SUPPLEMENTAL FIGURES AND TABLES. Supplementary Table 1: List of new and improved features in GSEA-P version 2 Java software. Examples and screenshots.

Slides:



Advertisements
Similar presentations
Chapter 3 – Web Design Tables & Page Layout
Advertisements

Control Case Common Always active
Charlie Whittaker – BIG meeting 12/3/14
Enrichment Map GSEA Tutorial
Getting Started: Ansoft HFSS 8.0
The Maize Inflorescence Project Website Tutorial Nov 7, 2014.
Overviews and Omics Viewers. SRI International Bioinformatics Introduction Each overview is a genome-scale diagram of a different aspect of the cellular.
Pathways analysis Iowa State Workshop 11 June 2009.
Gene Set Enrichment Analysis (GSEA)
Macromedia Dreamweaver 4 Foundation Level Course.
Kate Milova MolGen retreat March 24, Microarray experiments: Database and Analysis Tools. Kate Milova cDNA Microarray Facility March 24, 2005.
Kate Milova MolGen retreat March 24, Microarray experiments. Database and Analysis Tools. Kate Milova cDNA Microarray Facility March 24, 2005.
Kate Milova MolGen retreat March 24, Microarray experiments. Database and Analysis Tools. Kate Milova cDNA Microarray Facility March 24, 2005.
NCBI resources III: GEO and expression data analysis Yanbin Yin Fall
Kate Milova MolGen retreat March 24, Microarray experiments. Database and Analysis Tools. Kate Milova cDNA Microarray Facility March 24, 2005.
1 Chapter 20 — Creating Web Projects Microsoft Visual Basic.NET, Introduction to Programming.
XP New Perspectives on Microsoft Access 2002 Tutorial 71 Microsoft Access 2002 Tutorial 7 – Integrating Access With the Web and With Other Programs.
2. Introduction to the Visual Studio.NET IDE 2. Introduction to the Visual Studio.NET IDE Ch2 – Deitel’s Book.
1 Identify the location of a particular gene, trait, QTL or marker - and the grass species they have been mapped to - on genetic, QTL, physical, sequence,
Support for MAGE-TAB in caArray 2.0 Overview and feedback MAGE-TAB Workshop January 24, 2008.
Copyright OpenHelix. No use or reproduction without express written consent1.
Tutorial 121 Creating a New Web Forms Page You will find that creating Web Forms is similar to creating traditional Windows applications in Visual Basic.
Basic features for portal users. Agenda - Basic features Overview –features and navigation Browsing data –Files and Samples Gene Summary pages Performing.
Domain 3 Understanding the Adobe Dreamweaver CS5 Interface.
SRI International Bioinformatics 1 Object Groups & Enrichment Analysis Suzanne Paley Pathway Tools Workshop 2010.
Using geWorkbench: Hierarchical & SOM Clustering Fan Lin, Ph. D Molecular Analysis Tools Knowledge Center Columbia University and The Broad Institute of.
GSEA Overview -- Workflow GSEA is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant.
CellFateScout step- by-step tutorial for a case study Version 0.94.
Course on Functional Analysis
Abstract Background: In this work, a candidate gene prioritization method is described, and based on protein-protein interaction network (PPIN) analysis.
Gene expression analysis
Copyright OpenHelix. No use or reproduction without express written consent1.
Using COS Funding Alert Alerting You to Relevant New Opportunities from the World’s Largest Funding Database ™ Via your COS Workbench ™
BIOS6660 shRNAseq Gene Set Enrichment Analysis Tzu L Phang PhD Robert Stearman PhD April 16, 2014.
Tutorial 7 Gene expression analysis 1. Expression data –GEO –UCSC –ArrayExpress General clustering methods –Unsupervised Clustering Hierarchical clustering.
The Public Face of TAIR User Interface Design Responsiveness to User Input.
Analysis of GEO datasets using GEO2R Parthav Jailwala CCR Collaborative Bioinformatics Resource CCR/NCI/NIH.
Gene Expression Omnibus (GEO)
CaIntegrator2 – Part 1: Create a Study with Clinical Data Fan Lin, Ph. D Molecular Analysis Tools Knowledge Center Columbia University and The Broad Institute.
Copyright OpenHelix. No use or reproduction without express written consent1.
1 ArrayTrack Demonstration National Center for Toxicological Research U.S. Food and Drug Administration 3900 NCTR Road, Jefferson, AR
Copyright OpenHelix. No use or reproduction without express written consent1.
Input data for analysis Users that have expression values (dataset 1_ chicken affy_foldchane.txt. can upload that file as shown in slide 30.
Copyright OpenHelix. No use or reproduction without express written consent1.
Using geWorkbench: Working with Sets of Data Fan Lin, Ph. D. Molecular Analysis Tools Knowledge Center Columbia University and The Broad Institute of MIT.
The Broad Institute of MIT and Harvard Differential Analysis.
Copyright OpenHelix. No use or reproduction without express written consent1.
Welcome to Gramene’s RiceCyc (Pathways) Tutorial RiceCyc allows biochemical pathways to be analyzed and visualized. This tutorial has been developed for.
Tutorial 8 Gene expression analysis 1. How to interpret an expression matrix Expression data DBs - GEO Clustering –Hierarchical clustering –K-means clustering.
Copyright OpenHelix. No use or reproduction without express written consent1 1.
Tools in Bioinformatics Genome Browsers. Retrieving genomic information Previous lesson(s): annotation-based perspective of search/data Today: genomic-based.
Welcome to the combined BLAST and Genome Browser Tutorial.
Gene Set Analysis using R and Bioconductor Daniel Gusenleitner
CCLE Cancer Cell Line Encyclopedia Alexey Erohskin.
MESA A Simple Microarray Data Management Server. General MESA is a prototype web-based database solution for the massive amounts of initial data generated.
CellExpress Tutorial A Comprehensive Microarray-Based Cancer Cell Line and Clinical Sample Gene Expression Analysis Online System :8080 NTU.
Networks and Interactions
Volume 44, Issue 1, Pages (January 2016)
Strategy for working on your own data sets.
Introduction to EBSCOhost
Gramene’s Ontologies Tutorial
Anne Pfeiffer, Hui Shi, James M. Tepperman, Yu Zhang, Peter H. Quail 
Cancer Cell Line Encyclopedia
Extended analysis of differential expression datasets.
The CREBBP-modulated network is enriched in signaling pathways upregulated in the light zone (LZ). The CREBBP-modulated network is enriched in signaling.
SY-1425 shows similar response in RARA-high AML cell lines to APL
Characteristic gene expression patterns distinguish LCH cells from other immune cells present in LCH lesions. Characteristic gene expression patterns distinguish.
Presentation transcript:

