GASN: gamma distribution test for driver genes identification based on similarity networks

Figures & data

Figure 1. The workflow of GASN is divided into 6 steps. Specifically, (a) is used to predict the genetic characteristics of gene mutation function influence score and the observed FIS, which are fused as the feature vector for constructing a similarity network. (b) Taking gene $g_i$ as an example, the similarity between $g_i$ and other genes is calculated. (c) The top k-nearest neighbor genes $g_{sk}$ in the similarity ranking of each gene are selected to form the topology. (d) Taking gene $g_i$ as an example, the gene similarity network is constructed, and its order is $g_i,g_{s1},g_i,g_{s2},\dots ,g_i,g_{sk}$, the similarity network of all genes is used as the input of convolutional neural network. (e) It shows the 5-layer basic structure of the convolutional neural network used in this paper, including the input layer, convolution layer, pooling layer, full connection layer, and output layer. (f) The gene background distribution is fitted by gamma distribution in the subclass, and the observed FIS is compared with the predicted FIS in the background distribution, to obtain the p value of each gene and select the gene with significant deviation as the driver gene.

Figure 2. Histogram of gene background distribution in 9 cancer types.

Table 1. 12 genetic characteristics of multimers data sources.

No.	Genetic feature
1	Expression level
2	Methylation status
3	DNA replication timing
4	Chromatin compartment (HiC) (Acemel et al., 2016)
5	Genomic copy number variation (CNA) (Gu et al., 2020)
6	The length of genomic regions (Wendl et al., 2011)
7	The hubness in a gene expression network (Khan et al., 2001)
8	The constraint score of non-synonymous mutation (Wyckoff et al., 2005)
9	The regulatory role of gene based on gene annotation
	databases (Huntley et al., 2009)
10	The total number of mutations in patients
11	The standard deviation of the patient's FIS
12	The number of deleterious mutation in patients

Table 2. Performance indexes of gene similarity network convolution neural network under different k values.

		K value of similarity network
Cancer		0	4	5	6	7	8	9	10
LUAD	$R^2$	0.9997	0.9997	0.9997	$\mathbf{0.9998}$	0.9998	0.9998	0.9998	0.9998
	RMSE	0.6099	0.5298	0.5332	$\mathbf{0.5239}$	0.5285	0.5251	0.5284	0.5241
LUSC	$R^2$	0.9995	0.9996	$\mathbf{0.9997}$	0.9996	0.9997	0.9997	0.9996	0.9996
	RMSE	0.5850	0.5064	$\mathbf{0.5011}$	0.5041	0.5030	0.5194	0.5096	0.5164
BRCA	$R^2$	0.9994	$\mathbf{0.9995}$	0.9994	0.9995	0.9995	0.9994	0.9994	0.9995
	RMSE	0.3618	$\mathbf{0.3583}$	0.3667	0.3638	0.3593	0.3683	0.3724	0.3623
HNSC	$R^2$	0.9972	0.9983	0.9986	$\mathbf{0.9990}$	0.9988	0.9978	0.9987	0.9982
	RMSE	0.7581	0.5859	0.5363	$\mathbf{0.4578}$	0.5048	0.6812	0.5239	0.6080
BLCA	$R^2$	0.9989	0.9989	$\mathbf{0.9989}$	0.9988	0.9989	0.9989	0.9988	0.9989
	RMSE	0.5478	0.5361	$\mathbf{0.5321}$	0.5641	0.5404	0.5350	0.5636	0.5370
KIRC	$R^2$	0.9997	0.9973	$\mathbf{0.9975}$	0.9968	0.9971	0.9971	0.9974	0.9973
	RMSE	1.3114	0.3131	$\mathbf{0.3015}$	0.3403	0.3266	0.3258	0.3077	0.3159
GBM	$R^2$	0.9991	0.9991	0.9991	0.9991	$\mathbf{0.9991}$	0.9991	0.9990	0.9991
	RMSE	0.3298	0.3274	0.3278	0.3247	$\mathbf{0.3260}$	0.3271	0.3296	0.3279
UCEC	$R^2$	0.9997	0.9998	0.9998	0.9998	0.9998	0.9998	$\mathbf{0.9998}$	0.9998
	RMSE	1.3069	1.2798	1.2739	1.2811	1.2856	1.2673	$\mathbf{1.2601}$	1.2706
OV	$R^2$	0.9997	0.9977	0.9977	0.9982	0.9992	$\mathbf{0.9992}$	0.9979	0.9969
	RMSE	0.5985	0.5931	0.5267	0.3548	0.3548	$\mathbf{0.3466}$	0.5632	0.6827
LAML	$R^2$	0.9957	0.9957	0.9960	0.9960	0.9959	$\mathbf{0.9960}$	0.9952	0.9956
	RMSE	0.1693	0.1723	0.1670	0.1670	0.1685	$\mathbf{0.1660}$	0.1816	0.1745

Figure 3. The number of NCG6.0 genes identified by different methods in 10 cancer types.

Figure 4. The precision of NCG6.0 genes identified by different methods in 10 cancer types.

Figure 5. The recall of NCG6.0 genes identified by different methods in 10 cancer types.

Figure 6. The number of CGC genes identified by different methods in 10 cancer types.

Figure 7. The precision of CGC genes identified by different methods in 10 cancer types.

Figure 8. The recall of CGC genes identified by different methods in 10 cancer types.

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