Contents
- Networks
- Pre-loaded data sets
- Adding your own data sets
- Highlighting factors via ontology associations
- Legend colors and how to adjust them
- Extracting customized subnetworks
- Restrictions
Networks
Two networks are available for exploration:
-
The Enriched Network was created
by merging the
Curated Network
with a hand-picked list of validated miRNA-target interactions.
-
The Curated Network gives a comprehensive
overview of dendritic cell activation pathways.
It was layouted and scientifically curated in a manual fashion
and is well-suited for exploration.
The controls for all networks are identical and elaborated below.
You can learn more details from
the original article featuring the platform
.
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Pre-loaded data sets
You can project data from the analysis featured in the
accompanying article.
The factors in the network view below will morph into pie charts
while at least one data set is selected.
The expression of each factor is shown
by projecting a color based on differential expression (log2FC)
into the pie slices.
Each slice corresponds to one of the selected data sets -
you can see which data set goes into which slice by checking
the pie icons that appear to the left of the checkboxes.
With each selected data set, the pie charts will acquire a slice
until you exhaust the number of possible slices at four.
For details about the expression color scale,
see the description below.
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Adding your own data sets
You can add your own data to the network for data mining purposes.
Due to the way this is implemented, no data upload to our server is
necessary.
Your data is only ever stored in your own browser session.
To successfully add data, you must first prepare a CSV file.
This can be done in a spreadsheet application like LibreOffice or
Microsoft Excel.
The spreadsheet should look similar to this one:
-
The table must have a header in the first row, and the
header of the first column must be "Symbol".
The headers of the remaining columns can be freely chosen
, with each one representing one condition or difference.
Column headers must be unique across all files you upload
in one session.
-
The first column lists the gene symbols for which you
have data.
As the map was set up to permit the analysis of human data,
you need to employ the official HGNC symbols.
(These are often identical to their
mouse homologs' official symbols,
but there are notable exceptions.)
Gene symbols can be found in many other databases - see the below
NCBI Gene and UniProt screenshots for eotaxin (CCL11) for reference:
The page will automatically match up your symbols
with all the corresponding factors (if there are any).
-
All columns after the first list the values you want to project,
with one condition or difference per column.
There is no restriction on the kind of values you use,
apart from them having to be numbers.
The interpretation, of course, is yours to make.
Once you have the spreadsheet prepared, export a file in CSV format.
Then click on the very first list entry
Your data (click to add)
and select the exported file.
Your column names will be appended as data sets to the first list entry
and can be selected for visualization from there.
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Highlighting factors via ontology associations
We have associated each factor in the network with the
GO term and
Reactome
ontologies.
Ontology terms that were related to at least three genes in the
current map can be selected from the drop-down menu, and the
associated factors will be highlighted with a golden halo.
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Legend colors and how to adjust them
While no data set is selected, the factors in the current map are
colored according to their molecular type.
Some will also have individual shapes.
In this state, the legend shows a color code for reaction and
factor types.
On selecting a data set, the legend will adapt to represent the spectrum
of values that were observed in the selected categories.
Blue represents values below and red values above 0 under the listed
conditions.
The color legend comes with two sliders that can be
used to adjust the colors in the network.
The sliders initially occupy their respective maxima, i.e., lowest and
highest observed value.
Moving either slider closer to zero will increase the saturation in all
like-colored pie slices, while moving them towards the limits will
decrease it.
Note that all factors with an absolute value higher than the
adjusted slider position will drop out of the linear color projection
range and appear fully saturated.
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Extracting customized subnetworks
You can focus on your molecules of interest by eliminating all other
factors from view.
To do this, first mark all relevant factors via either of two methods:
-
Click on a factor in the network view and press the
Mark this factor button.
-
Select the factor from the second drop-down menu below the one
for regulatory subnetwork.
No matter which method you use, every marked factor will be added to
the above-mentioned drop-down and shown with a thick black rim in the
network view.
Factors can be unmarked by deleting them from the drop-down or by
clicking the Unmark this factor after seleting it in the
network view.
Once you are finished with your selection, click the
Extract button
at the bottom right side.
Its label will change to
Restore and all unmarked factors
will be temporarily removed from the network view.
If the
☑ Include connected factors
option above the button is checked, factors with direct connections
to the marked ones will remain visible.
While a customized subnetwork is shown, you cannot mark or unmark factors.
To restore the original network and re-enable factor marking operations,
click the Restore button.
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