence of variables, including non-linear relationships. Suitable for largesample size (>20 needed). Notice that both highly positively and negatively correlated vectors are associated to high mutual information.

Genmir: Combined analysis based on a Variational Bayesian method. Suitable only for sparse incidence matrices of target predictions.

The most intuitive and simple to interpret measures are those based on correlations.

Obviously, the power of the analysis depends on the number of available samples (technical replicates do not contribute much information) and on the variability of

expression profiles in considered samples. This is the reason why we recommend a preprocessing of expression matrices to eliminate miRNAs and/or genes almost

invariable.

NOT MATCHED SAMPLES

When matched data are not available one option is to collect miRNA and genes expression data produced on similar samples.

For instance, miRNAs only expression data in interesting normal and tumor samples have been produced in a lab and gene expression data in the same type of

biological samples (but not the same) are available in public database. The data collection and processing will give rise to two matrices, for miRNAs and genes, say

with 30 and 40 samples respectively. Each matrix include two classes of samples (T for tumor and N for normal), continuing our example we may have, 15 T plus 15

N for the miRNA matrix and 10 T plus 30 N for the gene matrix.

The meta analysis separately considers each matrix to identify expression profiles significantly variable among classes (which may be two or more) and integrate

results with target predictions.

The power of the meta-analysis of not-matched data is generally less that of the integrated analysis of matches data, also depending on sample size and real

similarity among considered samples, but can indeed give interesting indications for hypothesis generation and experimental design.

Expression matrices

Expression matrices must be tab-delimited text files; the first row must contain sample names; the first column must contain gene/miRNA IDs

Unmatched data: the sample name represent the sample class and the same classes should be present in both matrix samples

In the figure below, a schema is shown describing how to prepare the matrices in the NOT MATCHED SAMPLES case.


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Integrative analysis WITH MATCHED expression matrices

Example 2: STEP 1 - Method

Start by selecting ID type (RefSeq and ENSEMBL transcripts, EntrezGene and ENSEMBL genes), then choose the appropriate method and measure for the integrated analysis. In

this example, Pearson correlation is used as measure of miRNAs and genes expression profiles pairwise relatedness.


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Example 2: STEP 2 - Target prediction

Select a target prediction algorithm (miRanda, PITA and TargetScan), or a combination thereof. In this example RefSeq ID is used, and the intersection of miRanda and PITA is

selected. For each prediction score, the default threshold has been applied.

Example 2: STEP 3 - Expression data upload

Upload miRNAs and genes expression matrices.


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Sample miRNA and gene expression matrices, fully compliant with user-selected settings, are downloadable at the Step 3 of the Analysis (Tip: download

sample expression matrices, GSE14834 and use them for the analysis)

The user is also allowed to select a subset of rows IDs to be considered for the integrated analysis (optional, leave blank to consider all IDs in the matrix)

Example 2: STEP 4 - RESULTS

SUMMARY RESULT PAGE - This page shows the network and the table of top top 250 regulatory relationships supported by expression data analysis and gives links to the files

containing all relationships and allowing functional enrichment analysis.


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Gene-centered page - For a selected gene, this page shows all miRNAs resulting to be included regulatory relationships supported by expression data analysis. For each gene and

miRNA hyperlinks to different databases are provided.


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miRNA-centered page - For a selected miRNA, this page shows all genes resulting to be included regulatory relationships supported by expression data analysis. For each gene

and miRNA hyperlinks to di fferent databases are provided.

Functional enrichment analysis - Top supported target genes of miRNAs (default 250), are directly uploaded, with corresponding settings on the DAVID page, for functional

enrichment analysis (mapping to pathways and knowledge maps, ...)

This is done by exploiting David APIs (http://david.abcc.ncifcrf.gov/content.jsp?file=DAVID_API.html), imposing a maximum of 400 genes and of 2048 URL characters. When the

user selection is not compliant with these limits warning messages suggest to reduce the number of genes or to conduct directly David analysis on gene list extracted from the

.TSV file.

For the selected set of genes induced by the ranked interaction


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Cytoscape file - A text file is also provided, to be imported in Cytoscape: software for network visualization and analysis

Integrative analysis WITH NOT MATCHED expression matrices

Example 3: STEP 1 - Method

Select ID type (RefSeq and ENSEMBL transcripts, EntrezGene and ENSEMBL genes) and "meta analysis" from the list of available methods for the integrated analysis. This means

that, separately for genes/transcripts and miRNAs in available sample classes, MAGIA will calculate LIMMA p-values of differential expression, which are then combined by using

the inverse chi square distribution to identify oppositely variable miRNA-gene pairs.
http://www.cytoscape.org/


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Example 3: STEP 2 - Target prediction

Select a target prediction algorithm (miRanda, PITA and TargetScan), or a combination thereof. In this example RefSeq ID is used, and the intersection of miRanda and PITA is

selected. For each prediction score, the default threshold has been applied.

Example 3: STEP 3 - Expression data upload

Upload miRNAs and genes expression matrices.


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Sample miRNA and gene expression matrices, fully compliant with user-selected settings, are downloadable at the Step 3 of the Analysis (Tip: download

sample expression matrices and use them for the analysis)

The user is also allowed to select a subset of rows IDs to be considered for the integrated analysis (optional, leave blank to consider all IDs in the matrix)

Example 3: STEP 4 - RESULTS

SUMMARY RESULT PAGE - This page shows the network and the table of top top 250 regulatory relationships supported by expression data analysis and gives links to the files

containing all relationships and allowing functional enrichment analysis.


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Gene-centered page - For a selected gene, this page shows all miRNAs resulting to be included regulatory relationships supported by expression data analysis. For each gene and

miRNA hyperlinks to different databases are provided.

miRNA-centered page - For a selected miRNA, this page shows all genes resulting to be included regulatory relationships supported by expression data analysis. For each gene

and miRNA hyperlinks to di fferent databases are provided.


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Functional enrichment analysis - For the selected set of genes induced by the ranked interaction (default 250), data are directly uploaded, with corresponding settings on the DAVID

page, for functional enrichment analysis page (mapping to pathways and knowledge maps, ...)

Cytoscape file - A text file is also provided, to be imported in Cytoscape: software for network visualization and analysis

HOW TO USE Cytoscape for miRNA-network visualisation from MAGIA results

Example 4: STEP 1 - Relationships are given from MAGIA

Download the .tsv file from MAGIA

Example 4: STEP 2 - Cytoscape

Install Cytoscape from www.cytoscape.org

Example 4: STEP 3 - Import data in Cytoscape

In Cytoscape, use Import network from table option
http://www.cytoscape.org/http://www.cytoscape.org/


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Example 4: STEP 4 - Import data in Cytoscape

Select columns 1 and 2 as "Source for interactions": you will see your network in the default format.

Example 4: STEP 5 - Format the network layout

Use a specific layout among those provided by Cytoscape (eg. Organic) to improve network readability.

Play with colours and network types.


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Home | Documentation | Analysis | Query

MAGIA (MiRNA And Genes Integrated Analysis) a web tool for mRNA target prediction with algorithms of different types. Copyright 2009 by Andrea

Bisognin, Alessandro Coppe and Gabriele Sales.
http://gencomp.bio.unipd.it/magia/starthttp://gencomp.bio.unipd.it/magia/queryhttp://gencomp.bio.unipd.it/magia/analysishttp://gencomp.bio.unipd.it/magia/documentationhttp://gencomp.bio.unipd.it/magia/start


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