Fig 1.
The algorithm of the AWGE-ESPCA model.
The steps are as follows: (A) Input Data: It includes sample data and gene pathway data. (B) Data Processing: It involves two core modules. First, Adaptive Noise Elimination Regularization is used to eliminate noise; then, Weighted Gene Network is constructed. (C) Result Output: It obtains gene expression values with different weights.
Fig 2.
Flow chart of the AWGE-ESPCA model.
Fig 2 shows the specific flow of the AWGE-ESPCA algorithm. First, randomly initialize and calculate u; then, identify
based on the modified regularizer and remove the data noise to get the new
; then, calculate the edge information based on the gene network and the weighted information; finally, retain the important genes and edge information based on the
and edge information and continue to loop through the process based on the current result.
Table 1.
The top two identified PC1 and PC2 loadings by ESPCA, DM-ESPCA and AWGE-ESPCA.
Fig 3.
Heatmaps comparing different methods for sample classification.
(A) the result of the AWGE-ESPCA model. (B) the result of the DM-ESPCA model. (C) the result of the AEs model. (D) the result of the VAEs model. (E) the result of the Lasso model. (F) the result of the Elastic Net model. The columns represent different categories, namely: Cu_0_FPKM, Cu_75_FPKM, Cu_150_FPKM. The rows are samples, and the colors in the heatmap represent the gene expression values.
Fig 4.
Sample distribution visualization and analysis results across different models.
(A) the score plots of the AWGE-ESPCA model. (B) the score plots of the DM-ESPCA model. (C) the score plots of the T-SNE model. (D) the score plots of the AEs model. (E) boxplots comparing gene expression levels under Cu_0_FPKM condition across different models. (F) The number of target pathway genes identified by each model.
Table 2.
The proportion of target pathway genes for Hermetia illucens experiment.
Fig 5.
Heatmaps comparing different methods for sample classification.
(A) the result of the AWGE-ESPCA model. (B) the result of the DM-ESPCA model. (C) the result of the AEs model. (D) the result of the VAEs model. (E) the result of the Lasso model. (F) the result of the Elastic Net model. The columns display two samples - P210 (P210_1, P210_2, P210_3) and T315 (T315_1, T315_2, T315_3). The rows display different genes.
Fig 6.
Principal component score plots and boxplots of various models.
(A) The score plot of the AWGE-ESPCA model. (B) The score plot of the DM-ESPCA model. (C) The score plot of the T-SNE model. (D) The score plot of the AEs model. (E) The boxplot of the p210_1. (F) The boxplot of the p210_2.
Table 3.
The percentage of target pathway genes for Drosophilamelanogaster dataset.
Table 4.
The proportion of target pathway genes for Ablation experiment.
Fig 7.
Principal Component Score Plot and the proportion of target pathway genes.
(A) the result of the AWGE-ESPCA model. (B) the result of the Non-Regularization model. (C) the result of the Non-Weighted model. (D) the number of target pathway genes.
Fig 8.
(A) the result of the AWGE-ESPCA model. (B) the result of the Non- Regularization model. (C) the result of the Non-Weighted model.
Fig 9.
Bio-enrichment analysis picture.
Bio-enrichment analysis revealing functional interactions across six MCODE modules, highlighting key pathways in cellular differentiation, neurogenesis, and morphogenesis with corresponding enrichment scores (p < 0.05).