EnrichmentMap Cytoscape App 3.0¶

Enrichment analysis (also known as functional enrichment) is an helpful technique for high-throughput data interpretation. Given a list of genes resulting from an experiment, enrichment analysis enables to identify functional categories that are over-represented. Such functional categories are typically derived from functional annotations (such as the Gene Ontology), or from pathway databases (such as KEGG), or other resources (such as the collection of disease signatures in MSig DB, or protein complexes in MIPS).

However, enrichment results are often characterized by lots of redundancy and inter-dependencies between gene-sets representing functional categories. For instance, Response to radiation, DNA Integrity Checkpoint and p53 Pathway have several genes in common. Since the typical enrichment analysis can output up to 300 hundred different gene-sets, some form of organization is required to navigate results.

To address this, we organize gene-sets into a network, called enrichment map. Two gene-sets are connected in the enrichment map network if they have a high overlap, i.e. if they share many genes. Applying automatic layout techniques, groups of inter-related gene-sets tend to cluster together, providing for a much easier and intuitive visualization.

Please also see The EnrichmentMap Protocol for details on automating EnrichmentMap.

Feature Requests and Reporting Bugs¶

The EnrichmentMap GitHub issue tracker can be used to report a bug or request a feature.

To Report a bug:

Go to https://github.com/BaderLab/EnrichmentMapApp/issues
Click on New Issue
Write a short description of the issue. It is very helpful to provide a series of steps that can be taken to reproduce the issue.
If possible attach a session file (.cys) or example input files.
Enter App version, Cytoscape version and operating system.
Click on Submit new issue

Cite EnrichmentMap¶

Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

Merico D, Isserlin R, Stueker O, Emili A, Bader GD

PLoS One. 2010 Nov 15;5(11):e13984.

PubMed Abstract - PDF

Examples of Use¶

Functional impact of global rare copy number variation in autism spectrum disorders.

Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, et al.

Nature. 2010 Jun 9 (Epub ahead of print)

PubMed Abstract - PDF

Nature Blogs
Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps

Isserlin R, Merico D, Alikhani-Koupaei R, Gramolini A, Bader GD, Emili A.

Proteomics 2010, March 10(6):1316-27

Pubmed Abstract - PDF

Papers Citing Enrichment Map¶

Citations in Pubmed Central
Pathway analysis of expression data: deciphering functional building blocks of complex diseases.

Emmert-Streib F, Glazko GV.

PLoS Comput Biol. 2011 May;7(5):e1002053.

PubMed
Inflammasome is a central player in the induction of obesity and insulin resistance.

Stienstra R, van Diepen JA, Tack CJ, Zaki MH, van de Veerdonk FL, Perera D, Neale GA, Hooiveld GJ, Hijmans A, Vroegrijk I, van den Berg S, Romijn J, Rensen PC, Joosten LA, Netea MG, Kanneganti TD.

Proc Natl Acad Sci U S A. 2011 Aug 29.

PubMed
Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma

Witt H, Mack SC, Ryzhova M, Bender S, Sill M, Isserlin R, Benner A, Hielscher T, Milde T, Remke M, Jones DTW, Northcott PA, Garzia L, Bertrand KC, Wittmann A, Yao Y, Roberts SS, Massimi L, Van Meter T, Weiss WA, Gupta N, Grajkowska W, Lach B, Cho YJ, von Deimling A, Kulozik AE, Witt O, Bader GD, Hawkins CE, Tabori U, Guha A, Rutka JT, Lichter P, Korshunov A, Taylor MD, Pfister SM

Cancer Cell, Volume 20, Issue 2, 143-157, 16 August 2011

PubMed Abstract - PDF

Installation¶

Cytoscape minimum version 3.4 is required.

Install Cytoscape

Download and install the latest version of Cytoscape at
http://www.cytoscape.org/download.php.

Install EnrichmentMap 3.0

Open Cytoscape

In the main menu select Apps > App Manager

In the App Manager select EnrichmentMap in the list of All Apps and click the Install button.

Alternatively the EnrichmentMap Pipeline Collection can be installed. This will install EnrichmentMap as well as a suite of other apps that work well with EnrichmentMap, including AutoAnnotate, WordCloud and clusterMaker2.

EnrichmentMap can also be installed from the Cytoscape App Store at
http://apps.cytoscape.org/apps/enrichmentmap

What’s New¶

EnrichmentMap 3.0¶

EnrichmentMap 3.0 is a major release, with the following new features:

EnrichmentMap networks can now be created from any number of enrichment data sets (EnrichmentMap 2.0 supported maximum 2 data sets).
New chart visualizations on nodes for visualizing NES scores, p-values or q-values. Three new charts are available: radial heat-map, linear heat-map and heat-strips.
New network creation dialog has the ability to scan a folder for files belonging to each dataset. In most cases this removes the need to manually enter the required files.
New control panel allows the contents and style of the network and charts to be updated dynamically.
New legend dialog.
New streamlined HeatMap panel has the ability to summarize expression data.
Several new commands that allow EnrichmentMap to be automated from external scripts and CyREST.

Quick Tour¶

Creating the Network¶

To create an Enrichment Map network go to the Cytoscape main menu and select Apps > EnrichmentMap.

This will show the EnrichmentMap panels and open the Create EnrichmentMap Dialog.

Note

If the Create EnrichmentMap Dialog does not appear then click the (+) icon at the top of the EnrichmentMap Main Panel.

The quickest way to import data is to scan a folder for enrichment analysis data files (for example a GSEA results folder).

Click the icon and select a folder. EnrichmentMap will scan the folder for files containing enrichment data, expression data, ranks, classes and gene set definitions. These files will be arranged into a List of Data Sets, each of which contains the data for one experiment. Click the Build button to create the network.

Note

For more details see Creating a Network

Panels¶

Main EnrichmentMap Panel
- Used to customize the look of the network in several ways.
EnrichmentMap Network
- The Cytoscape network view shows the EnrichmentMap network.
Expression Panel (Heat Map)
- Shows gene expression data for selected nodes and edges.

Interpreting the Network¶

Nodes represent gene sets.
Node size represents the number of genes in the gene set.
Edges represent overlap between gene sets.
Edge width represents the number of genes that overlap.
The default layout algorithm causes gene sets with high overlap to cluster together.
Each node contains a chart that shows the enrichment scores, such as NES (for GSEA), P-value or FDR Q-value. The enriched phenotype is conveyed by a color gradient. The chart data can be changed using the Style section of the EnrichmentMap panel.

EnrichmentMap creates several columns in the node and edge tables. They can be seen in the Node Table and Edge Table panels. Columns created by EnrichmentMap start with “EM”.

Note

For more details see Network Visualization.

Main EnrichmentMap Panel¶

The Main EnrichmentMap Panel can be used to customize the network in several ways.

Filter section
- Node cutoff slider: Nodes with a p-value or q-value that do not pass the cutoff are hidden from view.
- Edge cutoff slider: Edges with a similarity score that does not pass the cutoff are hidden from view.
- Data set list: Lists Data Sets that were used to create the network. De-selecting the checkbox next to the name of a data set causes gene set nodes that are not contained in the data set to be hidden from view.
- Add signature gene sets button: Opens the Post Analysis dialog which is used to add more gene sets to the network.
Style section
- Chart data: Allows to pick which data columns are used by the node charts (e.g. NES, p-value or q-value).
- Chart type: Various chart visualizations are available.
- Chart colors: Various color schemes are available for the charts.

Note

For more details see Main Panel

Legend Dialog¶

The legend dialog can be opened by clicking on the gear icon at the top of the main panel and selecting Show Legend.

The Legend Dialog shows a visual representation of how the network elements can be interpreted, including what data is visible on the node charts.

Note

For more details see Legend Dialog

Expression Panel (Heat Map)¶

The HeatMap panel shows gene expression data for selected nodes and edges.

Genes: You may select multiple gene sets (by selecting several nodes or edges). When more than one gene set is selected you may choose to view all of the genes (union) or just the genes that are common (intersection).
Expressions: Allows to show the raw expression data or to normalize the data.
Compression: If there is a large number of expression columns in the HeatMap they can be compressed down to the median, min or max value.
Show values: When selected shows the actual expression values, otherwise just shows the color gradient.
The contents of the HeatMap can be exported to a TXT or PDF file.

Note

For more details see Expression Panel (Heat Map)

Post Analysis (Add Signature Gene Sets)¶

The Add Signature Gene Sets panel you to add more gene sets to an existing network. This is also called Post Analysis.

To access the dialog click the Add Signature Gene Sets… button on the Main EnrichmentMap panel.

There are currently two types of Post Analysis Available: Known Signature and Signature Discovery. The contents of the panel will change depending on the type of analysis chosen. Known signature mode calculates post analysis edges for a small subset of known gene-sets. Signature discovery mode allows for filtering of large set of potential signatures to help uncover most likely sets.

The result of running Post Analysis is a new node for each signature gene set (yellow triangle) and edges from the signature gene set to each existing gene set when the similarity passes the cutoff test. A new data set for the signature gene sets is added to the data set list on the Main EnrichmentMap panel.

Note

For more details see Post Analysis

EnrichmentMap Protocol¶

Please see The EnrichmentMap Protocol for details on automating EnrichmentMap.

