Stability of different methods in the between-dataset setting, as a function of the size of the signature.

<p>Stability of different methods in the between-dataset setting, as a function of the size of the signature.</p

Bias in the selection through entropy and Bhattacharyya distance.

<p>Estimated cumulative distribution functions (ECDF) of the first ten genes selected by four methods on GSE1456. They are compared to the ECDF of randomly chosen background genes.</p

Stability for a signature of size 100.

<p>Average and standard errors are obtained over the four datasets. a) Soft-perturbation setting. b) Hard-perturbation setting. c) Between-datasets setting.</p

Characterisation of LUAD patient subtypes obtained with NetNorM (<i>N</i> = 5 groups, <i>k</i> = 315, Pathway Commons).

<p>(a) Kaplan Meir survival curves for NetNorM subtypes with significantly distinct survival outcomes. In the legend are indicated the subtype number followed by the number of patients in the subtype. (b) Metapatients matrix obtained by applying NMF to mutation profiles processed with NetNorM. The matrix shown is restricted to the genes with highest variance across metapatients. The genes (columns) are clustered via hierarchical clustering. Clusters are numbered from 1 to 20 from left to right. (c) Distribution of gene replication times across gene clusters. (d) A <i>χ</i>² contingency test was performed for each gene cluster to test its enrichment (or depletion) in mutations across patient subtypes given the subtypes’ marginal number of mutations. The value represents the contribution of a subtype to the test statistic, and the colour indicates an enrichment (red) or a depletion (blue) in mutations. (e) Distribution of patients’ total number of (raw) mutations across patient subtypes.</p

AUC (between-datasets setting).

<p>AUC obtained with Nearest Centroids when a signature is learnt from one dataset and tested by 10-fold cross-validation on the three remaining datasets. Standard error is shown within parentheses. For each training dataset, we highlighted the best performance. The <i>Type</i> column refers to the use of feature selection run a single time (S) or through ensemble feature selection, either with the mean (E-M), exponential (E-E) or stability selection (E-S) procedure to aggregate lists.</p

Area under the ROC Curve.

<p>NC classifier trained as a function of the number of samples in a -fold CV setting for each of the four datasets. We show here the accuracy for 100-gene signatures.</p

Comparison of the survival predictive power of the raw mutation data, NSQN and NetNorM (with Pathway Commons as gene network) for 8 cancer types.

<p>For each cancer type, samples were split 20 times in training and test sets (4 times 5-fold cross-validation). Each time a sparse survival SVM was trained on the training set and the test set was used for performance evaluation. The presence of asterisks indicate when the test CI is significantly different between 2 conditions (Wilcoxon signed-rank test, <i>P</i> < 5 × 10⁻² (*) or <i>P</i> < 1 × 10⁻² (**)).</p

GO stability for a signature of size 100 in the soft-perturbation setting.

<p>Average and standard errors are obtained over the four datasets. A) Soft-perturbation setting. B) Hard-perturbation setting. C) Between-datasets setting.</p

Area under the ROC curve.

<p>Signature of size in a -fold CV setting and averaged over the four datasets.</p

Exploring NSQN and NetNorM performances levers.

<p>(a) Subtypes log-rank statistic obtained for LUAD (left) and SKCM (right). One circle indicate a P-value <i>P</i> ≤ 5 × 10⁻² and two concentric circles indicate <i>P</i> ≤ 1 × 10⁻² (log-rank test). (b) Consensus clustering matrices for LUAD. (c) Survival prediction performances for LUAD (left) and SKCM (right). (d) Confusion matrices for LUAD (top) and SKCM (bottom) comparing the subtypes obtained with NSQN and SimpNSQN on the one hand, and NSQN and NetNorM on the other hand. (a, b, c, d) were obtained with Pathway Commons.</p