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University of Texas MD Anderson Cancer Center
Statistical Issues in Proteomic Research
Jeffrey S. Morris Thu, 31 Jul 2008 11:03:24 -0700
Microproteomics: Analysis of protein diversity in small samples
Howard B. Gutstein Fri, 13 Jun 2008 14:38:53 -0700
Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The effects of miniaturization on these analyses are highlighted using neuroscience-related examples, as are statistical issues unique to the high-dimensional datasets generated by proteomic experiments.
Pinnacle: A Fast, Automatic Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data
Jeffrey S. Morris Tue, 04 Dec 2007 09:44:53 -0800
Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust, and reproducible methods for detecting, matching, and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels, and finally quantifying each spot by calculating normalized spot volumes. This approach is time con-suming, error-prone, and frequently requires extensive manual edit-ing, which can unintentionally introduce bias into the results.Results: We introduce a new method for spot detection and quanti-fication called Pinnacle that is automatic, quick, sensitive and spe-cific, and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller CVs. More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.
Laser capture sampling and analytical issues in proteomics
Howard Gutstein Tue, 04 Dec 2007 09:35:54 -0800
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for the analysis of the highly complex datasets generated are also reviewed.
Statistical contributions to proteomic research
Jeffrey S. Morris Wed, 04 Apr 2007 12:55:09 -0700
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
Wavelet-based functional mixed model analysis: Computational considerations
Richard C. Herrick Wed, 04 Apr 2007 12:48:45 -0700
Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.
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Statistical Issues in Proteomic Research
Jeffrey S. Morris Thu, 31 Jul 2008 11:03:24 -0700
Microproteomics: Analysis of protein diversity in small samples
Howard B. Gutstein Fri, 13 Jun 2008 14:38:53 -0700
Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The effects of miniaturization on these analyses are highlighted using neuroscience-related examples, as are statistical issues unique to the high-dimensional datasets generated by proteomic experiments.
Pinnacle: A Fast, Automatic Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data
Jeffrey S. Morris Tue, 04 Dec 2007 09:44:53 -0800
Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust, and reproducible methods for detecting, matching, and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels, and finally quantifying each spot by calculating normalized spot volumes. This approach is time con-suming, error-prone, and frequently requires extensive manual edit-ing, which can unintentionally introduce bias into the results.Results: We introduce a new method for spot detection and quanti-fication called Pinnacle that is automatic, quick, sensitive and spe-cific, and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller CVs. More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.
Laser capture sampling and analytical issues in proteomics
Howard Gutstein Tue, 04 Dec 2007 09:35:54 -0800
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for the analysis of the highly complex datasets generated are also reviewed.
Statistical contributions to proteomic research
Jeffrey S. Morris Wed, 04 Apr 2007 12:55:09 -0700
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
Wavelet-based functional mixed model analysis: Computational considerations
Richard C. Herrick Wed, 04 Apr 2007 12:48:45 -0700
Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.
Sites:
Breast Cancer: Provides access to secondary-research articles on carcinogenesis-based prevention, early detection, evidence-based current treatment and future directions.Prevention: Web site for Cancer Epidemiology Biomarkers & Prevention.
Proteomics: International journal featuring articles and reviews on the application of related technology to basic, experimental and clinical cancer research.
American Cancer Society Journals: Web site for American Cancer Society Journals.
American Journal of Clinical Oncology: Cancer Clinical Trials: Provides access to full text content, online-only content, features and services and author submission materials.
Annals of Oncology: Journal of the European Society for Medical Oncology. Publishes articles across a multi-disciplinary spectrum.
Annals of Surgical Oncology: Web site for Annals of Surgical Oncology.
Anti-Cancer Drugs: Provides access to full text content, online-only content, features, services and author submission materials.
Anticancer Research: Publishes articles which address the areas of experimental and clinical oncology. Provides subscription information and instructions for authors.
Breast Cancer Online: Breast Cancer Online (BCO) aims to facilitate timely access to new trends and topical information in breast cancer for healthcare professionals.
Breast Cancer Research: Online and print journal covering topics of basic and clinical research relevant to breast cancer. Research articles are free to all users.
British Journal of Cancer: The BJC is owned by Cancer Research UK, a charity dedicated to understanding the causes, prevention and treatment of cancer and to making sure that the best new treatments reach patients in the clinic as quickly as possible. The journal reflects these aims. It was founded more than fifty years ag...
