magia documentation
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Home Documentation Analysis Query
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.
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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
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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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