Clustering Algorithms to make sense of Microarray data: Systems Analyses in Biology Doug Welsh and Brian Davis BioQuest Workshop Beloit Wisconsin, June.

Slides:



Advertisements
Similar presentations
Asking translational research questions using ontology enrichment analysis Nigam Shah
Advertisements

Integrating Cross-Platform Microarray Data by Second-order Analysis: Functional Annotation and Network Reconstruction Ming-Chih Kao, PhD University of.
Gene Set Enrichment Analysis (GSEA)
Prof. Carolina Ruiz Computer Science Department Bioinformatics and Computational Biology Program WPI WELCOME TO BCB4003/CS4803 BCB503/CS583 BIOLOGICAL.
D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April Seminar.
Principal Component Analysis (PCA) for Clustering Gene Expression Data K. Y. Yeung and W. L. Ruzzo.
CAVEAT 1 MICROARRAY EXPERIMENTS ARE EXPENSIVE AND COMPLICATED. MICROARRAY EXPERIMENTS ARE THE STARTING POINT FOR RESEARCH. MICROARRAY EXPERIMENTS CANNOT.
Gene Ontology John Pinney
Threshold selection in gene co- expression networks using spectral graph theory techniques Andy D Perkins*,Michael A Langston BMC Bioinformatics 1.
Gene expression analysis summary Where are we now?
Microarrays Dr Peter Smooker,
Computational Molecular Biology (Spring’03) Chitta Baral Professor of Computer Science & Engg.
4 th NETTAB Workshop Camerino, 5 th -7 th September 2004 Alberto Bertoni, Raffaella Folgieri, Giorgio Valentini
Metabolomics Bob Ward German Lab Food Science and Technology.
Microarrays and Cancer Segal et al. CS 466 Saurabh Sinha.
Gene Expression Networks Esra Erdin CS 790g Fall 2010.
Demonstration Trupti Joshi Computer Science Department 317 Engineering Building North (O)
Classical tree view of cell cycle data (Spellman, et al MolBiolCell 9, 3273)
CISC667, F05, Lec24, Liao1 CISC 667 Intro to Bioinformatics (Fall 2005) DNA Microarray, 2d gel, MSMS, yeast 2-hybrid.
CIBB-WIRN 2004 Perugia, 14 th -17 th September 2004 Alberto Bertoni, Raffaella Folgieri, Giorgio Valentini Feature.
Computational Approaches for Understanding Biological Significance of Microarray Data Liangjiang (LJ) Wang KSU Bioinformatics Center, Biology.
Analysis of GO annotation at cluster level by H. Bjørn Nielsen Slides from Agnieszka S. Juncker.
Analysis of microarray data
341: Introduction to Bioinformatics Dr. Natasa Przulj Deaprtment of Computing Imperial College London
Computational Molecular Biology Biochem 218 – BioMedical Informatics Gene Regulatory.
Principal Component Analysis (PCA) for Clustering Gene Expression Data K. Y. Yeung and W. L. Ruzzo.
MATISSE - Modular Analysis for Topology of Interactions and Similarity SEts Igor Ulitsky and Ron Shamir Identification.
Analysis of Microarray Data 1.Scan the images 2.Quantify intensity of spots 3.Normalization 4.Analysis of data 5.Identification of genes of interest 6.Validation.
1 Identifying differentially expressed sets of genes in microarray experiments Lecture 23, Statistics 246, April 15, 2004.
Gene Set Enrichment Analysis (GSEA)
DNA microarray technology allows an individual to rapidly and quantitatively measure the expression levels of thousands of genes in a biological sample.
Clustering of DNA Microarray Data Michael Slifker CIS 526.
Genetic Regulatory Network Inference Russell Schwartz Department of Biological Sciences Carnegie Mellon University.
Analyzing transcription modules in the pathogenic yeast Candida albicans Elik Chapnik Yoav Amiram Supervisor: Dr. Naama Barkai.
GTL Facilities Computing Infrastructure for 21 st Century Systems Biology Ed Uberbacher ORNL & Mike Colvin LLNL.
Graph and Topological Structure Mining on Scientific Articles Fan Wang, Ruoming Jin, Gagan Agrawal and Helen Piontkivska The Ohio State University The.
GenMAPP and MAPPFinder for Systems Biology Education Kam Dahlquist Vassar College June 12-20, 2004 BioQUEST Summer Workshop Beloit College.
Networks and Interactions Boo Virk v1.0.
Intralab Workshop - Reactome CMAP Chang-Feng Quo June 29 th, 2006.
BioQUEST / SCALE-IT Module From Omics Data to Knowledge Case 1: Microarrays Namyong Lee Minnesota State University, Mankato Matthew Macauley Clemson University.
Bioinformatics Brad Windle Ph# Web Site:
GeWorkbench Highlights caBIG ® Molecular Analysis Tools Knowledge Center AACR Annual Meeting, April 3, 2011.
Biology-Driven Clustering of Microarray Data Applications to the NCI60 Data Set K.R. Coombes, K.A. Baggerly, D.N. Stivers, J. Wang, D. Gold, H.G. Sung,
SP Cancer Metastasis Summary Hypothesis: We hypothesize that miRNAs regulate breast cancer cell invasiveness and metastasis by synergistically targeting.
Intel Confidential – Internal Only Co-clustering of biological networks and gene expression data Hanisch et al. This paper appears in: bioinformatics 2002.
Data provenance in biomedical discovery Donald Dunbar Queen’s Medical Research Institute University of Edinburgh Workshop on Principles of Provenance in.
Analysis of GO annotation at cluster level by Agnieszka S. Juncker.
The Stanley Neuropathology Consortium Integrative Database: A novel web-based tool for exploring neuropathological traits, gene expression and associated.
Blind Information Processing: Microarray Data Hyejin Kim, Dukhee KimSeungjin Choi Department of Computer Science and Engineering, Department of Chemical.
Bioinformatics MEDC601 Lecture by Brad Windle Ph# Office: Massey Cancer Center, Goodwin Labs Room 319 Web site for lecture:
Data Mining the Yeast Genome Expression and Sequence Data Alvis Brazma European Bioinformatics Institute.
Biological Networks & Systems Anne R. Haake Rhys Price Jones.
GeWorkbench John Watkinson Columbia University. geWorkbench The bioinformatics platform of the National Center for the Multi-scale Analysis of Genomic.
DNA Microarray Data Analysis using Artificial Neural Network Models. by Venkatanand Venkatachalapathy (‘Venkat’) ECE/ CS/ ME 539 Course Project.
Prof. Yechiam Yemini (YY) Computer Science Department Columbia University (c)Copyrights; Yechiam Yemini; Lecture 2: Introduction to Paradigms 2.3.
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network Science, Vol 292, Issue 5518, , 4 May 2001.
Shortest Path Analysis and 2nd-Order Analysis Ming-Chih Kao U of M Medical School
Pan-cancer analysis of prognostic genes Jordan Anaya Omnes Res, In this study I have used publicly available clinical and.
1 Genomics Advances in 1990 ’ s Gene –Expressed sequence tag (EST) –Sequence database Information –Public accessible –Browser-based, user-friendly bioinformatics.
Pathway Ranking Tool Dimitri Kosturos Linda Tsai SoCalBSI, 8/21/2003.
Statistical Analysis for Expression Experiments Heather Adams BeeSpace Doctoral Forum Thursday May 21, 2009.
Fanconi Anemia Anthony Winchell. What is Fanconi Anemia?
Gene Set Analysis using R and Bioconductor Daniel Gusenleitner
Affymetrix User’s Group Meeting Boston, MA May 2005 Keynote Topics: 1. Human genome annotations: emergence of non-coding transcripts -tiling arrays: study.
Identification of Cancer Stem Cells using Flow Cytometry Analysis تعيين الخلايا الجذعية السرطانية باستخدام تحليل التدفق الخلوي Dr. Ayat Al-Ghafari Biochemistry.
Practice:submit the ChIP_Streamline.pbs 1.Replace with your 2.Make sure the.fastq files are in your GMS6014 directory.
` Comparison of Gene Ontology Term Annotations Between E.coli K12 Databases REDDYSAILAJA MARPURI WESTERN KENTUCKY UNIVERSITY.
Analysis of GO annotation at cluster level by Agnieszka S. Juncker
What is an Ontology An ontology is a set of terms, relationships and definitions that capture the knowledge of a certain domain. (common ontology ≠ common.
The Omics Dashboard.
Presentation transcript:

