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Results: 9

1.
Fig. 9.

Fig. 9. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Kaplan-Meier survival plots for the clusters from Figure 8.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
2.
Fig. 8.

Fig. 8. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Clustering of IPAs for TCGA GBM. Each column corresponds to a single sample, and each row to a biomolecular entity. Color bars beneath the hierarchical clustering tree denote clusters used for Figure 9.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
3.
Fig. 2.

Fig. 2. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Overview of the PARADIGM method. PARADIGM uses a pathway schematic with functional genomic data to infer genetic activities that can be used for further downstream analysis.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
4.
Fig. 4.

Fig. 4. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Learning parameters for AKT1. IPAs are shown at each iteration of the EM algorithm until convergence. Dots show IPAs from permuted samples and circles show IPAs from real samples. The red line denotes the mean IPA in real samples and the green line denotes the mean IPA of null samples.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
5.
Fig. 5.

Fig. 5. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Distinguishing decoy from real pathways with PARADIGM and SPIA. Decoy pathways were created by assigning a new gene name to each gene in a pathway. PARADIGM and SPIA were then used to compute the perturbation of every pathway. Each line shows the receiver-operator characteristic for distinguishing real from decoy pathways using the perturbation ranking. In breast cancer, the areas under the curve (AUCs) are 0.669 and 0.602 for PARADIGM and SPIA, respectively. In GBM, the AUCs are 0.642 and 0.604, respectively.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
6.
Fig. 7.

Fig. 7. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

CircleMap display of the ErbB2 pathway. For each node, ER status, IPAs, expression data and copy-number data are displayed as concentric circles, from innermost to outermost, respectively. The apoptosis node and the ErbB2/ErbB3/neuregulin 2 complex node have circles only for ER status and for IPAs, as there are no direct observations of these entities. Each patient's data is displayed along one angle from the circle center to edge.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
7.
Fig. 6.

Fig. 6. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Patient sample IPAs compared with ‘within’ permutations for Class I PI3K signaling events mediated by Akt in breast cancer. Biological entities were sorted by mean IPA in the patient samples (red) and compared with the mean IPA for the permuted samples. The colored areas around each mean denote the of SD each set. IPA's on the right include AKT1, CHUK and MDM2.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
8.
Fig. 1.

Fig. 1. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

NCI Pathway interactions in TCGA GBM data. For all (n=462) pairs where A was found to be an upstream activator of gene B in NCI-Nature Pathway Database, the Pearson correlation (x-axis) computed from the TCGA GBM data was calculated in two different ways. The histogram plots the correlations between the A's copy number and B's expression (C2E, solid red) and between A's expression and B's expression (E2E, blue). A histogram of correlations between randomly paired genes is shown for C2E (dashed red) and E2E (dashed blue). Arrows point to the enrichment of positive correlations found for the C2E (red) and E2E (blue) correlation.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.
9.
Fig. 3.

Fig. 3. From: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Conversion of a genetic pathway diagram into a PARADIGM model. A. Data on a single patient is integrated for a single gene using a set of four different biological entities for the gene describing the DNA copies, mRNA and protein levels, and activity of the protein. B. PARADIGM models various types of interactions across genes including transcription factors to targets (upper-left), subunits aggregating in a complex (upper-right), post-translational modification (lower-left) and sets of genes in a family performing redundant functions (lower-right). C. Toy example of a small sub-pathway involving P53, an inhibitor MDM2, and the high level process, apoptosis as represented in the model.

Charles J. Vaske, et al. Bioinformatics. 2010 June 15;26(12):i237-i245.

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