Creating a Network¶

To start using EnrichmentMap go to the Cytoscape main menu and select Apps > EnrichmentMap. This will show the EnrichmentMap panels and open the Create EnrichmentMap Dialog.

The dialog can also be opened by clicking the button at the top of the main EnrichmentMap panel.

Note

See File Formats for details on the various file formats accepted by EnrichmentMap.

Create EnrichmentMap Dialog¶

This dialog is used to enter paths to data files and filtering parameters.

The dialog has the following panels:

List of Data Sets.
File entry panel.
Node filtering parameters.
Edge filtering parameters.
Action buttons.

Each of these panels will be explained in more detail below.

If the dialog is closed and then reopened everything that was previously entered will be saved. You may experiment with creating multiple EnrichmentMap networks with different parameter choices without having to enter all of the information every time. To clear out and reset all fields to their defaults click the Reset button at the bottom left of the dialog.

1) The Data Set List¶

A Data Set contains the results of one enrichment analysis, along with associated data such as expressions, gene sets and classes.

Selecting an entry in the Data Set List will show the file input fields for that data set.

There is a special entry called Common Files. Files entered on this panel will be included in all the data sets.

As of EnrichmentMap 3.0 there is no limit on the number of data sets that can be entered. However in practice adding more data sets increases the size and complexity of the resulting network.

Creating Data Sets by Scanning For Files¶

The first step is to enter the paths to the data set files. This can be a time consuming process if done manually; for that reason EnrichmentMap has the ability to scan a folder and automatically detect enrichment, expression, class and GMT files. These files are automatically assembled into data sets based on naming conventions. This scanning process works well for GSEA results because GSEA outputs a folder of results files.

To scan a folder click the button, then select a folder. If EnrichmentMap can detect data files it will automatically add one or more data sets to the list. Scanning is based on a heuristic (that may change between versions of EnrichmentMap), so please check that the file entry panel contains the correct files after scanning.

Creating Data Sets Manually¶

To manually create a data set click the button. A new data set will appear in the list and all the file input fields for that data set will be empty.

To delete a data set select it in the list and then click the button.

2) File Entry Panel¶

The file entry panel has the following fields:

Data set name
- The data set can be named anything. However two data sets may not have the same name. When scanning for files a name will be automatically chosen based on the file name of the enrichment file(s).
Analysis type
1. GSEA
  - Takes as inputs the output files created in a GSEA analysis. When GSEA is chosen there will be two input fields for enrichment files. GSEA analysis always has two enrichment results files, one for each of the phenotypes compared.
2. Generic/gProfiler
  - Takes as inputs the same file formats as a GSEA analysis except the Enrichment results file is a different format and there is only one enrichment file.
3. David/BiNGO/Great
  - Has no GMT or expression file requirement and takes as input enrichment result file as produced by DAVID, BiNGO or GREAT tools.
File input fields
- There are input fields for Gene Set, Enrichment, Expression, Rank and Class files. Fields with a * next to their name are required, all other fields are optional. Which fields are required depends on the analysis type.
- Click the button next to an input field to open a file browser.
Phenotypes
- Enter the names of two classes from the class file that are being compared in the enrichment analysis. When a class file is entered the dialog will parse the class file and automatically fill in these fields.
- These phenotypes will be highlighted in the Expression Panel (Heat Map).

Note

See File Formats for details on the various file formats accepted by EnrichmentMap.

Common Files¶

Select Common Files at the top of the data set list to show a special file entry panel. GMT, expression and class files entered on this panel will be included in all the data sets.

Files on the Common Files panel will override files entered in the individual data set panels.

Note

Even though Common Files is located inside the data set list it is not a data set.

3) Gene Set (Node) Filtering¶

Gene sets must pass the following criteria to be included in the network.

Basic Options¶

Filter gene sets by expressions
- If selected genes that are contained in the gene set (GMT) files or the enrichment files, but are not contained in the expression files will not be included in the network.
FDR q-value cutoff
- Gene set with a q-value lower than the one entered will not be included in the network.

Advanced Options¶

Available when the Show Advanced Options checkbox at the bottom right of the dialog is selected.

p-value cutoff
- Gene sets with a p-value lower than the one entered will not be included in the network.
- The default value of 1.0 will not cause any gene sets to be removed from the network.
NES (GSEA only)
- Positive: Only gene sets from the positive enrichment file will be included.
- Negative: Only gene sets from the negative enrichment file will be included.
- All: Both enrichment files will be included
Filter by minimum experiments
- Selected this to enable the Minimum experiments field.
Minimum experiments
- A gene set must be included in this many data sets to be included in the network.

Note

See Tips on Parameter Choice for more details on how to tune gene set filtering.

4) Gene Set Similarity (Edge) Filtering¶

A similarity score is computed for every pair of gene sets based on how many genes they have in common (set intersection). If the similarity score passes the following criteria then an edge will be created between the gene set nodes.

Basic Options¶

Data set edges (Note: This option has no effect if there is only one data set)
- Separate edge for each data set
  - If a gene set is associated with more than one data set it is possible for the contents of the gene set to be different in each data set. This often happens when the data sets have different expression files and the “filter gene sets by expressions” option is enabled. A separate similarity score will be computed for each data set resulting in potentially many more edges and a much denser network.
- Combine edges across data sets
  - Gene sets with the same name are combined (set union) and then the similarity score is calculated.
  - There will be at most one edge between a pair of gene set nodes.
- Automatic (default)
  - EnrichmentMap decides which of the above options to use.
  - If there are exactly two data sets and they have different expression files then separate edges is chosen, otherwise combine edges is chosen. This is done to be consistent with the behavior of EnrichmentMap 2.0.
Connectivity
- Moving the slider towards sparse will produce fewer edges, moving it towards dense will produce more edges.

Advanced Options¶

When show advanced options is enabled the Connectivity slider is replaced with options that allow greater control over the number of edges in the network.

Cutoff
- Edges with a similarity score lower than the one entered will not be included in the network.
Metric
- Used to choose the formula used to calculate the similarity score.
- Jaccard Coefficient
```
Jaccard Coefficient = [size of (A intersect B)] / [size of (A union B)]
```
- Overlap Coefficient
```
Overlap Coefficient = [size of (A intersect B)] / [size of (minimum( A , B))]
```
- Combined
  - Merges the Jaccard and Overlap coefficients.
  - When selected a slider appears allowing to adjust the percentage of each coefficient to use.

Note

See Tips on Parameter Choice for more details on how to tune gene set filtering.

5) Action Buttons¶

Reset
- Clears out and resets all fileds to their defaults.
Build
- Creates the EnrichmentMap network.
- First runs validation on the inputs. If there are any problems (eg. required fields missing, duplicate data set names) a error dialog is shown. The problems must be fixed before the network can be created.
- This is a potentially long running task.
Cancel
- Close the dialog without creating a network.

Network Visualization¶

Style¶

EnrichmentMap creates a separate visual style for each network. The visual style has the following characteristics:

Nodes represent gene sets.
Node size represents the number of genes in the gene set.
Edges represent overlap (similarity) between gene sets.
Edge width represents the number of genes that overlap between a pair of gene sets.
Node fill represents enrichment scores, such as NES (for GSEA), p-value or q-value.
- When there is only 1 data set the enriched phenotype is conveyed using a color gradient.
- When there are 2 or more data sets nodes are overlayed with charts, where each segment of the chart shows enrichment using a color gradient.

The network is arranged by a force directed layout which causes gene sets with high overlap to cluster together.

Expression data is visualized in a separate panel called the Heat Map panel. For more details see Expression Panel (Heat Map).

Visual Properties¶

Warning

EnrichmentMap automatically maintains the visual properties described below. If you manually change these visual properties EnrichmentMap will overwrite your changes. If you want to create a custom visual style you must first create a copy and then make changes to the copy.

Visual properties are available on the Style tab of the Control Panel.

Node Visual Properties¶

There are two types of nodes:

Enrichment gene set nodes

Regular gene set nodes that are created when the network is first created.

Enrichment nodes can have many different visualizations depending on the settings in the Style section of the Main Panel.

Signature gene set nodes

Added to an existing network by Post Analysis.

Do not have chart visualizations.

Edges connected to signature nodes are dashed.

Enrichment Signature

Visual properties¶

Visual Property	Meaning	Type	Column(s)
Shape	Gene Set Type	Discrete Mapping	EM#_GS_Type (ENR = Square, SIG = Diamond)
Fill Color	NES, p/q-value	Discrete Mapping	EM#_pvalue, EM#_fdr_qvalue, EM_NES
Label	Gene set name	Passthrough Mapping	EM#_GS_DESCR
Size	Size of gene set	Continuous Mapping	EM#_gs_size
Image/Chart 1	NES, p/q-value	Chart	EM#_pvalue, EM#_fdr_qvalue, EM_NES

Edge Visual Properties¶

If there are 2 data sets, and/or the Separate edge for each data set option was chosen, then edges will have different colors for each data set. The edge color corresponds to the color of the icon next to the data set name in the main panel.

If the network has only one data set, or if the Combine edges across data sets option was chosen, then all the edges between enrichment gene sets will be the same color.

