A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph

Abstract

Background Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. Results Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. Conclusions The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. Availability The source code can be obtained at https://github.com/weiba/Subdyquency . Electronic supplementary material The online version of this article (10.1186/s12859-019-2847-9) contains supplementary material, which is available to authorized users.

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

“…Masica and Karchin present one of the early models based on such a strategy by employing statistical methods for setting up the correlation between mutated genes and the differentialy expressed genes to identify candidate drivers [1]. Many different models follow a similar trail by further incorporating biological pathway/network information for setting up such a correlation [6,19,20,21,22,23]. DriverNet is among the notable approaches employing mutations data in addition to gene expression and biological network data [19].…”

“…Many subsequent approaches are inspired by DriverNet [20,21,22,23]. Among them DawnRank [20], the algorithm by Shi et al [21], and Subdyquency [23] employ, on top of the overall DriverNet model, versions of heat diffusion on the networks integrating data in the form of biological interactions, mutations, and gene expression. Heat diffusion is a technique employed commonly in many cancer driver gene or gene module discovery algorithms [9,24,25,26,27,28].…”

“…We propose BetweenNet algorithm for cancer driver gene prioritization. Similar to the methods proposed in [20,21,22,23], BetweenNet is also inspired by the DriverNet framework in that it relates the mutated genes and the so-called outlier A bipartite graph B is constructed where genes that have a mutation in at least one patient appear on the top, and outlier genes on the bottom.…”

An O(n^3) time algorithm for the maximum weight b-matching problem on bipartite graphs

“…Given two sets of objects A and B, a matching matches each object of A (respectively B) to at least one object of B (respectively A). The matching has many uses including computational biology and pattern recognition [1][2][3]. We can represent the sets and their relations using a bipartite graph; for example one part can represent mutated gens and other part outlying gens [2].…”

“…The matching has many uses including computational biology and pattern recognition [1][2][3]. We can represent the sets and their relations using a bipartite graph; for example one part can represent mutated gens and other part outlying gens [2]. Given a weighted bipartite graph G = (A ∪ B, E), a matching in G is the set of the vertex disjoint edges M ⊆ E. The weight of the matching M which is denoted by W (M ) is the sum of the weights of all the edges in M , hence…”