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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:
Melanoma Patients' Information Page: Comprehensive site for melanoma patients with information on the latest available therapies and research. Searchable databases for clinical trial abstracts, bulletin board, chat roomABCDs of Melanoma Detection: Look for danger signs in pigmented skin lesions. Presented by the American Academy of Dermatology.
CancerBACUP: Understanding Malignant Melanoma: Information about malignant melanoma, including causes, symptoms, diagnosis, treatments, side effects and further resources.
CancerNews: Watch Your Back: Malignant melanoma. Each year over a million people in the United States alone will be diagnosed with skin cancer. Of these, approximately 80,000 will be afflicted with melanoma. The incidence or the number of new cases of melanoma per year continues to rise. One in seventy-one persons born in th...
Diagnosis of Melanoma: Created as a tutorial for medical students.
FAQ about Melanoma: Links and information presented as an educational aid.
Genetic Change in Jewish Melanoma: FAMILIAL/INHERITED MALIGNANT MELANOMA IN JEWS PROJECT AT THE WEIZMANN INSTITUTE OF SCIENCE
Genomel: Melanoma Genetics Consortium: GenoMEL consortium brings together teams from around the world who are working on the genetics of melanoma and identifying who is prone to developing Melanoma.
Interferon for Melanoma: Your source for facts on melanoma — reliable and understandable. Here, you'll find answers about treatment options, talking to your doctor, and making the most of your life with melanoma. Learn what to do if you're worried about a mole on your skin. Find out how to protect yourself and your fa...
Learn Melanoma.org: An online learning experiment that teaches subjects to recognize, detect and distinguish Malignant Melanoma from benign moles and other skin conditions.
M. D. Anderson Cancer Center: Melanoma: A discussion of the topic from this Houston, Texas institution.
Malignant Melanoma: Links and articles on research and treatment.
Mayo Clinic: Melanoma Study Group: Melanoma Study group
Melanoma Care Consortium: A PharmAdura LLC Site
Mole Melanoma Information: This website has been designed to help members of the public and the general medical community increase their awareness of the changes in moles that could indicate the development of cancerous changes known as malignant melanoma. Detection, diagnosis, and treatment of cutaneous malig...
Mole Monitor UK: MoleMonitor is the UK's only clinic for investigating and monitoring moles. It's high quality professional services help detect Malignant Melanoma early.
NCI: Melanoma: Information about melanoma treatment, prevention, causes, screening, clinical trials, research, and other topics from the National Cancer Institute.
Northern California Melanoma Center: The Northern California Melanoma Center is your central resource for melanoma information including ground breaking new protocols
Ocular Melanoma Metastatic to the Liver: Treatment and Information regarding Ocular Melanoma Mestastatic to the Liver
SkinLesions.net: Diagnosis of Melanoma: Scientific site which highlights the use of computers in medicine.
SunBlitz: Melanoma: A personal oddesy into the ravages of the Sun on our skin and the health benefits to be derived from avoidance of overexposure - your definitive guide for information.
The MOLE Clinic UK: The MOLE Clinic ™ UK - advanced skin screening of moles for malignant melanoma skin cancer and mole removal services. Our goal is to reduce deaths from melanoma and reduce unnecessary mole removal. Clinics throughout the uk.
UK Melanoma Study Group: The UK Melanoma Study Group exists to advance the knowledge and treatment of malignant melanoma in the United Kingdom
Understanding Melanoma: Understanding Melanoma - Information for people with melanoma, their families and carers. Includes information on diagnosis, treatment and follow-up. Information on support services is also included.
University of Pittsburgh: Melanoma: Addresses a number of topics related to the condition.
University of Sydney: The Melanoma Foundation: Australian entity fosters prevention, research and treatment.
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