Edges connected to signature gene sets have a different color and are dashed.

Visual properties¶

Visual Property	Meaning	Type	Column(s)
Line Type	Edge Type	Discrete Mapping	interaction (default = solid, sig = dashed)
Stroke Color	Data Set or Signature	Discrete Mapping	EM#_Data Set
Width	Size of gene set overlap	Continuous Mapping	EM#_similarity_coefficient

Chart Visualization¶

There are 3 types of chart available for visualizing enrichment values.

Radial Heat Map	Heat Map	Heat Strips

Each segment of the chart is equal size and represents the enrichment value from one data set. The color of each chart segment is a color gradient indicating the enrichment value. The default color scheme shows down-regulated scores in red and up-regulated scores in blue.

The legend dialog can be used to see which chart segment corresponds to which data set and the color gradient.

Use the Style section of the Main Panel to change the type chart and the color scheme.

Columns¶

EnrichmentMap creates several columns in the node and edge tables. They can be seen in the Node Table and Edge Table panels.

Columns created by EnrichmentMap have the following pattern:

EM#_column_name (data set name)

Where…

# is a number that is automatically assigned to each network
Data set name is used to differentiate between data sets. There will be one such column for each data set.

Node Columns¶

EM#_Name

The gene set name.

EM#_Formatted_name

A wrapped version of the gene set name so it is easy to visualize.

EM#_GS_DESCR

The gene set description (as specified in the second column of the gmt file).

EM#_Genes

The list of genes that are part of this gene set.

EM#_gs_size

Number of genes the union of the gene set across all data sets.

EM#_GS_Type

Used by the visual style to discern between regular enrichment nodes and signature gene set nodes.

Additionally there are attributes created for each dataset:

EM#_pvalue (…)

Gene set p-value, as specified in GSEA enrichment result file.

EM#_fdr_qvalue (…)

Gene set q-value, as specified in GSEA enrichment result file.

EM#_Colouring (…)

Enrichment map parameter calculated using the formula 1-pvalue multiplied by the sign of the ES score (if using GSEA mode) or the phenotype (if using the Generic mode)

GSEA specific attributes (these attributes are not populated when creating an enrichment map using the generic mode).

EM#_ES_dataset (…)

Enrichment score, as specified in GSEA enrichment result file.

EM#_NES_dataset (…)

Normalized Enrichment score, as specified in GSEA enrichment result file.

EM#_fwer_qvalue (…)

Family-wise error score, as specified in GSEA enrichment result file.

Edge Columns¶

For each Enrichment map created the following attributes are created for each edge:

EM#_Data Set

Contains the name of the data set that the edge is associated with, or ‘compound’ if the Combine edges across data sets option was selected when the network was created.

EM#_Overlap_size

The number of genes associated with the overlap of the two gene sets that this edge connects.

EM#_Overlap_genes

The names of the genes that are associated with the overlap of the two gene sets that this edge connects.

EM#_similarity_coefficient

The calculated coefficient for this edge.

Main Panel¶

The main EnrichmentMap panel has the following sections:

Toolbar
Filter section
Style section

Each of these sections will be explained in more detail below.

If the main panel is not visible go to the Cytoscape main menu and select Apps > EnrichmentMap.

To hide the panel click the Close button at the bottom right.

Toolbar¶

Network combo box
- This combo box can be used to quickly switch between EnrichmentMap networks without having to navigate to the Network tab. Only networks created by EnrichmentMap are listed.
Plus button
- Opens the Create EnrichmentMap Dialog.
Gear button
- Opens the panel menu (explained in more detail below).

Panel Menu¶

Show Legend
- Opens the Legend Dialog.
Hide/Highlight filtered nodes and edges
- Changes the appearance of nodes and edges that are filtered out. See Filter Section below.

No Filter	Highlight	Hide

Style Section¶

The style panel is mainly used to manipulate chart visualizations on nodes.

For more details on chart visualizations see Chart Visualization.

Chart Data
- – None –
  - If there is 1 data set then node shows a pre-computed color gradient for the p-value. If there are 2 or more data sets then the node color has no meaning and is set to grey.
- NES Columns
  - Enrichment values from the EM#_NES columns are used.
  - Only available if the analysis type is GSEA.
- P-value Columns
  - Enrichment values from EM#_pvalue columns are used.
- Q-value (FDR) Columns
  - Enrichment values from EM#_fdr_qvalue columns are used.
Chart Type
- Field is enabled if Chart Data is set to a value other than – None –.
- Three chart types are available: Radial Heat Map, Heat Map, and Heat Strips. For more details see Chart Visualization.
Color Scheme
- Several Color Brewer colorblind safe palettes are available.
- When NES Columns is chosen for Chart Data then the RdBu-9 palette will be available. This palette is the same as the standard color gradient used in EnrichmentMap 2.0.
Show chart labels
- Enable this option to show enrichment values for each chart segment.
Publication-Ready
- Makes the network view ready for printing. Removes node labels and sets the network background to white.
Set Signature Edge Width…
- Opens a dialog that has several options for how the width of signature edges is calculated. For more details see Edge Width Dialog.
Refresh Button
- Resets the visual style.
- Sometimes Cytoscape does not update the visual style properly. To fix any inconsistencies between the network view and the Style section of the main panel click this button.

Expression Panel (Heat Map)¶

The Heat Map panel shows expression values for genes that are associated with selected nodes and edges.

You may select any number of nodes and/or edges in the network. Selecting an edge is equivalent to selecting the two gene set nodes that are connected to the edge.

Note

In EnrichmentMap 2.0 there were two separate Heat Map panels for nodes and edges. In EnrichmentMap 3.0 they have been combined into a single panel.

Note

“Where are my expression values?”

If there are more than 50 expression values per gene then the Compress: Median option will be automatically enabled. Set Compress to –None– to see the original values. See below for more details.

Table¶

Click on any of the column headers to sort the table by that column.

Gene Column
Description Column
Sort Column
- This column is used to sort by ranks or by hierarchical clustering.
- Click the Sort button to show a menu of ranking options.
- If a data set has a rank file then the ranks will be listed in the menu.
- See Panel Menu below for details on how to load additional rank files.
- Hierarchical Clustering: Genes are clustered using a hierarchical clustering algorithm based on their expression values, the resulting hierarchy is then used to sort the genes.
Expression Columns
- Shows expression values for each experiment.
- If there is more than one data set and each data set has common expression values then the values will only be shown once.
- If there are two or more data sets and they have different expression values then all the expression values are shown.
  - A colored bar that runs along the top of the expression column headers can be used to differentiate between the data sets. The color of the bar corresponds to the color shown next to the data set name in the main panel.
- Genes that do not have expression data are shown in gray.
Phenotype Highlight
- The phenotypes that were entered in the Create EnrichmentMap Dialog are highlighted.
Leading Edge
- Genes that are part of the leading edge are highlighted in yellow.
- Available for GSEA results when a single gene set is selected.
- See below for more details.

GSEA Leading Edge¶

For every gene set that is tested for significance using GSEA there is a set of proteins in that gene set defined as the Leading Edge. According to GSEA the leading edge is:

“the subset of members that contribute most to the ES. For a positive ES, the leading edge subset is the set of members that appear in the ranked list prior to the peak score. For a negative ES, it is the set of members that appear subsequent to the peak score.”

In essence, the leading edge is the set of genes that contribute most to the enrichment of the gene set.

For Enrichment Map, leading edge information is extracted from the GSEA enrichment results files from the column denoted as Rank at Max. Rank at max is the rank of the gene where the ES score has the maximal value, i.e. the peak ES score. Everything with a better rank than the rank at max is part of the leading edge set.

Panel Menu¶

Add Rankings
- Opens a pop-up dialog that allows you to load an additional rank file. See Add Ranks Dialog below for more details.
Export as TXT
- Export the expressions currently being viewed in the heat map table as a tab-separated text file. The first line of the file contains the table headers.
- If the heat map is showing the leading edge then you will be prompted to save just the genes that are part of the leading edge or all the genes.
Export as PDF
- Export the the expressions currently being viewed in the heat map table as a PDF file.
- The visual state of the table is reflected in the PDF file. For example to show the expression values in the PDF file enable the show values option in the toolbar.
Hierarchical Cluster - Distance Metric
- Allows to select the distance metric used by the hierarchical cluster algorithm.
Auto-Focus HeatMap
- If enabled then every time a node/edge is selected the HeatMap panel will be brought to the front.

Note

Auto-Focus HeatMap was enabled by default in EnrichmentMap 2.0. It is now disabled by default in EnrichmentMap 3.0.

Add Ranks Dialog¶

Used to load additional ranks files into an existing data set.

Legend Dialog¶

The legend dialog provides a legend for the current network visualization.

The legend dialog can be left open. It will update automatically when changes are made to the network visualization using the Style Section of the main panel.

Sections¶

Node Color¶

Enabled when there is one data set and chart data is set to none.
Shows the range for the node fill color gradient.

Node Shape¶

Shows the meaning of node shape.
Shows signature nodes after post analysis has been run.

Node Chart¶

Shows the meaning of the node charts.
Shows how the chart segments map to the data sets.

Node Chart Colors¶

Shows the color range for the chart segment color gradients.

