Abstract | The gene analysis is based on a multiple regression model , to provide better statistical performance. |
Gene analysis | This model first projects the SNP matriX for a gene onto its principal components (PC), pruning away PCs with very small eigenval-ues, and then uses those PCs as predictors for the phenotype in the linear regression model . |
Gene analysis | This multiple regression model ensures that LD between SNPs is fully accounted for. |
Gene analysis | The linear regression model is also applied when Y is a binary phenotype. |
Gene-set analysis | As such, using this variable Z a very simple intercept-only linear regression model can now be formulated for each gene set 5 of the form Z5 2 flOT —|— 55, where Z5 is the subvector of Z corresponding to the genes in 5. |
Gene-set analysis | It should be clear that in this framework, the gene-set analysis models are a specific instance of a more general gene-level regression model of the form Z 2 Q; —|— C1 Bl —|— Czflz —|— . |
Gene-set analysis | This is achieved by adding these variables, as well as the log of these variables, as covariates to the gene-level regression model . |
Introduction | Both self-contained and competitive gene-set analyses are implemented using a gene-level regression model . |
Activator-Inhibitor Combinations Highlight Multiple Actions of an ADP Inhibitor | To evaluate the significance of the observed combination effects, we carried out multiple regression modelling . |
Activator-Inhibitor Combinations Highlight Multiple Actions of an ADP Inhibitor | The regression model was fitted by including a parameter for the main effect for each of the activators and inhibitors. |
Integrated Model | To combine the three strands of information, we took (i) the linear regression model derived from the activator-inhibitor combination analysis, that already included all main effects and four activator-inhibitor combination effects, and added (ii) the two significant activator-activator synergy and (iii) the three significant inhibitor-inhibitor synergy terms identified above. |
Integrated Model | These parameters were then fitted together in a unified multiple regression model predicting platelet activation. |
Statistical Modeling | To integrate the three strands of information, we took the significant interactions identified in the double Wilcoxon test for synergy, and the significant activator-inhibitor combination terms identified from the stepwise linear regression modelling . |
Supporting Information | Multiple regression model with main effect terms (assumes no activator-inhibitor |
Supervised learning: Classification | Furthermore, the logistic regression model enables straightforward identification of the key contributors, and points toward feature roles consistent with known IgG and innate immune cell biology. |
Supervised learning: Regression | When switching to the PCA-derived features, the ADCC regression model is driven by PC6 (V1V2), as with the classification model, while the cytokine regression model agrees with the classification model in its use of PC6 and PC7 with opposing signs, while weakening PC2 (IgG2/4 vs. 1/3) perhaps in lieu of added contributions from PC4 (IgG4) and PC3 (IgG3). |
Supervised learning: Regression | SVR is based on the same theory as SVM, discussed above, but uses the kernel-based approach to fit a regression model to reduce the quantitative prediction error. |
readily interpretable. | Regression modeling of ADCP from antibody features by Lars. |