General principle

The signet R package implements a method to detect selection in biological pathways. The general idea is to search for gene subnetworks within biological pathways that present unusual features, using a heuristic approach (simulated annealing).

The general idea is simple: we consider a gene list with prealably defined scores (e.g. a differentiation measure like the Fst) and we want to find gene networks presenting a score higher than expected under the null hypothesis.

To do so, we will use biological pathways databases converted as gene networks and search in these graphs for high-scoring subnetworks.

Details about the algorithm can be found in Gouy et al. (2017).

Citation

Please cite this paper if you use signet for your project:

Walkthrough example

Input

signet takes as input a data frame of gene scores. The first column must correspond to the gene ID (e.g. Entrez) and the second columns is the gene score (a single value per gene).

The other input is a list of biological pathways (gene networks) in the graphNEL format. We advise to use the package graphite to get the pathway data:

library(graphite)

# pathwayDatabases() #to have a look at pathways and species available
# get the pathway list:
paths <- graphite::pathways("hsapiens", "kegg")

# convert the first 3 pathways to graphs:
kegg_human <- lapply(paths[1:3], graphite::pathwayGraph)
head(kegg_human)

Note that gene identifiers must be the same between the gene scores data frame and the pathway list (e.g. entrez). graphite provides a function to convert gene identifiers.

A example dataset from Daub et al. (2013, MBE) as well as human KEGG pathways are provided:

library(signet)
data(daub13)
head(scores) # gene scores

##    gene     score
## 1     1 0.9200665
## 2    10 1.5974385
## 3   100 1.6885589
## 4  1000 3.3314333
## 5 10000 1.6668512
## 6 10001 1.3529425

Workflow

We first have to search for high-scoring subnetworks within the provided biological pathways, using simulated annealing:

# Run simulated annealing on the first 3 KEGG pathways:
HSS <- searchSubnet(kegg_human, scores)

This function returns, for each pathway, the highest-scoring subnetwork found, its score and other information about the simulated annealing run.

Then, to test the significance of the high-scoring subnetworks, we generate a null distribution of high-scores:

#Generate the empirical null distribution
null <- nullDist(kegg_human, scores, n = 1000)

Note that the null object is a simple vector of null high-scores (here, 1000). Therefore, you can run other iterations afterwards and concatenate the output with the previous vector if you want to compute more precise p-values.

This distribution is finally used to compute p-values and update the signet object:

HSS <- testSubnet(HSS, null)

Interpretation of the results

When p-values have been computed, you can generate a summary table (one row per pathway):

# Results: generate a summary table
tab <- summary(HSS)
head(tab)

##                           pathway net.size subnet.size     subnet.score
## 1          Acute myeloid leukemia       53           6 2.89287686851351
## 2               Adherens junction       66           8 3.10224504061292
## 3 Adipocytokine signaling pathway       62           8 3.27409311142754
##   p.val                             subnet.genes
## 1 0.104            207 3551 3815 4790 8503 10000
## 2 0.066 1499 1500 2241 5795 5879 6714 8826 81607
## 3 0.048  3551 3667 3717 3953 4790 5602 8660 9021

# you can write the summary table as follow:
# write.table(tab,
#             file = "signet_output.tsv",
#             sep = "\t",
#             quote = FALSE,
#             row.names = FALSE)

Note that searching for high-scoring subnetworks and generating the null distribution can take a few hours. However, these steps are easy to parallelize on a cluster as different iterations are independent from each other.

Plot the results using Cytoscape

Cytoscape (www.cytoscape.org) is an external software dedicated to network visualization. signet allows to generate an XGMML file to be loaded in Cytoscape (File > Import > Network > File…). This file can be written in your working directory thanks to the function writeXGMML.

  1. Plot a single pathway and its highest-scoring subnetwork

If the input of the function is a single signet object, the whole pathway will be represented and nodes belonging to the highest-scoring subnetwork (HSS) will be highlighted in red.

writeXGMML(HSS[[1]], filename = "cytoscape_input.xgmml")
  1. Merge all the significant subnetworks and plot the resulting graph

If a list of pathways (signetList) is provided, all subnetworks with a p-value below a given threshold (default: 0.01) are merged and represented. Note that in this case, only the nodes belonging to HSS are kept for representation.

writeXGMML(HSS, filename = "cytoscape_input.xgmml", threshold = 0.01)

The representation can then be finely customised in Cytoscape.