ISCB LA 2022

RIABIO 2022 Workshop: Analysis of biological and biomedical transcriptomic data using Artificial Intelligence (AI) and Machine Learning methods (ML) methods with R

Session 2: Coexpression networks

Maribel Hernandez Rosales, CINVESTAV, Mexico

Marisol Navarro Miranda, CINVESTAV, Mexico

igraph is a collection of network analysis tools.

Lets look at igraph funtions/tools.

setwd("/Users/solouli/Desktop/")

library(igraph)         # Network analysis library

We generate a random network of 15 undirected nodes. We set a seed for random numbers generation.

set.seed(25032022)
g <- sample_gnp(15, 0.2, directed = FALSE, loops = FALSE)
plot(g)

plot(g,
     vertex.color="darkorchid",  # nodes colors
     vertex.size=20,             # size of nodes
     edge.color="black")         # edges colors

We are going to use igraph functions to obtain some metrics:

Definition of the metrics can be foun in Network Science from Albert-László Barabási

1) Vertexes

V(g)

2) Edges

E(g)

3) Degree

degree(g)

4) Distance matrix, paths

distances(g)

5) Connected components

is_connected(g)
count_components(g)
components(g)

6) Shortest path

all_shortest_paths(g, 1, to = 5)
average.path.length(g, directed=FALSE, unconnected=TRUE)

7) Diameter

diameter(g)

8) Density

edge_density(g)

9) Betweenness centrality

betweenness(g)

Write our network as graphml

write.graph(g,"/Users/solouli/Desktop/yeast_protein_interaction.graphml", format="graphml")

Save our network as png

png("random_network.png", width = 300*10, height = 300*8,
    res = 300, units = "px")

set.seed(25032022)

plot(g,
     vertex.size=2*sqrt(V(g)$degree),
     vertex.color=V(g)$color,
     vertex.label=NA,
     layout=layout_nicely,
     edge.color="gray80")            # color de las aristas

dev.off()

> Study case: intronless genes

We are going to work in teams of 4 with a subsaet of intronless genes.

You can find the published paper here: Deciphering the Tissue-Specific Regulatory Role of Intronless Genes Across Cancers.

Lets look at igraph funtions/tools.

References

• Rsources

https://igraph.org/r/html/latest/ http://networksciencebook.com/chapter/1

https://kateto.net/2016/05/network-datasets/

https://kateto.net/wp-content/uploads/2016/01/NetSciX_2016_Workshop.pdf https://github.com/elaragon/R-igraph-Network-Workshop/blob/master/NetSciX%202016%20Workshop.R

• Data Bases

https://snap.stanford.edu/data/

https://networkrepository.com/

https://icon.colorado.edu/#!/networks

• Create a dataset

https://www.genecards.org/

https://string-db.org/cgi/input?sessionId=bvevnhF1MzII&input_page_show_search=on