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Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

Bottom Line: In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

ABSTRACT
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

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Optimization for elastic net in Step 1-2.
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alg1: Optimization for elastic net in Step 1-2.

Mentions: Note that zero weighted coefficient cannot be recovered back to nonzero in adaptive lasso of Step 3. Therefore, in order to carefully keep only SNPs that are more likely to be true eQTLs in fi, we give l1-norm penalty to only fi but not bi using elastic net defined as(8)arg min⁡bi,fi⁡//yi−biY−fiX//22+λ1//bi//22+λ2//fi//1.As the objective function is convex, which guarantees a convergence, fij can be optimized by using coordinate descent iteration given parameters, λ1 and λ2. To find the optimal fi, the derivative of (8) with respect to fij is considered as follows:(9)fiXXjT−yiXjT+biYXjT+λ2∂fij//fi//1.Since the derivative of (8) with respect to bi is the same as (5), bi in (9) is substituted with (5), and then (9) is simplified to(10)(fi(−j)X(−j)−yi)S2+fijxjS2−λ2∂fij//fi//1,where(11)S2=(YT(YYT+λ1I)−1Y−I)xjT;fi(−j) indicates row vector fi whose jth element is removed, X(−j) denotes matrix X whose jth row is removed, and xj is jth row vector of X. After defining Cj = (fi(−j)X(−j) − yi)S2 and aj = xjS2 in (10), the update rule in the coordinate descent algorithm is written as(12)fij={(−Cj−λ2)ajif  Cj<−λ2,0if  /Cj/≤λ2,(−Cj+λ2)ajif  Cj>λ2.Algorithm 1 describes the procedures to solve (8) in Step 2. If fij is nonzero, jth SNP is a candidate eQTL for ith gene.


Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

Optimization for elastic net in Step 1-2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127230&req=5

alg1: Optimization for elastic net in Step 1-2.
Mentions: Note that zero weighted coefficient cannot be recovered back to nonzero in adaptive lasso of Step 3. Therefore, in order to carefully keep only SNPs that are more likely to be true eQTLs in fi, we give l1-norm penalty to only fi but not bi using elastic net defined as(8)arg min⁡bi,fi⁡//yi−biY−fiX//22+λ1//bi//22+λ2//fi//1.As the objective function is convex, which guarantees a convergence, fij can be optimized by using coordinate descent iteration given parameters, λ1 and λ2. To find the optimal fi, the derivative of (8) with respect to fij is considered as follows:(9)fiXXjT−yiXjT+biYXjT+λ2∂fij//fi//1.Since the derivative of (8) with respect to bi is the same as (5), bi in (9) is substituted with (5), and then (9) is simplified to(10)(fi(−j)X(−j)−yi)S2+fijxjS2−λ2∂fij//fi//1,where(11)S2=(YT(YYT+λ1I)−1Y−I)xjT;fi(−j) indicates row vector fi whose jth element is removed, X(−j) denotes matrix X whose jth row is removed, and xj is jth row vector of X. After defining Cj = (fi(−j)X(−j) − yi)S2 and aj = xjS2 in (10), the update rule in the coordinate descent algorithm is written as(12)fij={(−Cj−λ2)ajif  Cj<−λ2,0if  /Cj/≤λ2,(−Cj+λ2)ajif  Cj>λ2.Algorithm 1 describes the procedures to solve (8) in Step 2. If fij is nonzero, jth SNP is a candidate eQTL for ith gene.

Bottom Line: In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

ABSTRACT
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

Show MeSH
Related in: MedlinePlus