This week, I got caught up with the state of the project. Anna and Miriam had cleaned up and optimized our code to the point where it could be submitted and run in a reasonable amount of time. However, there still remained the problem of nodes only connected to positives. Since positives are held at a constant score of 1.0, and negatives at a constant 0.0, any nodes only connected to a positive will have a score of 1.0, which misrepresents how a gene fits within the polygenic nature of schizophrenia and cell motility.
To solve this, we decided to make three networks, each one identical to the one we were previously testing. However, there are two key differences.
- The positives are divided between the three networks.
- The three nodes in each networks representing their gene are also connected to a prime node that is only connected to those three nodes. The prime node’s score is the node by which we judge a gene.
This reduces the edge cases where a gene is only connected to a positive or a negative, since the gene cannot be connected to only one node. This also allows us to evaluate positive nodes within the context of all other positives. If a positive node is found to be positive in other networks, then the score will be high. Even the neighbor positives within the same network will diffuse the positivity through their prime nodes down to other networks, increasing the score for the neighbor of the positive and therefore increasing the score for the positive.
One thing to note is that the AUC seems to be relatively stable despite the network threshold or the prime node implementation. However, my guess is that this is probably due to the positives generally being only vaguely functionally related. Also, only 116 SZ genes are in the 0.200 network, reducing the polygenic power of our method. One thing to try is to add more genes from other studies. I will research other studies to find more gene lists and p-values.