Cancer Biology and Therapy: Landes Bioscience
Cancer Detection and Prevention Online: The web resource on cancer control by predictive and preventive oncology.
Cancer Gene Therapy: Cancer Gene Therapy is the essential gene therapy resource for cancer researchers and clinicians, keeping readers up to date with the latest developments in gene therapy for cancer.
Cancer Immunology: Cancer Immunity, the online journal of the Academy of Cancer Immunology, provides a forum for the exchange of scientific and clinical information in the field of tumor immunology.
Cancer Informatics: Open Access journals and text books in science, technology and medicine
Cancer Research: Web site for Cancer Research.
Cancer Wise: CancerWise is a monthly electronic publication that contains information about the latest advancements in cancer treatment and research, and cancer prevention tips, among other cancer news and information. CancerWise is produced by MD Anderson Cancer Center.
Cell Cycle: Landes Bioscience
Clinical Cancer Research: Web site for Clinical Cancer Research.
Clinical Care Options for Oncology: Online journal includes articles, case discussion, medical meeting information, conference coverage and news. Requires registration.
Cogent Medicine: Provides monthly updated listings of current medical literature in oncology for cancer care professionals. Registration required.
Contemporary Oncology Journal: Wspolczesna Onkologia is a Polish bimonthly covering recent advances, and offering scientific papers and reviews from Europe.
Current Opinion in Oncology: Provides access to full text content, online-only content, features and services and author submission materials.
European Journal of Cancer Prevention: Provides access to full-text content, online-only content, features and services and author submission materials.
Expert Review of Anticancer Therapy: Offers articles on therapeutic and diagnostic advances in oncology including tumor management, biomarkers, therapy and treatment guidelines.
Hematology / Oncology Clinics of North America: Each issue focuses on a single topic and is presented under the direction of a guest editor.
Integrative Cancer Therapies: SAGE Publications is an independent international publisher of journals and books. Known for our commitment to quality and innovation, we are a world leader in scholarly, educational, and professional markets.
International Journal of Oncology: Publishes basic and clinical studies, and features well-known scientists each month.
Journal of Clinical Oncology: Web site for Journal of Clinical Oncology.
Journal of Surgical Oncology: Online journal specifically dedicated to education of residents in surgery.
Journal of the National Cancer Institute: Publishes original research papers, and acts as resource for current related information.
Leukemia: Leukemia is one of the leading journals in hematology and oncology. It is published monthly and covers all aspects of the research and treatment of leukemia and allied diseases. Studies of normal hemopoiesis are covered because of their comparative relevance.
MedBio World - Oncology Journal List: Links to Oncology Journals
MedBioWorld: Medical and Biotechnology Resource and Reference Portal for Professionals
Medical and Pediatric Oncology: Publishes original articles on the diagnosis, treatment, epidemiology, biology, and molecular and clinical genetics of these diseases.
Melanoma Research: Provides access to full text content, online-only content, features and services and author submission materials.
Michigan Oncology Journal: Advancements in Clinical and Basic Science Research from the University of Michigan Comprehensive Cancer Center.
Molecular Cancer Research: Web site for Molecular Cancer Research.
Molecular Cancer Therapeutics: Web site for Molecular Cancer Therapeutics.
Nature Reviews Cancer: Science Articles: Features scientific review articles oriented to a variety of related topics.
Neoplasia: International journal oriented to basic cancer research and published by the Nature Publishing Group.
Neuro-Oncology: Academic and general books. Catalogs, secure online ordering. Sign up for email notification of new books in your field.
New England Journal of Medicine: Oncology Articles: The Oncology collection covers topics such as chemotherapy, metastatic disease, prostate cancer and imatinib mesylate and includes research articles, case reports, reviews, and editorial commentary.
Oncogene: Nature - the world's best science and medicine on your desktop
OncologyLinx: Features peer-reviewed cancer articles, newsletters, peer-reviewed journal articles, CME, conferences, and medical dictionaries. HemeOncLinx and MDLinx aggregate for physicians, health care professionals, residents, med students the most current medical news, journals, and research.
Radiation Oncology Online Journal: Radiation Oncology Online Journal (ROOJ) extensive information and resources.
Seminars in Cancer Biology: Review journal aimed at current developments in molecular oncology. Available to subscribers of the IDEAL library.
Surgical Oncology Clinics of North America: Each issue focuses on a single topic and is presented under the direction of a guest editor.
The Oncologist CME Online: Web site for The Oncologist CME Online.
This directory is based on the Open Directory and may have been modified