Tools

Biomarker / High-throughput Analysis

caCORRECT


For laboratories that produce many microarray chip data files, quality control is central to valid results. Some rely on quality control provided by microarray manufacturers and scanner hardware. Others use statistics software such as dChip, or routines provided in bioconductor (RMA, MAS, PLIER) to detect outliers in these experiments. caCORRECT represents the next-generation of microarray quality control technology that fuses spatial artifact detection (similar to Harshlighting) and model-based techniques to provide improved gene expression data quality in the presence of artifacts.

Team Members: Richard Moffitt Ph.D., Todd Stokes Ph.D., James Torrance, Martin Ahrens
Website: http://cacorrect.bme.gatech.edu
caCorrect Demo Video
caCorrect has had 221 unique users.

Publications:
  1. Stokes TH, Moffitt RA, Phan JH, Wang MD. chip artifact CORRECTion (caCORRECT): a bioinformatics system for quality assurance of genomics and proteomics array data. Ann Biomed Eng. 2007 Jun;35(6):1068-80. pubmed
  2. Stokes TH, Torrance JT, Li H, Wang MD. ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses. BMC Bioinformatics. 2008;9 Suppl 6:S18. pubmed
  3. 3. Phan JH, Moffitt RA, Stokes TH, Liu J, Young AN, Nie S, Wang MD. Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment. Trends Biotechnol. 2009 Jun;27(6):350-8. pubmed

omniBiomarker



OmniBiomarker is a web-based bioinformatics application that addresses the microarray ‘curse of dimensionality’ problem as well as the software standards problem. Biomarker identification from high-throughput microarray data for clinical prediction is sensitive to analysis parameters. As a result, candidate biomarker lists can be difficult to reproduce, limiting the efficiency of translating candidate biomarker lists to clinical applications. OmniBiomarker addresses this problem by tuning steps in the analysis pipeline to a clinical problem based on prior biological knowledge. By integrating knowledge in this manner, we can overcome the ‘curse-of-dimensionality’ problem and increase the reproducibility of biomarker identification and clinical prediction. Furthermore, omniBiomarker will also include functionality for knowledge-driven data combination to increase the statistical power of biomarker identification. Finally, omniBiomarker addresses the problem of community accessibility. It is focused on not only the novelty of the analysis pipeline, but also on the integration of these analytical steps into a user-friendly, web-accessible interface. OmniBiomarker is now caBIG Silver level compliant, further increasing the interoperability of its functions with other bioinformatics tools in the cancer research community.

Team Members: John Phan Ph.D.
Website: http://omnibiomarker.bme.gatech.edu
omniBiomarker Demo Video
omniBiomarker has had 91 unique users.

Publications:
  1. Phan JH, Yin-Goen Q, Young AN, Wang MD. Improving the efficiency of biomarker identification using biological knowledge. Pac Symp Biocomput. 2009:427-38. pubmed
  2. Phan JH, Moffitt RA, Stokes TH, Liu J, Young AN, Nie S, Wang MD. Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment. Trends Biotechnol. 2009 Jun;27(6):350-8. pubmed
  3. Phan JH, Yin-Goen Q, Young AN, and Wang MD. Translational feedback in biomarker identification using clinically-derived prior knowledge and Bayesian algorithm selection. Journal of Biomedical Informatics. (In preparation, submission: 11/1/2009.)
  4. Phan JH, Yin-Goen Q, Young AN, and Wang MD. Knowledge-driven combination of multiple high-throughput gene expression experiments for biomarker identification. Advances in Bioinformatics. (In preparation, submission: 11/1/2009.)
  5. Phan JH, Moffitt RM, Stokes TH, Yin-Goen Q, Young AN, and Wang MD. Translational bioinformatics pipeline: identifying and validating clinically relevant predictive biomarkers. Nature Biotechnology. (In preparation.)

OmniVisGrid



OmniVisGrid is a library of visualization services with semantically annotated interfaces that is currently in preparation for submission for caBIG silver-level review1. OmniVisGrid supports commonly-used statistical visualizations: box plots, Kaplan-Meier survival plots, correlation plots and histograms. OmniVisGrid includes other visualizations designed to enhance appeal and interpretability of software user interfaces. These include pie charts, graphs and heatmaps. Standard network graphs construction uses the open-source GraphViz package. This is a common technique for Gene Ontology visualizations like those in the GoMiner functional interpretation tool2. It is also useful for tracking the progress of many high-performance computing algorithms3.

