Dataset Information

"Big Data" for Breast Cancer: Where to look and what you will find.

ABSTRACT: Accessing the massive amount of breast cancer data that is currently publically available may seem daunting to the brand new graduate student embarking on his/her first project or even to the seasoned lab leader, who may wish to explore a new avenue of investigation. In this review, we provide an overview of data resources focusing on high-throughput data and on cancer-related data resources. While not intended as an exhaustive list, the information included in this review will provide a jumping off point with descriptions of and links to the various data resources of interest. The review is divided into six sections: (1) Compendia of data resources; (2) Biomolecular Repository "Hubs"' (3) A list of cancer-related data resources, which provides information on contents of the resource and whether the resource enables upload and analysis of investigator provided data; (4) A list of seminal publications containing specific breast cancer data, e.g., publications from METBRIC, Sanger, TCGA; (5) A list of journals focused on data science that include cancer-related "Big Data"; and (6) Miscellaneous resources.

SUBMITTER: Clare SE

PROVIDER: S-EPMC5289822 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

"Big Data" for Breast Cancer: Where to look and what you will find.

Clare Susan E SE Shaw Pamela L PL

NPJ breast cancer 20161102

Accessing the massive amount of breast cancer data that is currently publically available may seem daunting to the brand new graduate student embarking on his/her first project or even to the seasoned lab leader, who may wish to explore a new avenue of investigation. In this review, we provide an overview of data resources focusing on high-throughput data and on cancer-related data resources. While not intended as an exhaustive list, the information included in this review will provide a jumping ...[more]

PMID: 28164152

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Project description:Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.

Dataset Information

"Big Data" for Breast Cancer: Where to look and what you will find.

Publications

"Big Data" for Breast Cancer: Where to look and what you will find.

Similar Datasets

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