WikiSysop: Created page with "= Exercise: Network topology and statistics = '''Exercise written by: Lars Rønn Olsen''' Notes on today's exercise: today we will get a bit more advanced in our use of igraph. If you get stuck, remember that ''Google and chatGPT are your friends.'' == Part I. Getting started ==

 load("/home/projects/22140/exercise3.Rdata")

'''TASK: Create an igraph object with node attributes'''

 g <- graph_from_data_f..."

2024-03-05T14:12:48Z

Created page with "= Exercise: Network topology and statistics = '''Exercise written by: Lars Rønn Olsen''' Notes on today's exercise: today we will get a bit more advanced in our use of igraph. If you get stuck, remember that ''Google and chatGPT are your friends.'' == Part I. Getting started == <pre style="overflow:auto;"> load("/home/projects/22140/exercise3.Rdata") </pre> '''TASK: Create an igraph object with node attributes''' <pre style="overflow:auto;"> g <- graph_from_data_f..."

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= Exercise: Network topology and statistics =

'''Exercise written by: Lars Rønn Olsen'''

Notes on today's exercise: today we will get a bit more advanced in our use of igraph. If you get stuck, remember that ''Google and chatGPT are your friends.''

== Part I. Getting started ==

<pre style="overflow:auto;">
load("/home/projects/22140/exercise3.Rdata")
</pre>

'''TASK: Create an igraph object with node attributes'''

<pre style="overflow:auto;">
g <- graph_from_data_frame(d = interactions, directed = FALSE, vertices = node_attributes)
</pre>

== Part II. Network layout and Selecting nodes ==

'''TASK: Explore network layouts'''

Try two different layouts "kk" and "fr".

<pre style="overflow:auto;">
ggraph(g, layout = "fr") +
geom_edge_link() +
geom_node_point()

ggraph(g, layout = "kk") +
geom_edge_link() +
geom_node_point()
</pre>

'''TASK: Explore TP53 in the network'''

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 1''': How many proteins interact with TP53?

Color the nodes depending on whether they interact with TP53 or not, and change the shape of the TP53 node.

<pre style="overflow:auto;">
degree(graph = g, v = "prot_7157")
V(g)$TP53_neighbors <- names(V(g)) %in% names(neighbors(graph = g, v = "prot_7157"))
V(g)$TP53 <- names(V(g)) == "prot_7157"
</pre>

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 2''': Paste a screen shot of your TP53 annotated network into your report.

<pre style="overflow:auto;">
ggraph(g, layout = "fr") +
geom_edge_link() +
geom_node_point(aes(color = TP53_neighbors, shape = TP53))
</pre>

Now create a subnetwork consisting only of TP53 and its interaction partners, and label with gene names.

<pre style="overflow:auto;">
g_TP53 <- delete_vertices(graph = g, v = !names(V(g)) %in% names(neighbors(graph = g, v = "prot_7157")))
</pre>

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 3''': Paste a screen shot of your TP53 subnetwork into your report.

<pre style="overflow:auto;">
ggraph(g_TP53, layout = "fr") +
geom_edge_link() +
geom_node_point(aes(color = TP53_neighbors, shape = TP53)) +
geom_node_text(aes(label = Gene_Id), repel = TRUE)
</pre>

== Part III. Network statistics ==

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 4''': Calculate the following statistics for the full network:

* Average node degree
* Average clustering coefficient (also known as transitivity)

<pre style="overflow:auto;">
mean(degree(g))
transitivity(g)
</pre>

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 5''': Make a plot of the distribution of node degrees and paste this into your report

<pre style="overflow:auto;">
df <- data.frame(k = degree(g))
ggplot(df, aes(x = k)) +
geom_density() +
geom_vline(xintercept = length(neighbors(graph = g, v = "prot_7157")), color = "red")
</pre>

Now calculate the node-wise clustering coefficient. This is done with the transitivity() function, setting the variable type = "local".
[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 6''': Make a plot of the distribution of clustering coefficients and paste this into your report

<pre style="overflow:auto;">
df <- data.frame(c = transitivity(graph = g, type = "local"))
ggplot(df, aes(x = c)) +
geom_density() +
geom_vline(xintercept = transitivity(graph = g, type = "local", v = "prot_7157"), color = "red")
</pre>

Look at box 2 in the Barabasi paper. In order to determine whether a network is random, scale free or hierarchical, you need to produce two figures: log(k) vs log(P(k)) and log(k) vs log(C(k)). k is the degree of the nodes in the graph, P(k) is the probability of a given k, and C(k) is the average clustering coefficient of all nodes with a degree of k.

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 7''': Make log-log plots of k vs P(k) and k vs C(k) with a regression line and paste them into your report.

<pre style="overflow:auto;">
df <- data.frame(table(degree(graph = g))/sum(degree(graph = g)))
df$Var1 <- as.numeric(df$Var1)
colnames(df) <- c("k", "Pk")
ggplot(data.frame(df), aes(x = log(k), y = log(Pk))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)

df <- data.frame(k = degree(graph = g), c = transitivity(graph = g, type = "local"))
df2 <- NULL
for(i in unique(df$k)) {
df2 <- rbind(df2, c(i, mean(df[df$k==i,]$c, na.rm = TRUE)))
}
colnames(df2) <- c("k", "Ck")
ggplot(data.frame(df2), aes(x = log(k), y = log(Ck))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
</pre>

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 8''': Based on the distribution of the node degree and the clustering coefficients, the network structure appears to be random, scale free or hierarchical? (Remember that a hierarchical network is also scale free).

'''Appears to be hierarchical'''

== Part IV. Network connectivity ==

Calculate the network diameter (longest shortest path).

<pre style="overflow:auto;">
diameter(g)
</pre>

[[Image:Office-notes-line_drawing.png|30px|left]]
'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?

'''4'''

ExTopology1 igraph solutions - Revision history