1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Negative Binomial Mixed Models for Identifying Oncogenic dependencies through analysis of RNAi Screening data
Paper ID : 1352-ICB10
Authors:
Zohreh Toghrayee *1, Sajjad Gharaghani2, Hesam Montazeri1
1University of Tehran
2مدیر گروه بیوانفورماتیک دانشگاه تهران
Abstract:
Background: Loss-of-function RNAi screening has been extensively used to identify cancer dependencies, including oncogene addiction. A few computational methods such as ATARiS, DEMETER, and RSA have been presented to compute gene-level scores by handling off-target effects in RNAi screening data. However, these methods often result in low statistical power in low sample-size settings. This paper presents a new statistical approach to tackle the off-target effects in order to provide higher statistical power compared to the mentioned methods.
Methods: We applied DRIVE project data and the CCLE data to detect gene drivers in pan-cancer and breast cancer, by thoroughly scrutinizing all shRNA data. In the proposed method, we first removed the effect of batches including pool and thermodynamic stability of shRNAs using an empirical Bayesian model available in the SVA package in R. Then negative binomial mixed effect models were performed on ranks of these logFC in each cell line.
Results: Among 6919 genes, known cancer genes such as KRAS, NRAS, BRAF, PIK3CA, CTNNB1, TP53, and CDK4 were reassuringly identified by the proposed method in Pan-cancer analysis. We demonstrated that the proposed approach outperformed ATARiS and DEMETER in terms of statistical power through sub-sampling approaches. In analyzing breast cancer data, we identified both putative oncogenes such as PAX5 and RASGRP2 and known oncogenes such as KRAS.
Conclusion: By using all information in RNAi screening data, the proposed method models on- and off-target effects and can identify oncogene addictions in cancer.
Keywords:
RNAi screening, Off-target effect, DRIVE Project, Batch effect removal
Status : Paper Accepted (Oral Presentation)