Table 1.
Shown are the number of patients, their age in years (with mean and standard deviation), sex, APOE genotype (binary encoding for at least one present E4 allele), MMSE score (with mean and sd) and Braak stage.
Fig 1.
The Integrative VAMBN (iVAMBN) approach.
The iVAMBN approach integrates gene expression data, clinical and patho-physiological (phenotype) measures (bottom left) into a joint quantitative, probabilistic graphical model. The method initially uses a knowledge graph (top left) for defining modules and for informing about potential connections between them. In a second step, a representation of each module using a Heterogeneous Incomplete Variational Autoencoder (HI-VAE) is learned. In a third step a modular Bayesian Network between autoencoded modules is learned while taking into account the information derived from the knowledge graph. Finally, the iVAMBN model is used to simulate gene perturbation (top right).
Fig 2.
Network representation of iVAMBN model for ROSMAP data.
Shown are the learned (grey) and knowledge-derived (green) edges between gene modules (purple nodes), single gene modules (green) and CD33 and phenotype module (red). All these edges appeared with bootstrap frequency > 0.4. The newly inferred shortest path between CD33 and phenotype is displayed in orange. Other edges with bootstrap frequency > 0.4 have been removed for visualization purposes, except for those six edges which were trained with a bootstrap confidence of 1.
Table 2.
Consistently newly learned edges in iVAMBN model.
All edges were found in each of 1000 network reconstructions from randomly subsampled data.
Fig 3.
Quantitative relationships learned by iVAMBN.
Each correlation (R) is shown along with its confidence interval (CI) and multiple testing adjusted p-value. Left: Correlation of NAV3 with TGF-Beta subgraph module member MAVS. Right: Correlation of Low density lipoprotein subgraph module member SRSF10 with CREB1, a member of the Calpastatin-calpain subgraph module. Further plots can be found in S2 and S3 Figs.
Fig 4.
Statistical significance −log10(p) value of the marginal log-likelihood of the model when evaluated on the training data (ROSMAP) and external validation data (Mayo).
Fig 5.
Module distributions in original and simulated CD33 down-expression.
The blue curve describes the original distribution, while the red one describes the CD33 down-expression scenario. CD33 down-expression simulation (left) results in lower scores of the prostaglandin pathway module (right).
Fig 6.
Predicted changes on phenotype (MMSE and Braak stages) as a consequence of CD33 down-expression.
Distribution of MMSE and Braak stages in CD33 original (blue) and down-expressed (red) patients shows a significant improvement of scores and thus cognition as well as brain pathophysiology.
Table 3.
Statistical significance of gene modules.
The table shows results of a global test [49], assessing the differential gene set expression of each gene module between WT and down-expression/KO of CD33. P-values of the test within simulated scenario, as well as, p-values from cell line KO are reported and corrected for multiple testing using the Benjamini-Hochberg method. The agreement of both tests is described in the last column, meaning if both tests are either significant or non-significant (+) or if they don’t show same direction of significance (-). For GRIN1 no p-value could be computed, as that gene is not present in the cell line data.