Edge Color¶

Shows how edge color maps to data sets.

Creation Parameters¶

Click the Creation Parameters button at the bottom left of the dialog to open the Creation Parameters dialog. This dialog shows the parameters that were originally entered in the Create EnrichmentMap Dialog when the network was originally created.

Post Analysis¶

To open the Post Analysis dialog click the Add Signature Gene Sets… button on the main panel.

There are currently two types of Post Analysis Available: Known Signature and Signature Discovery. The contents of the panel will change depending on the type of analysis chosen. Known signature mode calculates post analysis edges for a small subset of known gene-sets. Signature discovery mode allows for filtering of large set of potential signatures to help uncover most likely sets.

The result of running Post Analysis is a new node for each signature gene set (yellow triangle) and edges from the signature gene set to each existing gene set when the similarity passes the cutoff test.

A new data set is added to the data set list on the Main panel. Signature data sets have a next to their name.

Known Signature¶

Post Analysis Type
- Known Signature: Calculates the overlap between gene-sets of the current Enrichment Map and all the gene sets contained in the provided signature file.
Gene Sets
- SigGMT: The gmt file with the signature-genesets. These will be compared against the gene-sets from the current Enrichment Map.
Edge Weight Parameters
- Choose a method for generating an edge between a signature-geneset and an enrichment geneset. Described in detail below.
Actions:
- Reset - clears input panel
- Close - closes input panel
- Add - takes all parameters in panel and performs the Post-Analysis

Signature Discovery¶

Post Analysis Type
- Signature Discovery: Calculates the overlap between gene-sets of the current Enrichment Map and the selected genesets.
Gene-Sets
- The gmt file with the signature-genesets.
- Filter: Genesets from the gmt file that do not pass the filter test will not be loaded.
- Load Gene-Sets: Press after the gmt file and filter have been chosen to load the signature-genesets.
Available Signature Genesets: Once the genesets have been loaded this box will contain a list of all the genesets in the SigGMT file (that passed the filter).
- To highlight more than one geneset at at time hold the Shift, Command or Ctrl keys while clicking with the mouse.
Selected Signature Genesets: The analysis will be performed with all genesets in this list. Use the down- and up-buttons to move highlighted genesets from one list to the other.
Edge Weight Parameters: Choose a method for generating an edge between a signature-geneset and an enrichment geneset. Described in detail below.
Actions:
- Reset - clears input panel
- Close - closes input panel
- Add - takes all parameters in panel and performs the Post-Analysis

Edge Weight Parameters¶

Test: Select the type of statistical test to use for edge width.
Cutoff: Edges with a similarity value lower than the cutoff will not be created.
Data Set: If the enrichment map contains multiple data sets choose the one to use here.
Notes:
- The results of the calculations will be available in the edge table after post analysis runs.
- The edge “interaction type” will be sig.
- The hypergeometric test is always calculated, even if it is not used for the cutoff. The results are made available in the edge table.
Available Tests
- Hypergeometric Test is the probability (p-value) to find an overlap of k or more genes between a signature geneset and an enrichment geneset by chance.
  
  with:
  
  k (successes in the sample) : size of the Overlap,
  
  n (size of the sample) : size of the Signature geneset
  
  m (total number of successes) : size of the Enrichment Geneset
  
  N (total number of elements) : size of the union of all Enrichment Genesets
  - Advanced Hypergeometric Universe: Allows to choose the value for N.
    - GMT: all the genes in the original gmt file, Expression Set: number of genes in the expression set,
    - Intersection: number of genes in the intersection of the gmt file and expression set,
    - User Defined: manually enter a value).

Overlap has at least X genes

The number of genes in the overlap between the enrichment map gene set and the signature gene set must be at least X for the edge to be created.

Overlap is X percent of EM gs

The size of the overlap must be at least X percent of the size of the Enrichment Map gene set.

Overlap is X percent of Sig gs

The size of the overlap must be at least X percent of the size of the Signature gene set.

Mann-Whitney (Two-sided, one-sided greater, one-sided less)

Note: The Mann-Whitney test requires ranks. It will not be available if the enrichment map was created without ranks.

Calculates the p-value using the Mann-Whitney U test where the first sample is the ranks in the overlap and the second sample is all of the ranks in the expression set.

Edge Width Dialog¶

When you create an Enrichment Map network a visual style is created. The default edge width property is a continuous mapping to the similarity_coefficient column. After running post-analysis the rules for calculating edge width become more complicated. Edge width for edges between enrichment sets are still based on the similarity_coefficient column, but edges between signature sets and enrichment sets are based on the statistical test used for cutoff. Currently Cytoscape does not provide a visual mapping that is capable of “if-else” logic. In order to work around this limitation, the width of the edges is calculated by EnrichmentMap and put into a new column called EM1_edge_width_formula. Then the edge width property uses a continuous mapping to that column.

To open the dialog click the Set Signature Edge Width.. button in the style section of the main panel.

Edge Width Dialog
- Geneset Overlap: Set the end points of the continuous mapping for edge width for edges between enrichment sets.
- Signature Set: Set the edge width value for signature set edges that are less than cutoff/100, <= cutoff/10 and > cutoff/10.
- Click OK to recalculate the values in the “EM1_edge_width_formula” column.

File Formats¶

Gene sets file (GMT file)¶

Each row of the gene set file represents one gene set and consists of:

geneset name (--tab--) description (--tab--) a list of tab-delimited genes

The gene set names must be unique.
The gene set file describes the gene sets used for the analysis. These files can be obtained…
1. directly downloading our monthly updated gene-set collections from Baderlab genesets collections. Description of sources and methods used to create collection can be found on the Download Gene Set Files page.
2. directly downloading gene-sets collected in the MSigDB
3. converting gene annotations / pathways from public databases

Note

If you use MSigDB Gene Ontology gene-sets, please consider that they do not include all annotations, as an evidence code filter is applied; if you are interested in achieving maximum coverage, download the original annotations.

Note

if you are a R user, Bioconductor offers annotation packages such as GO.db, org.Hs.eg.db, KEGG.db

Expression Data file (GCT, TXT or RNK file) [OPTIONAL]¶

The expression data can be loaded in three different formats: gct (GSEA file type), rnk (GSEA file type) or txt.
The expression data serves two purposes:
- Expression data is used by the Heat Map when clicking on nodes and edges in the Enrichment Map.
- Gene sets can be filtered based on the genes present in the expression file. For example, if Geneset X contains genes {1,2,3,4,5} but the expression file only contain expression value for genes {1,2,3} then Geneset X will be represented as {1,2,3} in the Enrichment Map.
Expression data is not required. In the absence of an expression file EnrichmentMap will create a dummy expression file to associate with the data set. The dummy expression gives an expression value of 0.25 for all the genes associated with the enriched genesets in the Enrichment map.

GCT (GSEA file type)¶

GCT differs from TXT only because of two additional lines that are required at the top of the file.
The GCT file contains two additional lines at the top of the file.
- The first line contains #1.2.
- The second line contains the number of data rows (--tab--) the number of data columns
- The third line consists of column headings.
name (--tab--) description (--tab--) sample1 name (--tab--) sample2 name ...
- Each line of expression file contains a:
name (--tab--) description (--tab--) list of tab delimited expression values

Note

If the GCT file contains Probeset ID’s as primary keys (e.g. as you had GSEA collapse your data file to gene symbols) you need to convert the GCT file to use the same primary key as used in the gene sets file (GMT file). You have the following options:

Use the GSEA desktop application: GSEA > Tools > Collapse Dataset
Run this Python script collapse_ExpressionMatrix.py using the Chip platform file that was used by GSEA.

RNK (GSEA file type)¶

RNK file is completely different from the GCT or TXT file. It represents a ranked list of genes containing only gene name and a rank or score.
The first line contains column headings

For example: Gene Name (--tab--) Rank Name
Each line of RNK file contains:

name (--tab--) rank OR score

Additional Information on GSEA File Formats

TXT¶

Basic file representing expression values for an experiment.
The first line consists of column headings.

name (--tab--) description (--tab--) sample1 name (--tab--) sample2 name ...
Each line of the expression file contains:

name (--tab--) description (--tab--) list of tab delimited expression values

Enrichment Results Files¶

GSEA result files¶

For each analysis GSEA produces two output files. One representing the enriched genesets in phenotype A and the other representing the enriched genesets in phenotype B.
These files are usually named gsea_report_for_phenotypeA.Gsea.########.xls and gsea_report_for_phenotypeB.Gsea.########.xls
The files should be loaded in as is and require no pre-processing.
There is no need to worry about which Enrichment Results Text box to put the two files. The phenotype is specified by the sign of the ES score and is computed internally by the program.

Additional Information on GSEA File Formats

Generic results files¶

The generic results file is a tab delimited file with enriched gene-sets and their corresponding p-values (and optionally, FDR corrections)
The Generic Enrichment Results file needs:
- gene-set ID (must match the gene-set ID in the GMT file)
- gene-set name or description
- p-value
- FDR correction value
- Phenotype: +1 or -1, to identify enrichment in up- and down-regulation, or, more in general, in either of the two phenotypes being compared in the two-class analysis
  - +1 maps to red
  - -1 maps to blue
- gene list separated by commas

Note

Description and FDR columns can have empty or NA values, but the column and the column header must exist.

