Resources for TCGA and GDAN
|
Tool |
Description |
Citation & Links |
|
PARADIGM |
Infers pathway activity with a factor graph model. |
(Vaske et al. 2010) github |
|
PARADIGM-SHIFT |
Compares upstream and downstream pathway activities to predict driver events. |
(S. Ng et al. 2012) github |
|
LURE |
Finds related biological events from expression signatures using ML method. |
|
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AKLIMATE |
Perform regression and classification tasks using stacked ML, multiple data types, and pathways. |
|
|
TieDIE |
Heat kernel connects genes affected by various genomic lesions (mutations, copy number, etc). |
|
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PathMark |
Find sub-nets of differential activity of nodes in a pathway graph. |
(Cancer Genome Atlas Research Network 2013) github |
|
GelNets |
Pathway-Based Genomics Prediction using Generalized Elastic Net. |
(Sokolov et al. 2016) |
|
One-Class |
Determines the presence of expression signatures in a mixed sample without the need for a negative example. |
(Sokolov, Paull, and Stuart 2016) R |
|
SCIMITAR |
Trajectory inference for single cell data using morphing gaussian model |
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PLATYPUS |
Drug sensitivity prediction multiple view learning. |
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ClueGene |
Recommends new pathway genes based on shared expression signatures. |
(D. M. Ng, Woehrmann, and Stuart 2007) web |
|
BPA |
Single-cell transformation based on pathway set enrichment to align multiple datasets. |
(Ding et al. 2019) github |
|
TumorMap |
Visualize high-dimensional data in a 2-D map with rich annotations. |
(Newton et al. 2017) web |
|
Prophetic granger causality |
Infer gene regulatory networks from time series data using an approach from economics theory. |
(Carlin et al. 2017) github |
|
Gene Programs |
Identify non-redundant gene expression modules for subtypes. |
(Hoadley et al. 2014, 2018) |
REFERENCES
Cancer Genome Atlas Research Network. 2013. “Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma.” Nature 499 (7456): 43–49.
Carlin, Daniel E., Evan O. Paull, Kiley Graim, Christopher K. Wong, Adrian Bivol, Peter Ryabinin, Kyle Ellrott, Artem Sokolov, and Joshua M. Stuart. 2017. “Prophetic Granger Causality to Infer Gene Regulatory Networks.” PloS One 12 (12): e0170340.
Cordero, Pablo, and Joshua M. Stuart. 2016. “TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 22: 576–87.
Ding, Hongxu, Andrew Blair, Ying Yang, and Joshua M. Stuart. 2019. “Biological Process Activity Transformation of Single Cell Gene Expression for Cross-Species Alignment.” Nature Communications 10 (1): 4899.
Graim, Kiley, Verena Friedl, Kathleen E. Houlahan, and Joshua M. Stuart. 2019. “PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 24: 136–47.
Haan, David, Ruikang Tao, Verena Friedl, Ioannis N. Anastopoulos, Christopher K. Wong, Alana S. Weinstein, and Joshua M. Stuart. 2020. “Using Transcriptional Signatures to Find Cancer Drivers with LURE.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 25: 343–54.
Hoadley, Katherine A., Christina Yau, Toshinori Hinoue, Denise M. Wolf, Alexander J. Lazar, Esther Drill, Ronglai Shen, et al. 2018. “Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.” Cell 173 (2): 291–304.e6.
Hoadley, Katherine A., Christina Yau, Denise M. Wolf, Andrew D. Cherniack, David Tamborero, Sam Ng, Max D. M. Leiserson, et al. 2014. “Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin.” Cell 158 (4): 929–44.
Newton, Yulia, Adam M. Novak, Teresa Swatloski, Duncan C. McColl, Sahil Chopra, Kiley Graim, Alana S. Weinstein, et al. 2017. “TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal.” Cancer Research 77 (21): e111–14.
Ng, David M., Marcos H. Woehrmann, and Joshua M. Stuart. 2007. “Recommending Pathway Genes Using a Compendium of Clustering Solutions.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing , 379–90.
Ng, Sam, Eric A. Collisson, Artem Sokolov, Theodore Goldstein, Abel Gonzalez-Perez, Nuria Lopez-Bigas, Christopher Benz, David Haussler, and Joshua M. Stuart. 2012. “PARADIGM-SHIFT Predicts the Function of Mutations in Multiple Cancers Using Pathway Impact Analysis.” Bioinformatics 28 (18): i640–46.
Paull, Evan O., Daniel E. Carlin, Mario Niepel, Peter K. Sorger, David Haussler, and Joshua M. Stuart. 2013. “Discovering Causal Pathways Linking Genomic Events to Transcriptional States Using Tied Diffusion Through Interacting Events (TieDIE).” Bioinformatics 29 (21): 2757–64.
Sokolov, Artem, Daniel E. Carlin, Evan O. Paull, Robert Baertsch, and Joshua M. Stuart. 2016. “Pathway-Based Genomics Prediction Using Generalized Elastic Net.” PLoS Computational Biology 12 (3): e1004790.
Sokolov, Artem, Evan O. Paull, and Joshua M. Stuart. 2016. “ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 21: 405–16.
Uzunangelov, V., C. K. Wong, and J. Stuart. 2020. “Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge.” bioRxiv . https://www.biorxiv.org/content/10.1101/2020.07.15.205575v1.abstract .
Vaske, Charles J., Stephen C. Benz, J. Zachary Sanborn, Dent Earl, Christopher Szeto, Jingchun Zhu, David Haussler, and Joshua M. Stuart. 2010. “Inference of Patient-Specific Pathway Activities from Multi-Dimensional Cancer Genomics Data Using PARADIGM.” Bioinformatics 26 (12): i237–45.