Basic features for portal users. Agenda - Basic features Overview –features and navigation Browsing data –Files and Samples Gene Summary pages Performing.
Post on 29-Jan-2016
228 Views
Preview:
Transcript
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
http://www.humanimmunology.org/cchi
Overview - organization
http://www.humanimmunology.org/cchi
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
http://www.humanimmunology.org/cchi
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 browserhttp://www.humanimmunology.org/cchi
2. Login/register if needed3. Click on the ‘BROWSE’ menu item
Then the ‘DATA’ submenu 4. Uncheck the ‘Sample annotation’ and ‘undefined’ filter
checkboxes5. Click on the ‘BROWSE’ menu item again
then the ‘SAMPLES’ submenu1. Select a dataset to browse2. 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 boxe.g. EGFR, FGFR3
2. Click a gene name on the results page3. 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 modulesCo-expression -> Gene NeighborsDiff. Expression -> Comparative Marker SelectionGene 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 0.49-1.6
6. For Class 2, Click ‘Tcell activation’ and the range 9-12.1
7. 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
Enric
hmen
t Sco
re 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
Enric
hmen
t Sco
re S
Max. Enrichment Score ES
Gene List Order Index
Enric
hmen
t Sco
re S Max.
Enrichment Score ES
Gene List Order Index
Every hit go up by 1/NH
Every miss go down by 1/NM
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 (http://www.broadinstitute.org/gsea/msigdb)
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
482-1280
6. For Class 2, Click ‘neutralizing antibody titer’ and the range 20-280
7. Enter a name and description,
8. Click ‘Run Analysis’9. Open results from ‘My Shelf’ when complete
Managing ‘My Shelf’
http://www.humanimmunology.org/cchi
Exercise 6. Review your shelf
1. Click on the ‘My Shelf’ button at the top right2. Click on the ‘Analyses’ tab
-Review the analyses you did earlier- revisit the results
3. Click on the ‘Sample Sets’ tabReview the Sample Sets you created for CMS, GSEA
4. Click on the ‘Profile’ tabReview your email 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
top related