Clustering Algorithms to make sense of Microarray data: Systems Analyses in Biology Doug Welsh and Brian Davis BioQuest Workshop Beloit Wisconsin, June 2004

Biological Questions What are the differences between cancer cells and normal cells? What are the differences in gene expression between cancer cells and normal cells? Can you guess at the cellular sub-systems that may be affected by cancer? What are the cellular processes (pathways) that might differ between cancer cells and normal cells? Can you guess at the components (proteins) of the pathways that might be involved in cancer

Goals Systems Biology (shift focus among levels of knowledge) Biology Gene Expression (technique) Pathways DNA Replication Individual Proteins Math Clustering Algorithms (theory and technique) Statistics Medicine (human phenotype)

Goals Knowledge Cluster StatsMath Physics Optics Biology Medicine Cell Biology Pathway Protein Robotics Programming

Goals Knowledge Cluster StatsMath Physics Optics Biology Medicine Cell Biology Pathway Protein “Tools” Robotics Programming

Problem Space Bedrock web site:

Problem Space DNA Replication Cell Cycle (Depends on Paper) Microarray Files Gene Annotation Microarray Analysis Pathway Analysis Statistical Analysis

Assumptions Assume co-expression of genes has significance. We can generate A LOT of data. Wheat and Chaff Clustering algorithms and viewing software allow a researcher to focus on subsets of ( “ significant ” ) data at a time.

Project Paper: Singh D. et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell Mar;1(2): Questions: What is the testable hypothesis? How is it tested? What are the results? Are the conclusions valid? Are there other (better?) ways to test this hypothesis? Are there better hypotheses to formulate?

Biological Questions What are the differences between cancer cells and normal cells? What are the differences in gene expression between cancer cells and normal cells? Can you guess at the cellular sub-systems that may be affected by cancer? What are the cellular processes (pathways) that might differ between cancer cells and normal cells? Can you guess at the components (proteins) of the pathways that might be involved in cancer

Cluster Download: version 1.4 (v2.2 has bug): /EisenSoftware.h tm Load data Data transform***

TreeView Download latest version gov/EisenSoft ware.htm Load data

Analysis Clustering may reveal organizational units What are these proteins and what processes are they involved in?

Next Steps Hand off clusters of organizational units to Doug (GeneMAPP and MAPPFinder: What are these proteins in the context of cellular pathways?) … Investigate interesting single proteins (e.g., with NCBI tools). Are these proteins conserved? (do yeast get cancer?) What is the molecular basis of cancer?

Goals Knowledge Cluster StatsMath Physics Optics Biology Medicine Cell Biology Pathway Protein Robotics