Included in omniVisGrid are novel visualizations pioneered in work with metabolomics4 and on gene expression classification as part of the FDA MAQC-II Project. This work include a combinatorial network graph display, a meta-data/data correlation heatmap for detecting high-throughput genomics and proteomics experiments, and a 2.5-dimensional histogram called a feature landscape.

Team Members: Todd Stokes Ph.D., Sovandy Hang, Martin Ahrens, C.F. Quo, Richard Moffitt Ph.D., John Phan Ph.D. and Mitchell Parry Ph.D.
Collaborators: Dr. Al Merrill, Georgia Tech School of Biology
Website: http://pathwayvis.bme.gatech.edu

Publications:
  1. Stokes TH, Wang MD, editors. SimpleVisGrid: Grid Services for Visualization of Diverse Biomedical Knowledge and Molecular Systems Data. 31st IEEE Annual Int Conf Engineering in Medicine and Biology Society (EMBC’09); 2009.
  2. Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 2003;4(4):R28.pubmed
  3. Stokes TH, Phan JH, Feng WM, Tuteja G, Wang MD, editors. GAVis: a Tool for Visualization and Control of Genetic Algorithms for-omic Data Analysis. IEEE 27th Annual Int Conf Engineering in Medicine and Biology Society (EMBS’05); 2005; Shanghai. pubmed
  4. Merrill AH, Jr., Stokes TH, Momin A, Park H, Portz BJ, Kelly S, Wang E, Sullards MC, Wang MD. Sphingolipidomics: a valuable tool for understanding the roles of sphingolipids in biology and disease. J Lipid Res. 2009 Apr;50 Suppl:S97-102. pubmed

Data Management Wikis



Wikis are valuable for data management because: 1) they have an intuitive interface for users to browse and contribute information, 2) they are searchable by Google and other web index/search engines and 3) they come with many tools to ensure data source attribution, conflict resolution, and data integrity maintenance. In the near future, if efforts such as the dbPedia project are successful, Wikis may be as useful as relational databases for executing queries on large repositories.

We seed our Wikis with data from public data repositories: ArrayWiki with Gene Expression Omnibus (GEO), TissueWiki with Human Protein Atlas (HPA), and QD/SERSWiki with caNanoLab. We offer additional meta-data not offered by any other repository. We calculate data quality scores and combine data compression with data visualization in a novel data format based on open standards. The data quality scores allow users to better discriminate between analyses of low or high confidence.

Team Members: Todd Stokes Ph.D., Sovandy Hang, Martin Ahrens, Yachna Sharma, Richard Moffitt Ph.D., John Phan Ph.D., Teresa Sanders, and J.T. Torrance
Websites: Publications:
  1. Stokes TH, Moffitt RA, Phan JH, Wang MD. chip artifact CORRECTion (caCORRECT): a bioinformatics system for quality assurance of genomics and proteomics array data. Ann Biomed Eng. 2007 Jun;35(6):1068-80. pubmed
  2. Stokes TH, Torrance JT, Li H, Wang MD. ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses. BMC Bioinformatics. 2008;9 Suppl 6:S18. pubmed
  3. Phan JH, Moffitt RA, Stokes TH, Liu J, Young AN, Nie S, Wang MD. Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment. Trends Biotechnol. 2009 Jun;27(6):350-8. pubmed
  4. Stokes TH, Sharma Y, Hang S, Ahrens MP, Sanders TH, and Wang MD. TissueWiki: an Integrative Translational Biomedical Informatics Tool for Clinical Biomarkers, Tissue Imaging and Targeting Antibody Performance Analysis. BMC Bioinformatics. (In preparation.)

Image Analysis

omniSpect



Multispectral imaging technologies capture spatial as well as spectral information from a sample. For example, quantum dots target specific biomarkers and emit different fluorescent spectra; or, mass spectrometry reveals different molecular distributions within a sample. OmniSpect untangles the contributions from different sources within a sample by leveraging their known spectral profiles or inferring them directly from the data1, 2.