Note

If no value is provided under phenotype, Enrichment Map will assume there is only one phenotype, and will map enrichment p-values to red.

Examples of Generic Enrichment Result Files

DAVID Enrichment Result File¶

Available only in v1.0 or higher
The DAVID option expects a file as generated by the DAVID web interface.
When using DAVID as the analysis type there is no requirement to enter either a gmt file or an expression file. Both are options if the user wishes to add them to the analysis.
The DAVID Enrichment Result File is a file generated by the DAVID Functional Annotation Chart Report and consists of the following fields: Important: Make sure you are using CHART Report and NOT a Clustered Report.
- Category (DAVID category, i.e. Interpro, sp_pir_keywords, …)
- Term - Gene set name
- Count - number of genes associated with this gene set
- Percentage (gene associated with this gene set/total number of query genes)
- P-value - modified Fisher Exact P-value
- Genes - the list of genes from your query set that are annotated to this gene set.
- List Total - number of genes in your query list mapped to any gene set in this ontology
- Pop Hits - number of genes annotated to this gene set on the background list
- Pop Total - number of genes on the background list mapped to any gene set in this ontology.
- Fold enrichment
- Bonferroni
- Benjamini
- FDR

Warning

In the absence of a GMT gene sets are constructed based on the field Genes in the DAVID output. This only considers the genes entered in your query set and not the genes in your background set. This will drastically affect the amount of overlap you see in the resulting Enrichment Map.

BiNGO Enrichment Result File¶

The BiNGO option expects a file as generated by the BiNGO Cytsocape Plugin.
When using BiNGO as the analysis type there is no requirement to enter either a gmt file or an expression file. Both are options if the user wishes to add them to the analysis.
The BiNGO Enrichment Result File is a file generated by the BiNGO cytoscape plugin and consists of the following fields: Important: When running BiNGO make sure to check off “Check Box for saving data”
- The first 20 lines of BiNGO output file list parameters used for the analysis and are ignored by the Enrichment map plugin
- GO-ID - Gene set name
- p-value - hypergeometric or binomial Exact P-value
- corr p-value - corrected p-value
- x - number of genes in your query list mapped to this gene-set
- n - number of genes in the background list mapped to this gene-set
- X - number of genes annotated to this gene set on the background list
- N - number of genes on the background list mapped to any gene set in this ontology.
- Description - gene list description
- Genes - the list of genes from your query set that are annotated to this gene set.

Warning

In the absence of a gmt gene sets are constructed based on the field Genes in the BiNGO output. This only considers the genes entered in your query set and not the genes in your background set. This will drastically affect the amount of overlap you see in the resulting Enrichment Map.

RPT files¶

A special trick for GSEA results, in any GSEA analysis an rpt file is created that specifies the location of all files (including the gmt, gct, results files, phenotype specification, and rank files).
Any of the Fields under the dataset tab (Expression, Enrichment Results 1 or Enrichment Results 2) will accept an rpt file and populate GMT, Expression, Enrichment Results 1, Enrichment Results 2, Phenotypes, and Ranks the values for that dataset.
A second rpt file can be loaded for dataset 2. It will give you a warning if the GMT file specified is different than the one specified in dataset 1. You will have the choice to use the GMT for data set 1, data set 2 or abort the second rpt load.
An rpt file is a text file with following information (parameters surrounded by ” ‘ ‘ ‘” are those that EM uses):

'''producer_class'''    xtools.gsea.Gsea
'''producer_timestamp'''        1367261057110
param   collapse        false
param   '''cls'''       WHOLE_PATH_TO_FILE/EM_EstrogenMCF7_TestData/ES_NT.cls#ES24_versus_NT24
param   plot_top_x      20
param   norm    meandiv
param   save_rnd_lists  false
param   median  false
param   num     100
param   scoring_scheme  weighted
param   make_sets       true
param   mode    Max_probe
param   '''gmx'''       WHOLE_PATH_TO_FILE/EM_EstrogenMCF7_TestData/Human_GO_AllPathways_no_GO_iea_April_15_2013_symbol.gmt
param   gui     false
param   metric  Signal2Noise
param   '''rpt_label''' ES24vsNT24
param   help    false
param   order   descending
param   '''out'''       WHOLE_PATH_TO_FILE/EM_EstrogenMCF7_TestData
param   permute gene_set
param   rnd_type        no_balance
param   set_min 15
param   include_only_symbols    true
param   sort    real
param   rnd_seed        timestamp
param   nperm   1000
param   zip_report      false
param   set_max 500
param   '''res'''       WHOLE_PATH_TO_FILE/EM_EstrogenMCF7_TestData/MCF7_ExprMx_v2_names.gct

file    WHOLE_PATH_TO_FILE/EM_EstrogenMCF7_TestData/ES24vsNT24.Gsea.1367261057110/index.html

Parameters used by EM and their meaning:

producer_class - can be xtools.gsea.Gsea or xtools.gsea.GseaPreranked

if xtools.gsea.Gsea:

get expression file from res parameter in rpt

get phenotype information from cls parameter in rot
if xtools.gsea.GseaPreranked:
No expression file

use rnk as the expression file from rnk parameter in rot

set phenotypes to na_pos and na_neg.

NOTE: if you want to make using an rpt file easier for GSEAPreranked there are two additional parameters you can add to your rpt file manually that the rpt function will recognize.
To do less manual work while creating Enrichment Maps from pre-ranked GSEA, add the following optional parameters to your rpt file:
param(--tab--)phenotypes(--tab--){phenotype1}_versus_{phenotype2}
param(--tab--)expressionMatrix(--tab--){path_to_GCT_or_TXT_formated_expression_matrix}

producer_timestamp - needed to find the directory with the results files
cls - path to class/phenotype file with information regarding the phenotypes:

path/classfilename.cls#phenotype1_versus_phenotype2

EM get the path to the class file and also pulls the phenotype1 and phenotype2 from the above field

gmx - path to gmt file
rpt_label - name of analysis and name of directory that GSEA creates to hold the results. Used when constructing the path to the results directory.
out - path to directory where GSEA will put the output directory. Used when constructing the path to the results directory.
res - path to expression file.

rpt Searches for the following results files:

Enrichment File 1 --> {out}(--File.separator--){rpt_label} + "." + {producer_class} + "." + {producer_timestamp}(--File.separator--) "gsea_report_for_" + phenotype1 + "_" + timestamp + ".xls"
Enrichment File 2 --> {out}(--File.separator--){rpt_label} + "." + {producer_class} + "." + {producer_timestamp}(--File.separator--) "gsea_report_for_" + phenotype2 + "_" + timestamp + ".xls"
Ranks File --> {out}(--File.separator--){rpt_label} + "." + {producer_class} + "." + {producer_timestamp}(--File.separator--) "ranked_gene_list_" + phenotype1 + "_versus_" + phenotype2 +"_" + timestamp + ".xls";

If the enrichments and rank files are not found in the above path then EM replaces the out directory with the path to the given rpt file and tries again.
If you would like to create your own rpt file for your own analysis pipeline you can put your own values for the above used parameters.
If your analysis only creates one enrichment file you can make a copy of enrichment file 1 in the path of enrichment file 2 with no consequences for EM running.

EDB File (GSEA file type)¶

Contained in the GSEA results folder is an edb folder. In the edb folder there are the following files:
- results.edb
- gene_sets.gmt
- classfile.cls [Only in a GSEA analysis. Not in a GSEAPreranked analysis]
- rankfile.rnk
If you specify the results.edb file in any of the Fields under the dataset tab (Expression, Enrichment Results 1 or Enrichment Results 2) the gmt and enrichment files fields will be automatically populated.
If you want to associate an expression file with the analysis it needs to be loaded manually as described here.

Note

The gene_sets.gmt file contained in the edb directory is filtered according to the expression file. If you are doing a two dataset analysis where the expression files are from different platforms or contain different sets of genes the edb gene_sets.gmt file can not be used as genes found in one analysis might be lacking in the other. In this case use the original gmt file (prior to GSEA filtering) and EM will filter each the gene sets separately according to each dataset.

Additional Files¶

For each dataset there are additional parameters that the user can set but are not required. The advanced parameters include:
- Ranks file - file specifying the ranks of the genes in the analysis
  - This file has the format specified in the above section - gene (–tab–) rank or score. See RNK (GSEA file type) for details.
- Phenotypes (phenotype1 versus phenotype2)
  - By default the phenotypes are set to Up and Down but in the advanced setting mode the user can change these to any desired text.
All of these fields are populated when the user loads the input files using the rpt option.

Examples of Generic Enrichment Result Files¶

Note

For readability the following examples have been formatted in a way, that the content of each column is properly aligned. In the actual files, replace each {tab} and it’s surrounding SPACE-characters by one TAB-character. The files can be also easily created with any spreadsheet-program (e.g. Excel) and then saved in the “Tab Delimited Text” format.

