README.md

Network analysis

This assignment consists of two tasks. The first one focuses on random walk with restart (RWR) and the second one on using the Laplacian matrix for clustering.

Random walk with restart

For this task, your goal is to find candidate genes associated with disease X using a guilt-by-association approach. To accomplish this, you will use a random walk with restart (RWR) algorithm implemented as a function in the rwr.R file. A undirected unweighted protein-protein interaction (PPI) network is also provided in the file ppi.tsv.gz. Each row in the file represents an edge and includes the two nodes corresponding to that edge.

You will use the RWR function and the PPI network to find top 10 candidate genes, besides PIK3R1, that are most likely to be causal for disease X. Remember that guilt-by-association methods assume that genes/proteins underlying the same phenotype tend to be interact, and this principle can be used to combine signals from individual genes. Therefore, a higher RWR score for a gene means that it is more likely to be functionally related to a "seed", a gene that is known to be involved in the disease.

For the first part of your exercise, you should use the RWR function and set the "PIK3R1" gene as the seed. Then, use the default values for gamma, tmax, and eps to find 10 genes most likely to be causal for disease X. Please submit the names and scores of these 10 genes once you have identified them.

For the next part of this task, you should vary gamma from 0.1 to 0.8 with a step size of 0.1 (seq(0.1,0.8,0.1)) and identify the top 10 candidates for each case. Pay attention to whether the top 10 genes change or remain the same and whether the order of the genes changes as you vary gamma. Do the top 10 genes or their order change with gamma? Please explain why you think that is?

Note that in the PPI network, an edge between nodes "A" and "B" implies an edge between nodes "B" and "A". To save space, ppi.tsv.gz only has one of the two edges.

Spectral clustering

Graph G, show below, includes 12 nodes.

For this undirected, unweighted graph $G$, define the adjacency matrix $A$. Use that to calculate the degree matrix $D$, and the Laplacian matrix $L$, where $L = D - A$. Similar to the example in class (Slide 41), decompose $L = U \Sigma U^{-1}$. Explain how you will cluster the nodes in $G$ using that decomposition. Submit the eigenvalues and eigenvectors of $L$ with the explanation or code of how you would use it to find clusters in the graph.