An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

Abstract

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network

“…Furthermore, the results before using CMI method called SP+DBN and after using CMI method called SP+DBN+CMI were tested. Considering the order dependency of CMI [43], we selected Order 0 and Order 2 for evaluation in the experiments. The proportion of candidate parent nodes was set as 20%.…”

“…Because the CMI method is used in the structure optimization step, choosing different orders will get different results of network [43]. In the verification of DBN-SP, Order 0 and Order 2 are selected.…”

A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

“…In ordering-based algorithms, the search for the network structure can be carried out in the space of orderings among the domain variables. K2 algorithm [7], order independent PC-based algorithm using quantile value (OIPCQ) [21], consensus network (CN) algorithm [2], and IPCA-CMI [1] belong to this type. In graph-based algorithms, the search for the network structure can be carried out in the space of DAGs.…”

An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information

“…Among them, Zhang et al (2015) developed conditional mutual inclusion information ( CMI2 ) to describe the regulatory relationship between genes, which involves calculating the information loss of a hypothetical distribution in the presence or absence of edges. Later, some studies have solved order dependency issue in the path consistency algorithm for inferring GRN , including SORDER ( Aghdam et al , 2015 ) and OIPCQ ( Mahmoodi et al , 2021 ).…”