Input data

Omics data can be uploaded by two ways in MetExplore:

• A text file with columns: By default, columns are separated by tabulations but the separator can be changed in the mapping form.
• By copy paste data from Excel or text tabulated file: Copy (Ctrl-C) your data in your Excel/tabulated file and paste them with Ctrl-V directly in the mapping grid.

In both cases, the first column must be the features of the biological objects (see next section) which will allow to map them in the biological network. Following columns are facultative and correspond to numeric values in different conditions. If the first line of your data contains the names of these conditions, don't forget to check "Consider first row as header of columns".



Copy-paste your data from Excel files.

Selection of the biological objects to map

Mapping can be performed in all biological objects stored in a metabolic network (pathways, reactions, metabolites, enzymes, gene products and genes). Select the type of the biological objects you want to map in the "Object" menu in the mapping grid. Then, in the menu "Element" select the feature of the objects which will be used to identify them.
Each biological object can be mapped thanks to its name or its identifier.

For metabolites, two additional features are available for the mapping: the Inchi code and the monoisotopic mass. In the latter case, the user can indicate the allowed error in ppm in the menu below the mapping grid.

Mapping modes

One mapping mode

The mapping default mode is the "one mapping mode". In this mode, only one mapping is done. This mode is used to simply map a list of objects (one column) with associated single numerical values (two columns) or to compare the same network in different conditions (multiple columns). Mapping and pathway enrichment will be performed for all elements in the first column, irrespectively of the values in the different columns.

Multiple mapping mode

The "multi mapping" mode will produce one mapping for each column. In each mapping, the objects for which the value is different from NaN will be tagged as identified in the grid and pathway enrichment will be performed only on these objects. Select "Perform one separate mapping for each column" to activate this mode.



Input file for a multiple mapping. Two mappings will be created: one for the Intestine condition and one for the Liver condition. In the Intestine condition, metabolites that will mapped as identified will be those that have a value different from NaN in the Intestine condition.

Mapping results displayed in grids

Results on the grid corresponding to the biological object mapped

In the data grid corresponding to the identified biological object, a main column by mapping is added. This main column is subdivided into several sub-columns. One indicates if the object has been identified and the others correspond to the values ​​indicated in the other columns of the input file, if they exist.

Propagation

Mapping on any biological object triggers an enrichment analysis of the metabolic pathways. In the metabolic pathways table, a main column corresponding to the mapping is added, comprising the following sub-columns:

• Propagate: the percentage of mapped biological objects (reactions, proteins, etc ...) in this pathway
• Nb mapped: The number of biological objects mapped in this pathway
• p-value: the over-representation of the mapped objects in each pathway is tested using a Right tailed Fisher Exact Test. The p-values are corrected to account for the multiple tests performed for all pathways. Bonferroni and Benjamini-Hochberg corrected p-values are presented in the grid. *** indicates a p-value < 0.0001; **indicates a p-value < 0.001; *indicates a p-value < 0.05.

Pathway enrichment from a metabolite mapping

Reaction (respectively Metabolite) mapping adds also mapping columns into Metabolite (respectively Reaction) grid with coverage values. Values in additional colums are also reported in the Pathway, Metabolite and Reaction grids if the option "Without condition values" is deselected in the Propagate menu in the Mapping form. Three options are possible:

• with conditions (min value): the minimum value among all the columns is reported
• with conditions (max value): the maximum value among all the columns is reported
• with conditions (average value): the average value among all the columns is reported

Metabolites mapping taking in account background

Pathway enrichment from metabolomic data

Pathway enrichment analysis helps researchers gain mechanistic insight into metabolite lists generated from metabolomic experiments. This method identifies biological pathways that are enriched in a metabolite list more than would be expected by chance. Pathway enrichment can be computed when all metabolites can be observed but it's not the case. So we have to take in account it to compute relevant pathway enrichment.

Method

To avoid this bias we purpose to load a file with all metabolites that you can analyse with your analytic methods. This file will be take in account to define pathway metabolite composition.



Mapping form to make Pathway enrichment taking in account metabolomic background

Results will be display as common mapping.

MetaboRank : a network based recommendation system to interpret and enrich metabolomics results

Find insightful metabolites that are well connected to your metabolites of interest obtained from experimental data. This tool uses an extended version of Recon2, the highly curated reconstruction of the human metabolic network, which contains only relevant connections selected based on biochemical criterions. It internally uses a recommendation algorithm inspired by what can be found on social networks to suggest you new users based on their network proximity.

This tool executes personalized Page Rank and Chei Rank algorithms:
C. Frainay, et al. "Metabolites you might be interested in: a network based recommendation system to interpret and enrich metabolomics results", Bioinformatics (2017), 31(20):3383–3386. | Submitted |

Use MetaboRank

Launch

To launch MetaboRank you need to select "MetaboRank" in header "Omics" menu.
After it you have do do a mapping of metabolites to select your fingerprint.

Then you can launch MetaboRank. This analysis return Page rank and Chei rank of each metabolite of the network.



Launch MetaboRank The results will appear in the Jobs grids when they will be finished. To get the result click on result button in job tab in side component.



Get MetaboRank results

Use fold change

MetaboRank allows to use fold change as weight in the random walk algorithm.
To do that you have to do a mapping of metabolites with a fold change condition to map fold change on our fingerprint and to select the corresponding condition in the MetaboRank form.

So metabolites near than metabolite of the fingerprint with big fold change could best recommendation.

Interpreting results

Page Rank & Chei rank

This analysis return Page rank and Chei rank of each metabolite of the network.
In the metabolite grid, a new column will be displayed with a three sub columns:

• "Fingerprint" to show the metabolite of the fingerprint
• "MetaboRank Out" for the score of the Page rank algorithm
• "MetaboRank In" for the score of the Chei rank algorithm

MetaboRank results in grid

Scatter plot

In order to have intuitive representation of MetaboRank, MetExplore provides an interactive scatter plot.



MetaboRank results in scatter plot

It allows to :

• Quickly see suggestions. So metabolites with high "MetaboRank In" and high "MetaboRank Out"are suggested
• Select node or multiple nodes
• Map selected node in MetExplore metabolites table in order to add good suggestion to relaunch the analysis and extend the fingerprint
• Set the axis scale in log to spread out point
• Filter node in "Fingerprint" or "Not in fingerprint" by clicking in the bottom caption
• Export images in the right top