Basic features for portal users

Agenda - Basic features Overview –features and navigation Browsing data –Files and Samples Gene Summary pages Performing Analyses on the portal –Co-expression, differential expression, GSEA Managing your shelf

Overview - portal home page

Overview - organization The portal data is organized around 4 main concepts Laboratories (aka projects) Studies (aka experiments) Data Sets Files Access control is organized around Users Groups

Overview - Labs and Studies Laboratories –Have defined ‘curator’ groups and ‘reader’ groups –Contain zero or more studies Studies –Represent a collection of data assembled to answer a question –Contain zero or more datasets –‘Reader’ groups are a subset of their Lab’s reader groups

Overview - Datasets and Files Datasets –All data is of one type (gene expression, CN, etc) –Multiple datasets of the same type is OK –contain zero or more files –‘reader’ groups are a subset of their Study’s reader groups Files –The basic unit of data in the portal –May be any format unrecognized formats may not be analyzed but may be shared and downloadable

Overview - Laboratories/projects

Browsing data Sharing Data –You can see (and download) any data files you can see –Filter data types with the checkboxes on top Page Info –At the top of most pages - brief help for the page My Shelf –Save datasets to your shelf for later (re)use

Browsing data

Browsing Samples Interactive browser of sample annotations Filter samples based on phenotypic information provided Thumb-scrollers for numeric data

Exercise 1. Browse the portal 1.Go to the portal in a web browser 2.Login/register if needed 3.Click on the ‘BROWSE’ menu item Then the ‘DATA’ submenu 4.Uncheck the ‘Sample annotation’ and ‘undefined’ filter checkboxes 5.Click on the ‘BROWSE’ menu item again then the ‘SAMPLES’ submenu 6.Select a dataset to browse 7.Experiment with filtering options

Gene Summary Pages Provide an overview of the information about a gene Heatmaps showing expression in the datasets that you can see Gene description (from Entrez), links to COSMIC Optional –Display summaries of mutations - if any are loaded in the portal –Display plot of copy number by expression - requires paired CN and expression samples & linking ids

Gene Search Enter a gene name in the search box on the home page or near the menus Multiple hits indicates multiple species (we’ll make this more explicit in a later version) click

Gene Summary Pages

Exercise 2. Review your favorite gene 1.Enter a gene name in the search box e.g. EGFR, FGFR3 2.Click a gene name on the results page 3.Review the gene summary page

Performing Analyses The portal is built to allow non-computational biologists to perform many common analyses –Look for co-expressed genes –Look for differentially expressed genes –Look for gene set enrichment Analyses are performed by a GenePattern server using its modules Co-expression -> Gene Neighbors Diff. Expression -> Comparative Marker Selection Gene Set enrichment -> GSEA

Performing Analyses - details Analysis parameter defaults are set by the portal curator –These are set portal-wide To change the parameters and/or assumptions, download the data and analyze it in GenePattern directly Detailed descriptions of the analyses, how to run them, and default parameters are available on the help menu –Text tutorials for all –Video tutorials for some

Performing Analyses - help

Co-expression Find genes with similar gene expression profiles to a particular gene You provide a gene and select a dataset An analysis is launched to detect the 20 most correlated genes in the dataset using Pearson Correlation The analysis displays a heat map –This is a java applet, you must tell your browser to ‘allow’ it when asked or you will not see it –The heat map viewer can be ‘popped’ out of the browser to allow you to see more detail –Menus (on the viewer) provide numerous other options to explore

Co-expression

Co-expression results

Exercise 3. Find co-expressed genes 1.Go to the portal home page 2.Select the ‘Analyses’ menu 3.Select the GeneNeighbors button, click ‘Next step’ 4.Enter a gene name (e.g. EGFR), click ‘Select Gene Symbol’ 5.Click the gene name (if needed), click ‘Select Data Set’ 6.Select ‘YFV_2008…’, click ‘Select Probe’ 7.Click ‘Run Analysis’

Differential expression This looks for genes whose expression levels vary between 2 conditions Select a dataset, then define 2 classes based on the sample annotations An analysis is launched to detect the 20 top ranked genes in each direction using 2-sided SNR (median) and 1000 permutations The analysis displays a heat map and a table with the genes and their significance –This heatmap is just an image, not an applet

Differential expression

Differential expression results

Exercise 4. differentially expressed genes 1.Go to the portal home page 2.Select the ‘Analyses’ menu 3.Select the Comparative Marker Selection button, click ‘Next step’ 4.Create a Sample Set, Select ‘YFV_2008…’, click ‘Create Sample Set’ 5.For Class 1, Click ‘Tcell activation’ and the range For Class 2, Click ‘Tcell activation’ and the range Enter a name and description, 8.Click ‘Run Analysis’ 9.Open results from ‘My Shelf’ when complete

Gene Set Enrichment Analysis Sometimes no individual genes are significantly differentially expressed We improve statistical power by comparing gene sets Example: human diabetes – No single gene significant – GSEA was used to assess enrichment of 149 gene sets including 113 pathways from internal curation and GenMAPP, and 36 tightly co- expressed clusters from a compendium of mouse gene expression data. Normal Diabetic Skeletal muscle biopsies These GSEA results appeared in Mootha et al. Nature Genetics 15 June 2003, vol. 34 no. 3 pp 267 – 273:

Enrichment: KS-score hit (member of G) miss (non-member of G) Gene Set G Enrichment Score S Gene List Order Index Max. Enrichment Score ES Mootha et al., Nature Genetics 2004 Ordered Marker List Phenotype Rank genes according to their “correlation” with the class of interest. Test if a gene set (e.g., a GO category, a pathway, a different class signature), “enriches” any of the classes. Use Kolmogorov-Smirnoff score to measure enrichment. Subramanian et al., PNAS 2005

Enriched Gene Set Un-enriched Gene Set Enrichment Score S Max. Enrichment Score ES Gene List Order Index Enrichment Score S Max. Enrichment Score ES Gene List Order Index Every hit go up by 1/N H Every miss go down by 1/N M The maximum height provides the enrichment score Enrichment: KS-score

Performing GSEA Like differential expression, select a dataset and define classes GSEA uses the c2 curated gene sets representing metabolic and signaling pathways (

GSEA Results

Exercise 5. GSEA 1.Go to the portal home page 2.Select the ‘Analyses’ menu 3.Select the GSEA button, click ‘Next step’ 4.Create a Sample Set, Select ‘YFV_2008…’, click ‘Create Sample Set’ 5.For Class 1, Click ‘neutralizing antibody titer’ and the range For Class 2, Click ‘neutralizing antibody titer’ and the range Enter a name and description, 8.Click ‘Run Analysis’ 9.Open results from ‘My Shelf’ when complete

Managing ‘My Shelf’

Exercise 6. Review your shelf 1.Click on the ‘My Shelf’ button at the top right 2.Click on the ‘Analyses’ tab -Review the analyses you did earlier - revisit the results 3.Click on the ‘Sample Sets’ tab Review the Sample Sets you created for CMS, GSEA 4.Click on the ‘Profile’ tab Review your and group memberships

Review of Basic Features Overview –features and navigation Browsing data –Files and Samples Gene Summary pages Performing Analyses on the portal –Co-expression, differential expression, GSEA Managing your shelf