1. An improved metric for the comparison of RNAi knockout phenotypesXX

2. Background RNAi can effectively ‘knock out’ a gene
Large-scale studies systematically perform RNAi on many genes, identify phenotypes
Embryonic Lethal, Uncoordinated, Thin…

3. Background Phenotypes can be thought of as gene descriptors
Each gene has a binary vector, with each entry corresponding to a single phenotype
Classic information theory setup

4. Previous methods
Classic approach: given a collection of genes, “eye them up” for common phenotypes
Piano “Gene Clustering Based on RNAi Phenotypes of Ovary-Enriched Genes in C. elegans”
Gunsalus “RNAiD and PhenoBlast: web tools for genome-wide phenotypic mapping projects.”
Gunsalus “Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis”

6. Tested metrics PREVIOUS METRICS Pearson Correlation
Uncentered Pearson Correlation
Simple Match (1s)
Simple Match (1s and 0s)
NOVEL METRICS
“Scaled Match”
“Loss of function agreement score”
IDF AND RELATED
Inverse Document Frequency (IDF)
Frequency Dot Product (FDP)
Residual IDF
Scaled IDF
OTHER
CanB
Euclidean Distance
Hamming Distance
Jaccard Distance
Mutual Information
Rand Index

7. Precision/Recall

8. Network Degree Distributions

9. Shared Phenotypes per linked gene pair

10. Overview of subnetwork phenotypes

11. Number of enriched phenotypes per subnetwork

12. Subnetwork coverage of best GO category

13. Circularity Issues Go is basically built from knockout phenotypes
Makes it very hard to evaluate predictions on a large scale
19/35 phenotypes overlap a GO category by at least 50% (several overlap a few)
For example, 71 genes have the ‘Sluggish Movement’ (SLU) phenotype. Of these, 70 are in the ‘positive regulation of locomotion’ category, which itself is comprised of only 82 genes.

14. Future Work Smaller subnetworks (or clustering)
How well does the new phenotype data integrate with other functional data (co-expression, p2p, genetic, combination)?
Metric level
Network level
Triangle level
Subnetwork level
Look for interesting biology in 9 novel subnetworks