Example with all possible columns

GO.ID      {tab} Description                     {tab} p.Val {tab} FDR  {tab} Phenotype
GO:0000346 {tab} transcription export complex    {tab} 0.01  {tab} 0.02 {tab} +1
GO:0030904 {tab} retromer complex                {tab} 0.05  {tab} 0.10 {tab} +1
GO:0008623 {tab} chromatin accessibility complex {tab} 0.05  {tab} 0.12 {tab} -1
GO:0046540 {tab} tri-snRNP complex               {tab} 0.01  {tab} 0.03 {tab} -1
...

Example without phenotype column

GO.ID      {tab} Description                     {tab} p.Val {tab} FDR
GO:0000346 {tab} transcription export complex    {tab} 0.01  {tab} 0.02
GO:0030904 {tab} retromer complex                {tab} 0.05  {tab} 0.10
GO:0008623 {tab} chromatin accessibility complex {tab} 0.05  {tab} 0.12
GO:0046540 {tab} tri-snRNP complex               {tab} 0.01  {tab} 0.03
...

Example without FDR and phenotype

GO.ID      {tab} Description                     {tab} p.Val
GO:0000346 {tab} transcription export complex    {tab} 0.01
GO:0030904 {tab} retromer complex                {tab} 0.05
GO:0008623 {tab} chromatin accessibility complex {tab} 0.05
GO:0046540 {tab} tri-snRNP complex               {tab} 0.01
...

Example without FDR but with phenotype

GO.ID      {tab} Description                     {tab} p.Val {tab} {tab} Phenotype
GO:0000346 {tab} transcription export complex    {tab} 0.01  {tab} {tab} +1
GO:0030904 {tab} retromer complex                {tab} 0.05  {tab} {tab} +1
GO:0008623 {tab} chromatin accessibility complex {tab} 0.05  {tab} {tab} -1
GO:0046540 {tab} tri-snRNP complex               {tab} 0.01  {tab} {tab} -1
...

Example without Description, FDR and phenotype

GO.ID      {tab} {tab} p.Val {tab} {tab} Phenotype
GO:0000346 {tab} {tab} 0.01  {tab} {tab} +1
GO:0030904 {tab} {tab} 0.05  {tab} {tab} +1
GO:0008623 {tab} {tab} 0.05  {tab} {tab} -1
GO:0046540 {tab} {tab} 0.01  {tab} {tab} -1
...

Example with dummy-description and without FDR and phenotype

GO.ID      {tab} DESCR {tab} p.Val {tab} {tab} Phenotype
GO:0000346 {tab} NA    {tab} 0.01  {tab} {tab} +1
GO:0030904 {tab} NA    {tab} 0.05  {tab} {tab} +1
GO:0008623 {tab} NA    {tab} 0.05  {tab} {tab} -1
GO:0046540 {tab} NA    {tab} 0.01  {tab} {tab} -1
...

Tips on Parameter Choice¶

Node (Gene Set inclusion) Parameters¶

Node specific parameters filter the gene sets included in the enrichment map.
For a gene set to be included in the enrichment map it needs to pass both p-value and q-value thresholds.

P-value

All gene sets with a p-value with the specified threshold or below are included in the map.

FDR Q-value

All gene sets with a q-value with the specified threshold or below are included in the map.
Depending on the type of analysis the FDR Q-value used for filtering genesets by EM is different
- For GSEA the FDR Q-value used is 8th column in the gsea_results file and is called “FDR q-val”.
- For Generic the FDR Q-value used is 4th column in the generic results file.
- For David the FDR Q-value used is 12th column in the david results file and is called “Benjamini”.
- For Bingo the FDR Q-value used is 3rd column in the Bingo results file and is called “core p-value”

Edge (Gene Set relationship) Parameters¶

An edge represents the degree of gene overlap that exists between two gene sets, A and B.
Edge specific parameters control the number of edges that are created in the enrichment map.
Only one coefficient type can be chosen to filter the edges.

Jaccard Coefficient

Jaccard Coefficient = [size of (A intersect B)] / [size of (A union B)]

Overlap Coefficient

Overlap Coefficient = [size of (A intersect B)] / [size of (minimum( A , B))]

Combined Coefficient

the combined coefficient is a merged version of the jacquard and overlap coefficients.
the combined constant allows the user to modulate reciprocally the weights associated with the jacquard and overlap coefficients.
When k = 0.5 the combined coefficient is the average between the jacquard and overlap.

Combined Constant = k
Combined Coefficient = (k * Overlap) + ((1-k) * Jaccard)

Tips on Parameter Choice¶

P-value and FDR Thresholds

GSEA can be used with two different significance estimation settings: gene-set permutation and phenotype permutation. Gene-set permutation was used for Enrichment Map application examples.

Gene-set Permutation

Here are different sets of thresholds you may consider for gene-set permutation:

Very permissive:

p-value < 0.05

FDR < 0.25

Moderately permissive:

p-value < 0.01

FDR < 0.1

Moderately conservative:

p-value < 0.005

FDR < 0.075

Conservative:

p-value < 0.001

FDR < 0.05

For high quality, high coverage transcriptomic data, the number of enriched terms at the very conservative threshold is usually 100-250 when using gene-set permutation.

Phenotype Permutation

Recommended:

p-value < 0.05

FDR < 0.25

In general, we recommend to use permissive thresholds only if your having a hard time finding any enriched terms.

Jaccard vs. Overlap Coefficient

The Overlap Coefficient is recommended when relations are expected to occur between large-size and small-size gene-sets, as in the case of the Gene Ontology.
The Jaccard Coefficient is recommended in the opposite case.
When the gene-sets are about the same size, Jaccard is about the half of the Overlap Coefficient for gene-set pairs with a small intersection, whereas it is about the same as the Overlap Coefficient for gene-sets with large intersections.
When using the Overlap Coefficient and the generated map has several large gene-sets excessively connected to many other gene-sets, we recommend switching to the Jaccard Coefficient.

Overlap Thresholds

0.5 is moderately conservative, and is recommended for most of the analyses.
0.3 is permissive, and might result in a messier map.

Jaccard Thresholds

0.5 is very conservative
0.25 is moderately conservative

Download Gene Set Files¶

Annotation (gene set) sources are regularly updated as new information is discovered. BaderLab has set up an automated system to update our gene set collections so we are always using the most up-to-date annotations.

Gene Set Files can be downloaded from: Baderlab genesets collections

If you use these gene sets, please cite our Enrichment Map paper.

Note

(January 2016) With the latest build of pathways we have removed KEGG from the main compilation set of pathways. If you would like to include KEGG in your analysis the sets are located in the misc/ directory and can be appended to your gmt file.

Note

(April 2012) Genesets files from December 2011, January 2012, Februrary 2012, and March 2012 had an error in the up-propagation of GO. Up-propagation only followed the is-a relationship and did not follow the part-of relationship which translates into missing annotations. This primarily effects genesets in GO cellular compartment.

Summary¶

Enrichment Map Gene Sets are a set of Gene Set files in GMT format (compatible with GSEA) updated monthly from original source locations available with:

Entrez gene ids
UniProt accessions
Gene symbols

The GMT File format contains one Gene Set per line. Each line contains:

Name (tab) Description (tab) Gene (tab) Gene (tab) …
In our format:
- Name = Gene Set Name % Gene Set Source % Gene Set Source identifier
  - Example –> ATP-dependent protein binding%GO%GO:0043008 OR arginine biosynthesis IV%HUMANCYC%ARGININE-SYN4-PWY
- Description = Gene Set Name
  - Example –> ATP-dependent protein binding OR arginine biosynthesis IV
- Gene = identified by one of the three possible identifiers (Entrez gene id, UniProt accession or gene symbols)
- IMPORTANT NOTE: Originally we used the “|” to separate information in the Name field but we came across issues with this separator in GSEA so we changed to “%”. The “%” was used as of the December 2011 build.

In the main directory (current_release/Human/symbol) there are 5 primary files to choose from:

Human_GO_AllPathways_with_GO_iea_{Date}_{ID}.gmt

Contains gene sets from all 3 divisions of GO (biological process, molecular function, cellular component) including annotations that have evidence code IEA (inferred from electronic annotation), ND (no biological data available), and RCA (inferred from reviewed computational analysis) and all pathway resources.

Human_GO_AllPathways_no_GO_iea_{Date}_{ID}.gmt

Contains gene sets from all 3 divisions of GO (biological process, molecular function, cellular component) excluding annotations that have evidence code IEA (inferred from electronic annotation), ND (no biological data available), and RCA (inferred from reviewed computational analysis) and all pathway resources.

Human_GOBP_AllPathways_with_GO_iea_{Date}_{ID}.gmt

Contains only gene sets from GO biological process including annotations that have evidence code IEA (inferred from electronic annotation), ND (no biological data available), and RCA (inferred from reviewed computational analysis) and all pathway resources.

Human_GOBP_AllPathways_no_GO_iea_{Date}_{ID}.gmt (recommended file)

Contains only gene sets from GO biological process excluding annotations that have evidence code IEA (inferred from electronic annotation), ND (no biological data available), and RCA (inferred from reviewed computational analysis) and all pathway resources.

Human_AllPathways_{Date}_{ID}.gmt

Contains only gene sets from all pathways resources.