Team Members: Mitchell Parry Ph.D., Richard Moffitt Ph.D., Andrea Barrett

Publications:
  1. Siy PW, Moffitt RA, Parry RM, Chen Y, Liu Y, Sullards MC, Merrill AH, Wang MD, editors. Matrix factorization techniques for analysis of imaging mass spectrometry data. IEEE International Conference on Bioinformatics and Bioengineering; 2008.
  2. Caldwell ML, Moffitt RA, Liu J, Parry RM, Sharma Y, Wang MD. Simple quantification of multiplexed quantum dot staining in clinical tissue samples. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1907-10. pubmed

QD-QC



The process of developing molecular assays for disease diagnosis and prognosis requires cross-disciplinary research which monitors quality and reproducibility at all levels. This project addresses challenges in the quality control of highly multiplexed Quantum Dot (QD) immunohistochemical staining and provides methods for improving accuracy of QD quantification. This work is expected to improve the overall reproducibility and quantification of multiplexed QD staining.

Team Members: Richard Moffitt Ph.D., Matthew Caldwell, and Michael Mancini

Publications:
  1. Caldwell ML, Moffitt RA, Liu J, Parry RM, Sharma Y, Wang MD. Simple quantification of multiplexed quantum dot staining in clinical tissue samples. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1907-10. pubmed
  2. Moffitt R, Caldwell M, Li T, Liu J, Nie S, and Wang MD. Quality control of highly multiplexed proteomic immunostaining with quantum dots: correction for crosstalk. 31st IEEE Annual Int. Conf. Engineering in Medicine and Biology Society (EMBC’09), 2009.

Q-IHC



Imaging modalities have been at the forefront of the fight against cancer by providing physicians with a variety of methods to make cancer diagnosis and prognosis. The increase in technology capabilities and data volume has lead to a critical need to help physicians make full use of the overwhelming information at hand. In addition, the traditional cancer diagnosis methods depend heavily on expert training, suffers from inter-observer variability, subjectivity and procedural inconsistencies. Quantitative cancer image analysis promises to address these issues by bringing consistency and accuracy to the results. Q-IHC is a set of cancer imaging analysis tools to assess Quantum Dots (QD) and Immunohistochemistry (IHC) based molecular and tissue images for various cancers types. This includes tools for semi-automatic segmentation, morphological feature analysis and quantification, color based region detection, automatic cell counting, and quantitative molecular profiling. With special emphasis on usability and usefulness, these tools will help accelerate research and contribute significantly to the fight against cancer.

Team Members: Qaiser Chaudry, Syed Hussain Raza, Yachna Sharma, Sonal Kothari
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Publications:
  1. Xing Y, Chaudry Q, Shen C, Kong KY, Zhau HE, Chung LW, Petros JA, O'Regan RM, Yezhelyev MV, Simons JW, Wang MD, Nie S. Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry. Nat Protoc. 2007;2(5):1152-65. pubmed
  2. Chaudry Q, Kong KY, Ahearn TU, Cohen V, Bostick RM, Wang MD, editors. An integrated image quantification system for colorectal cancer assessment using quantum dots and molecular profiling. IEEE International Symposium on Biomedical Imaging: from Nano to Macro (ISBI’07); 2007; Arlington, VA.
  3. Chaudry Q, Raza SH, Sharma Y, Kothari S, Young AN, Wang MD. Automated renal cell carcinoma subtype classification using cellular features of elliptical models of segmented nuclei clusters. Computerized Medical Imaging and Graphics. 2009; Under Review.
  4. Chaudry Q, Raza SH, Sharma Y, Young AN, Wang MD, editors. Improving renal cell carcinoma classification by automatic region of interest selection. International Conference on BioInformatics and BioEngineering (BIBE'08); 2008.
  5. Kothari S, Chaudry Q, Wang MD, editors. Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques. 2009 IEEE International Symposium on Biomedical Imaging (ISBI’09); 2009.
  6. Chaudry Q, Raza SH, Sharma Y, Kothari S, Young AN, and Wang MD. Automated renal cell carcinoma subtype classification using cellular features of elliptical models of segmented nuclei clusters. Journal of Computerized Medical Imaging and Graphics, (Under revision.)
  7. Chaudry Q, Sharma Y, Raza SH, Yusuf A, Young AN, and Wang MD. Segmentation of QD and IHC stained tissue images using interactive 2D color map. (In preparation.)