SUPPLEMENTAL FIGURES AND TABLES

Supplementary Table 1: List of new and improved features in GSEA-P version 2 Java software. Examples and screenshots can be found at: Supplementary Figure 1: The ‘Run GSEA’ panel that allows datasets to be collapsed from a number of microarray platforms into Entrez gene symbols. Supplementary Figure 2: GSEA-P plot (in green) of the running enrichment score of the RAS pathway in a comparison of expression profiles from P53 mutant vs. wild type cell lines. The black bars in the center section indicate locations of the RAS pathway members in the list of genes ranked by their differential expression. The red-blue color bar is a heat map of the correlation of genes with the P53 mutant vs. wild type phenotype, while the plot in gray is the graph of correlation value with respect to position in the gene list. Supplementary Figure 3: (A) Leading edge plot of the top 10 enriched gene sets from a comparison of P53 wild type vs. mutant expression profiles. Genes in red are present in several of the gene sets upregulated in the mutant class and genes in blue are upregulated in the wild type class. (B) Jacquard matrix of the overlap between the top 10 gene sets. Supplementary Figure 4: (A) The Chip2Chip panel that provides a tool to map identifiers between platforms. (B) Currently, GSEA-P 2.0 supports mappings for 93 platforms. Supplementary Figure 5: The MSigDB browser showing a list of gene sets alongside several search options. Supplementary Figure 6: The MSigDB web page showing the 4 gene set collections. Supplementary Figure 7: Screenshot of a single GeneSetCard. These are web pages with annotations including the source and biological relevance of MSigDB gene sets. Legends for Supplementary Figures & Table

COMPONENTNEW ( # ) AND IMPROVED (*) FEATURES IN GSEA-P VERSION 2 Running GSEADataset collapsing (conversion of native probe ids to gene symbols) available directly in the GSEA panel as an option Enrichment reports If the gene set is from MSigDB, a hyperlink to the Gene Set Card is automatically inserted in the report # Custom gene sets can gain similar annotation by inserting an value in the gene set matrix file # Snapshot page gives a global picture of the top 20 (or as specified) enriched gene sets # Enrichment mountain plot includes a color bar that depicts the phenotype correlation (e.g signal-to-noise scores)* Gene marker permutation report for easy comparison b/w genes and gene sets # Chip platform annotations Several new microarray platforms added (total is now 93)* Custom chip annotations can be specified by adding a.chip file to the gsea_home/anotations folder* Chip2ChipGene sets can be converted between gene symbols  93 chip platforms # Gene sets can be converted between chip platforms # Addition of a gene thesarus for mapping gene sets into standard HUGO gene symbols. Alias database for gene symbols from Unigene (release 180) and internally curated # SystemThe application base directory (gsea_home) can be installed anywhere on the file system and its location specified by a preference setting* Object browser displays datasets currently in memory # Batch modeCommand line usage simplified by adding defaults for algorithm parameters* Heat maps and all other visualizations will be made even in headless mode* Gene set cardsHTML page containing extensive annotation about the derivation of a gene set including source, category and PubMedID (when available) # Gene set browser Browse gene sets in MSigDB from within the GSEA-P software # Search for gene sets in several ways including by gene and by abstract keyword # DocumentationComprehensive user manual # Software tutorial* FAQ* Wiki # Supplementary Table 1: List of new and improved features in GSEA-P version 2

Supplementary Figure 1: Run GSEA panel

Supplementary Figure 2: GSEA-P plot (in green) of the running enrichment score of the RAS pathway

Supplementary Figure 3A & 3B: Leading edge analysis

Supplementary Figure 4A: Chip2Chip: map gene sets between platforms

Supplementary Figure 4B: Platform annotations available via FTP

Supplementary Figure 5: MSigDB gene sets browser

Supplementary Figure 6: New MSigDB release

Supplementary Figure 7: Gene Set Cards