A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila

Abstract

Background: The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues.

Results: By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level.

Conclusion: By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.

Figure 1

The flowchart for the rank-based test. We put one hit into the nonhit set as if it were a nonhit (simulated FN). We then ranked all nonhits, including the simulated FN, using different scoring methods. Presumably, a good scoring system can rank the "FN" higher, while a bad scoring system cannot.

Figure 2

The overall performance of different methods in identifying FNs (a) and FPs (b) in the rank-based test and the screen-specific performance of different methods in identifying FNs (c) and FPs (d) in the rank-based test. The error bars represent the estimated standard deviations for the corresponding quantities. The DN, SP, DK and AT represent the four different network similarity measurements, i.e., direct neighbor, shortest path, diffusion kernel and association analysis-based transformation, respectively. Index 1, 2 and 3 represent the three different summarizing formulas, respectively (see Additional file 1 – Table S2 for details). GR represents the GeneRank algorithm.

Figure 3

The distributions of RR of FNs among nonhits (a) and RR of FPs among hits (b) for each screen in the rank-based test. The RR was computed by the best performing scoring method for that screen according to the rank-based test. The notation for each method is the same as in Figure 2.

Figure 4

The reproducibility rate of hits in the validation screen within each interval of the RR by NePhe score for Hh (a) and Wnt (b) signaling pathway.

Figure 5

The proportion of OT-related hits within each interval of the RR by NePhe score for Hh (a) and Wnt (b) signaling pathway.

Figure 6

The reproducibility rate for OT-related and OT-unrelated hits (a), for low-ranked and high-ranked OT-related hits by NePhe score (b) and for low-ranked and high-ranked OT-unrelated hits by NePhe scores (c) for Hh signaling pathway.

Figure 7

Distributions of the mutant phenotype similarities between nonhits within each interval of the RR by NePhe score and known regulators (blue bars), between hits and known regulators (orange bar) and among known regulators themselves (red bar) for Hh signaling pathway (a) and Wnt signaling pathway (b). Only genes with at least one allele phenotype are considered here.

Figure 8

A sub-network associated with the Wnt signaling pathway. Red: hits of RNAi screening. White: top-ranking nonhits by NePhe score. Green boundary: genes within KEGG Wnt signaling pathway. Square: genes supported by literature for their association with the Wnt pathway. Module I: canonical participants-related. Module II: transcription factor TFIID complex-related. Module III: PcG protein complex-related. Module IV: other signaling pathways-related. The visualization was generated using Cytoscape [57].