PASuite



PASuite1 is a tool to recognize and preprocess cancerous regions in an image. Currently, tissue biopsies are analyzed and graded manually by expert pathologists which can be time consuming and challenging due to variations in tissue morphology, inconsistencies in preparation of tissue specimen and errors in the image acquisition process. PASuite is designed to automatically standardize the variations in different images due to changing illumination and experimental conditions. Segregating cancerous regions from non-cancerous areas is a crucial step before extracting relevant information from cancer images such as the number and size of nuclei and subsequently using it for classification and quantitative analysis. PASuite was tested on for two widely different cancers: Head and Neck Cancer (HNC) and Renal Cell Carcinoma (RCC). The tool successfully marked the cancerous areas for both types of cancers and the results match the pathologist manual validation. With PASuite, cancerous ROI are marked and can be used for feature extraction and grading of squamous cell carcinoma of head and neck (SCCHN).

Team Members: Yachna Sharma, S. Hussain Raza, Koon Y. Kong, Qaiser Chaudry
Publications:
  1. Sharma Y, Raza SH, Kong KY, Chaudry Q, Muller S, Young AN, Chen ZG, Wang MD. PASuite: a preprocessing algorithm suite for cellular and molecular image classification in cancer diagnosis and treatment. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:3114-7. pubmed
  2. Sharma Y, Raza SH, Kong KY, Chaudry Q, Muller S, Chen ZG, Wang MD, editors. Improving classification of head and neck squamous cell carcinoma using knowledge based features. Microscopic Image Analysis with Applications in Biology (MIAAB’09); 2009.

3D-CellViz



Multiplexed quantum dot (QD) imaging enables targeting and staining of several protein biomarkers on a single tissue slide. Although multiplexed staining of a single slide may reveal much about a tissue, it is often informative to explore the distribution of biomarkers in all three dimensions. An important step in 3D visualization is the alignment (registration) of images corresponding to consecutive tissue sections. Since the size of the typical eukaryotic cell is around 10µm in diameter, it is not feasible to introduce physical landmarks into tissue slides which may positively identify a single cell in two consecutive slices. Other issues pertaining to image alignment of microscopic tissue sections include missing slices and loss of sample during slicing. 3D-CellViz is a comprehensive method3 for 3D visualization of QD stained multiplexed images. The system is capable of 1) Registration of tissue image sections without established feature points. 2) Automatic feature discovery and matching. 3) Visualization of combined volume or decomposition into individual volumes. 4) Customized color map implementation for better visualization. 5) Volume slicing through any angular plane and in any orientation. 6) Visualization of whole gland or a single region of interest. 3D-CellViz methodology has been tested on prostate gland sections. Promising results suggest applications to many other clinical problems where 3D visualization and nanotechnology will enhance understanding and diagnosis of disease.

Team Members: Yachna Sharma, Richard Moffitt Ph.D., Qaiser Chaudry, Brandon Fox, Todd Stokes Ph.D.

Publications:
  1. Sharma Y, Moffitt RA, Chaudry Q, Liu J, Fox B, Stokes TH, Ni S, Wang MD. 3D Visualiz ation of Multiplexed Quantum Dot Stained Images. 2009. (In preparation.)

SurgicalART



Surgery cures approximately 45% of all cancer patients, while chemotherapy and radiation therapy cure only 5%. The most important predictor of patient survival for almost all cancers is complete surgical resection of the primary tumor, draining lymph nodes, and removing metastatic lesions. It has been shown in cases of lung, prostate, colon and pancreatic cancer, complete resection improves survival greatly. However, about 40% of the patients that undergo surgery leave the operating room without complete resection due to missed lesions. Our SurgicalART (Surgical Assist in Real Time) aims at assisting the surgeon in complete resection by providing enhanced visualization of tumor. The surgeon will be able to identify residual tumor cells and micrometasases and determine if the tumor has been completely resected in real time.

Team Members: S. Hussain Raza, R. Mitchell Parry Ph.D., Richard A. Moffitt Ph.D.

Modeling

caDrugBench



caDrugBench is our initiative to integrate biomedical knowledge, nanoparticle characterization data, and healthcare informatics for cancer research and treatment. To accomplish this goal, we will develop key component tools to build a web-enabled hub that will be available for public access:
  • nanoDRIVE will extract and visualize information from existing biomedical knowledge bases to support generating viable hypotheses for experimental and clinical tests;
  • caNanoLab will be extended to record nanoparticle characterization data in an integrated format within the suite for data sharing, communication, and collaboration;
  • VirtualOncology will simulate tumor behavior in response to nanoparticle treatment, and
  • omniClinic will gather patient records and treatment data to support clinical decisions.
Team Members: Todd Stokes Ph.D., Richard A Moffitt, Ph.D., Brandon Fox, Emma Sizemore, Neil Shah, Jaydeep Srimani, Chang F Quo