Current Stats¶

Human: http://download.baderlab.org/EM_Genesets/current_release/Human/Summary_Geneset_Counts.txt
Mouse: http://download.baderlab.org/EM_Genesets/current_release/Mouse/Summary_Geneset_Counts.txt

Sources¶

Human¶

Source	File Type	ID extracted	Frequency source is updated	Number of pathways	File Origin
KEGG	GMT	Symbol	static as of July 1, 2011	236	KEGG ftp site (July 2011)
MSigDB - C2	GMT	Entrez gene	sporadically	Biocarta - 217, Other - 47	manual download
NCI	BioPAX	Entrez gene	sporadically	219 pathways	scripted download of zipped release
Institute of Bioinformatics (IOB)	BioPAX	Entrez gene	sporadically	35 pathways - 10 are the same as CellMap, 1 is the same as NetPath	received directly from IOB - static (July 2011)
NetPath (IOB)	BioPAX	Entrez gene	static	25 pathways - 12 are cancer pathways (10 are CellMap), 13 are immunity pathways	scripted download of files numbered 1-25
HumanCyc	BioPAX	UniProt	updated periodically	249 Pathways	scripted download of zipped release
Reactome	BioPAX	UniProt	updated release	1117 pathways (release 37)	scripted download of zipped release
GO	GAF	Uniprot	released once a month	13034 no GO IEA, 15181 with GO IEA	scripted download from EBI ftp site
MSigDB - C3	GMT	Entrez gene	sporadically	221 miRs, 616 TFs	manual download
Panther	BioPAX	UniProt	updated periodically	307 Pathways	scripted download of biopax archive

Mouse¶

Source	File Type	ID extracted	Frequency source is updated	Number of pathways	File Origin
Reactome	BioPAX	UniProt	updated release	946 pathways (release 37)	scripted download of zipped release
GO	GAF	MGI	released once a month	14563 no GO IEA, 15041 with GO IEA	scripted download from MGI ftp site
KEGG	GMT	Entrez gene	static as of July 1, 2011	236	translated from Human using Homologene
MSigDB - C2	GMT	Entrez gene	sporadically	total 880: Kegg - 186, Reactome - 430, Biocarta - 217, Other - 47	translated from Human using Homologene
NCI	GMT	Entrez gene	sporadically	219 pathways	translated from Human using Homologene
Institute of Bioinformatics (IOB)	GMT	Entrez gene	sporadically	35 pathways - 10 are the same as CellMap, 1 is the same as NetPath	translated from Human using Homologene
NetPath (IOB)	GMT	Entrez gene	static	25 pathways - 12 are cancer pathways (10 are CellMap), 13 are immunity pathways	translated from Human using Homologene
HumanCyc	GMT	Entrez gene	updated periodically	249 Pathways	translated from Human using Homologene
Panther	BioPAX	UniProt	updated periodically	307 Pathways	translated from Human using Homologene

Specialty Gene Sets¶

The bulk of our genesets are groupings from similar biological processes, pathways and functional annotations but there are a few additional collections of sets that we don’t group with them. They include:

miRs

Sets consisting of all the targets for a given microRNA.
miR genesets are retrieved from Msigdb c3 collection.

Transcription Factors

Sets consisting of all the targets for a given transcription factor.
TF genesets are retrieved from Msigdb c3 collection.

Disease Phenotype

Sets consisting of all known proteins associated with the given disease.
Disease phenotype genesets are retrieved from the Human phenotype ontology. Genes associated with a particular disease are annotated to it. In addition, in the same style as the Gene Ontology, the relationship between each disease is stored creating an ontology of diseases. Annotations are up-propagated to related disease terms.

Drugs Targets

Sets consisting of all the known or predicted targets for a given drug.
Drug target information is retrieved from drugbank. Drugbank is a resource containing 6711 drug entries including 1447 FDA-approved small molecule drugs, 131 FDA-approved biotech (protein/peptide) drugs, 85 nutraceuticals and 5080 experimental drugs. In addition to the compilation of all drugs contained in drugbank geneset files are also created for each of the defined drug categories including approved, experimental, illicit, nutraceutical, and small molecule.

File Structure¶

< > denotes directory

<Release> - directory is named according to date sets were updated.
- <Species>
  - <Identifier> - (either Entrez gene, UniProt, Gene symbol)
    - <GO>
      - BP = biological process
      - MF = molecular function
      - CC = Cellular component
      - All = BP + MF + CC
      - no_GO_IEA - indicates that the file excludes GO annotations with evidence codes - ‘IEA’ (inferred from electronic annotation), ‘ND’ (No biological data available), ‘RCA’ (inferred from reviewed computational analysis)
      - with_GO_IEA - indicates that the file includes GO annotations with evidence codes - ‘IEA’ (inferred from electronic annotation), ‘ND’ (No biological data available), ‘RCA’ (inferred from reviewed computational analysis)
    - <Pathways>
    - <miRs>
    - <TF>
    - <Disease phenotypes>
In each <identifier> directory There are amalgamated Gene Set files:
- AllPathways - contains all pathway sources in the Pathways directory
- GOPathways - contains all GO (MF, BP, CC) and all Pathway sources in the Pathways directory.

Creating customized Gene Sets¶

Download the desired gene set files you would like to use in your customized set and concatenate the files.

For example, to combine Human_IOB_Entrezgene.gmt Human_NetPath_Entrezgene.gmt, you can use the following linux command:

cat Human_IOB_Entrezgene.gmt Human_NetPath_Entrezgene.gmt > MyCustomizedSet.gmt

References¶

Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M.

KEGG for integration and interpretation of large-scale molecular data sets.

Nucleic Acids Res. 2011 Nov 10. PMID: 22080510

Pubmed.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP.

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. PMID: 16199517

Pubmed.
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH.

PID: the Pathway Interaction Database.

Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9. PMID: 18832364

Pubmed.
Kandasamy K, et al

NetPath: a public resource of curated signal transduction pathways.

Genome Biol. 2010 Jan 12;11(1):R3. PMID: 20067622

Pubmed.
Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD.

Computational prediction of human metabolic pathways from the complete human genome.

Genome Biol. 2005;6(1):R2. Epub 2004 Dec 22. PMID: 15642094

Pubmed.
Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D’Eustachio P, Stein L.

Reactome: a database of reactions, pathways and biological processes

Nucleic Acids Res. 2011 Jan;39(Database issue):D691-7. PMID: 21067998

Pubmed.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

Nat Genet. 2000 May;25(1):25-9. PMID: 10802651

Pubmed.
Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S, Guo N, Muruganujan A, Doremieux O, Campbell MJ, Kitano H, Thomas PD.

The PANTHER database of protein families, subfamilies, functions and pathways.

Nucleic Acids Res. 2005 Jan 1;33(Database issue):D284-8. PubMed PMID: 15608197

Pubmed.

Automating EnrichmentMap¶

EnrichmentMap provides several commands which allow basic features to be automated via scripts, the command line or REST.

CyREST App¶

To call commands via REST the CyREST App is required. CyREST is normally installed by default.

CyRest is updated often. There are two ways to install the latest version:

App Manager

Open the App Manager dialog from the main menu at Apps > App Manager. Then go to the Check For Updates tab. If there is a newer version available it will be listed.
App Store

CyRest can also be installed or updated from the App Store Website

Command Documentation¶

CyREST Documentation¶

On-line documentation for EnrichmentMap commands can be accessed from the main menu at Help > Automation > CyREST Command API. This will open a web browser with documentation for all the commands that are available through CyREST. Navigate to the enrichmentmap entry and expand it for detailed documentation on each command.

Note

For more details on using CyRest see the CyREST Documentation

Command Line Dialog Documentation¶

Open the Command Dialog from the main menu at Tools > Command Line Dialog.

Type help enrichmentmap to list the available commands provided by EnrichmentMap. To get help on a particular command type, for example the build command, type help enrichmentmap build.

Note

For more details on the Command Line Dialog see the Cytoscape User Docs

Properties¶

EnrichmentMap has some semi-hidden properties than can be used to customize the behavior of the App.

To manually edit these properties go to the Cytoscape main menu and select Edit > Preferences > Properties…. Then select org.baderlab.enrichmentmap in the Cytoscape Preferences Editor.

Supported properties:

heatmapAutofocus

If true then selecting a node/edge in the network will automatically bring the Heat Map panel to the front.
This property can also be changed from the Heat Map using the Panel Menu.
Default Value: false
Allowed Values: true, false

default.distanceMetric

Specifies the how the Hierarchical Clustering algorithm calculates distance.
This property can also be changed from the Heat Map using the Panel Menu.
Default Value: PEARSON
Allowed Values: PEARSON, EUCLIDEAN, COSINE

create.warn

When creating a network with the Create EnrichmentMap Dialog sometimes a validation warning dialog will be shown if there are any issues that need to be brought to the user’s attention. If this property is set to false then the warning dialog will never be shown even if there are issues. Note: The dialog will still be shown if there are errors that prevent the network from being created.
Default Value: true
Allowed Values: true, false

default.pvalue

Default p-value in the Create EnrichmentMap Dialog
Default Value: 1.0
Allowed Values: > 0.0, < 1.0

default.qvalue

Default q-value in the Create EnrichmentMap Dialog
Default Value: 0.1
Allowed Values: > 0.0, < 1.0

collapse_ExpressionMatrix.py¶

Download collapse_ExpressionMatrix.py.

