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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic data

Fig. 2

Example Scn10aG1662S Gdaphen analysis. A Variance explained by the top ten PCA components and cumulative variance using the full model containing the 14 variables. B Variance explained by the top ten PCA components and cumulative variance using the sel30% model containing 9 variables. C 3D-PCA plots showing the individuals clustering on the first 3 dimensions coloring based on each genotype and sex combination. Left panel shows all the genotypes and sexes, the middle plot shows only wild type and heterozygous data, and the right plot shows only wildtype and heterozygous data. D Classifiers results for the 9 variables included in the Sel30% model showing the most important variables to genotype discrimination after scaling to the top discriminative one. E Classifiers results for all the 14 variables included in the full model showing the most important variables to genotype discrimination after scaling to the top discriminative one. F 2D-PCA plots showing the individuals clustering on the first 3 dimensions and colored based on each genotype and sex combination. G Categorical variables discrimination component map. The panels show the distribution in 2 dimensions of the categorical variables PCA coordinates calculated in the MFA analysis using the MFAmix function from PCAmixdata R package [21]. H Square loadings plot with coordinates calculated in the MFA analysis using the MFAmix function from PCAmixdata R package [21]. I Cosine similarity distance coordinates drawn in each principal component for the selected 30% variables of the analyses calculated by the MFAmix function. The arrow length measures the contribution of each variable to the discrimination on each dimension. Arrows that follow similar trajectories (stronger cosine similarity distance) contribute to the discrimination of the data in the same dimensions

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