This tool can process a gene expression matrix (in GCT or TXT format) ranked list (RNK format) and:

convert the Identifier based on a Chip Annotation file (e.g. AffyID -> Gene Symbol)
collapse the expression values or rank-scores for Genes from more than one probe set.

Converting and collapsing can be done either individually or both at the same time.

In case you are collapsing a ranked list (RNK format) to perform a “preRanked GSEA” that you later on want to analyze with EnrichmentMap and want to see an expression heatmap for the genesets, you need to generate an expression matrix that contains the expression values from the same probesets that were chosen to represent the gene in the ranked list. This can by selecting the ranked List (RNK) as the primary input file (-i) and the expression Matrix (GCT or TXT) as additional input Expression-table (-e). When using the GUI this can be done by selecting the mode “Ranked List with Expression Matrix”.

In this use-case ID-conversion and collapsing have to be done in the same step. The DESCRIPTION column of the collapsed expression matrix will for every given gene then contain the Probeset-ID of the Probeset with the highest absolute Score in the RNK file and in brackets followed by a list of Probeset-IDs that where omitted due to lower absolute rank-scores.

The option ‘Suppress gene “NULL”’ (–null) will drop all Probeset ID’s assigned to the Gene Symbol NULL, as this is used for probesets that are not linked to any Gene in several Chip-Annotation files available from the Broad Institute’s FTP server. (These will be dropped by GSEA anyway).

Requirements¶

Python 2.3 or newer (but not Python 3.x!)
the Tkinter Library (comes with most Python installations) for the GUI

Supported Operating Systems:

MacOS X 10.5 “Leopard” ore newer (probably also MacOS X 10.4 “Tiger”)
Windows (download and install the most recent version of Python 2.x from: http://www.python.org/download/ or http://www.activestate.com/activepython/downloads/
Linux (Python and Tcl/Tk are probably already installed out of the box, otherwise install the packages with your Distribution’s package manager)

GUI Mode¶

_images/collapse_ExpressionMatrix_GUI.png

collapse_ExpressionMatrix.py now has a Tk-based Graphical User Interface (GUI). To use the GUI, just start the program without any arguments. This can be done:

on Windows: double-click on the collapse_ExpressionMatrix.py-file
on MacOS 10.5 or newer with installed “Developer Tools”:
- Control-click (or right-click) on the collapse_ExpressionMatrix.py-file in the finder and choose “Open With/Build Applet.app”
- This will create an MacOS Application collapse_ExpressionMatrix.app which can be started by double clicking.
on MacOS, Linux or other Unix-like Systems in a Terminal/Shell: see in section “Command Line Mode” how to make the program executable.

After starting the GUI:

for collapsing either an expression matrix or Ranked gene list:
1. select mode “Expression Matrix or Ranked List”
2. use the first Browse-Button to select an Expression Matrix or Ranked gene list as an input file.
3. use the second Browse-Button to choose a name and location of the output file (the program will suggest to use the same name as the input file with an inserted “_collapsed” before the extension)
4. choose if the Identifiers should be converted or the file should be collapsed by checking the check-boxes
5. choose if the Gene Symbol “NULL” should be dropped
6. if Identifiers are to be converted, choose a matching chip file
7. start the conversion by clicking the Run button
for collapsing a Ranked gene list and generating an expression matrix containing the same probesets:
1. select Mode “Ranked List with Expression Matrix”
2. use the first Browse-Button to select the Ranked gene list as an input file.
3. use the second Browse-Button to choose a name and location of the Ranked gene list output file (the program will suggest to use the same name as the input file with an inserted “_collapsed” before the extension)
4. choose if the Gene Symbol “NULL” should be dropped
5. use the third Browse-Button to select an Expression Matrix input file
6. use the fourth Browse-Button to choose a name and location of the Expression Matrix output file
7. choose a matching chip file
8. start the conversion by clicking the Run button

Command Line Mode¶

If you are familiar with command line tools under Unix/Linux, collapse_ExpressionMatrix.py -h gives you all the information you need (if not, see below):

$ collapse_ExpressionMatrix.py -h
Usage: collapse_ExpressionMatrix.py [options] -i input.gct -o output.gct [-c platform.chip] [--collapse]

This tool can process a gene expression matrix (in GCT or TXT format) or
ranked list (RNK format) and either replace the Identifier based on a Chip
Annotation file (e.g. AffyID -> Gene Symbol), or collapse the expression
values or rank-scores for Genes from more than one probe set. Both can be done
in one step by using both '-c platform.chip' and '--collapse' at the same
time. If a ranked list is to be collapsed, an additional expression matrix can
be supplied by the -e/-x parameters and will be filtered to contain the same
probe-sets as selected from the RNK file. If however the file supplied by -i
is not recognized as a RNK file, these options have no effect.  For detailed
descriptions of the file formats, please refer to:
http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats
Call without any parameters to select the files and options with a GUI
(Graphical User Interface)

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -i FILE, --input=FILE
                        input expression table or ranked list
  -o FILE, --output=FILE
                        output expression table or ranked list
  -c FILE, --chip=FILE  Chip File This implies that the Identifiers are to be
                        replaced.
  -e FILE, --ei=FILE    (optional) additional input Expression-table, to be
                        restricted to the same probe-sets as the RNK file
  -x FILE, --xo=FILE    (optional) corresponding output file for -i/--ei
                        option
  --collapse            Collapse multiple probe sets for the same gene symbol
                        (max_probe)
  --no-collapse         Don't collapse multiple probesets [default]
  --null                suppress Gene with Symbol NULL
  -g, --gui             Open a Window to choose the files and options.
  -q, --quiet           be quiet

MacOS and Linux

On MacOS and Linux you need to make the program executable. Therefore:

copy the file to a directory, e.g. ${HOME}/bin
open a Terminal
set the executable flag:

chmod a+x ${HOME}/bin/collapse_ExpressionMatrix.py
if the ${HOME}/bin directory is not in your search Path (test by running collapse_ExpressionMatrix.py from a terminal) add it by adding the line export PATH=$HOME/bin:$PATH to your ${HOME}/.bash_profile using your favourite text editor (pico, vi, emacs, gedit, TextWrangler, etc.) or with the command

echo export PATH=\$HOME/bin:\$PATH >> ${HOME}/.bash_profile

or refer to your local SysAdmin for any other shell that bash.
open a new terminal or run source ${HOME}/.bash_profile
test with collapse_ExpressionMatrix.py -h

Windows

copy the file to a directory, e.g. C:\bin
open the Conrtol Panel
open System
go to Advanced System Settings (on Vista and 7 only)
go to the Advanced Tab
Click on Environment-button
if in the section “User variables for {USERNAME}” there is already an entry called “PATH”:
- click on Edit…
- append ;C:\bin at the very end
otherwise click on New…
- Variable Name: PATH
- Variable Value: %PATH%;C:\bin
open a Command Prompt (Programs/Accessories)
test with collapse_ExpressionMatrix.py -h

EnrichmentMap Cytoscape App 3.0¶

Feature Requests and Reporting Bugs¶

Cite EnrichmentMap¶

Examples of Use¶

Papers Citing Enrichment Map¶

Installation¶

What’s New¶

EnrichmentMap 3.0¶

Quick Tour¶

Creating the Network¶

Panels¶

Interpreting the Network¶

Main EnrichmentMap Panel¶

Legend Dialog¶

Expression Panel (Heat Map)¶

Post Analysis (Add Signature Gene Sets)¶

EnrichmentMap Protocol¶

Creating a Network¶

Create EnrichmentMap Dialog¶

1) The Data Set List¶

Creating Data Sets by Scanning For Files¶

Creating Data Sets Manually¶

2) File Entry Panel¶

Common Files¶

3) Gene Set (Node) Filtering¶

Basic Options¶

Advanced Options¶

4) Gene Set Similarity (Edge) Filtering¶

Basic Options¶

Advanced Options¶

5) Action Buttons¶

Network Visualization¶

Style¶

Visual Properties¶

Node Visual Properties¶

Visual properties¶

Edge Visual Properties¶

Visual properties¶

Chart Visualization¶

Columns¶

Node Columns¶

Edge Columns¶

Main Panel¶

Toolbar¶

Panel Menu¶

Filter Section¶

Style Section¶

Expression Panel (Heat Map)¶

Toolbar¶

Table¶

GSEA Leading Edge¶

Panel Menu¶

Add Ranks Dialog¶

Legend Dialog¶

Sections¶

Node Color¶

Node Shape¶

Node Chart¶

Node Chart Colors¶

Edge Color¶

Creation Parameters¶

Post Analysis¶

Known Signature¶

Signature Discovery¶

Edge Weight Parameters¶

Edge Width Dialog¶

File Formats¶

Gene sets file (GMT file)¶

Expression Data file (GCT, TXT or RNK file) [OPTIONAL]¶

GCT (GSEA file type)¶

RNK (GSEA file type)¶

TXT¶

Enrichment Results Files¶

GSEA result files¶

Generic results files¶

DAVID Enrichment Result File¶

BiNGO Enrichment Result File¶

RPT files¶

EDB File (GSEA file type)¶

Additional Files¶