Presentation July15 08

8/14/2019 Presentation July15 08

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MeV: An overview ofcontributions/work accomplished

Prepared by: Sarita Nair


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Outline

o Annotation model integration

o Revamped MeV file loaders

o Addition of two new GEO file format support in MeV

o GSEA Algorithm and software development status


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What exactly is this Annotation model andDo we really care about it?

o MeV provided a bare bones option to the user forloading gene annotations.o User could upload any information deemeduseful, as long as it satisfied MeVs annotation file

format.

This worked fine TILLwe saw the growingpopularity of Affymetrix as a preferred platform byMeV users for microarray analysis.SO..

We came up with a solution to provide the LATESTannotation information through our popular web basedresource RESOURCERER


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Simple, step by

step instructions

Just Click and Go

Shows youwhere to findyour file

Shows the chiptypescorrespondingto the organismselected.

q User friendly interface


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Individual gene profiles can be

viewed in the SpotInformation dialog box

Future scope: Plan on linking each ofthese labels to their respective webresources

After you load theannotations.


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New!! GEO file loaders

q Re wrote the existing GEO (Gene Expression Omnibus) file loadersto make it compatible to the new NCBI file format

q Added support for two new GEO file formats; Series Matrix and GDS


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GEO files can contain multiple samples; loaders aredesigned such that the order of the probes in the sampleswithin a data file DOES NOT matter.

Flags an error to the user if probes are found to be missing

in any of the samples.

Does not require the user to load platform informationprovided by GEO separately.

User can load files which are data only or platforminformation+data using the GEO SOFT Two Color andAffymetrix file loaders


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Nowfor the revamped loaders


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Better organized and easy tounderstand the flow

NEW Format

OLD Format

click here Look here


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No moreguessing theexperiment

platform

Separate panels (File, Annotation, Expression Table etc) == easy navigation + better UI


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Spell check, people!?Affymatrix : Neverheard of that before!

Versus


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FinallyAccessible color scheme

q Added a color scheme accessible to people withcolor blindnessq Available under Display Color Scheme menu

Color palette adapted from the web based

resource:http://jfly.iam.u-tokyo.ac.jp/color/index.html
http://jfly.iam.u-tokyo.ac.jp/color/index.htmlhttp://jfly.iam.u-tokyo.ac.jp/color/index.html


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Gene Set Enrichment Analysis

( Jiang, Z., Gentleman, R., (2007). Bioinformatics.2007 Feb 1;23(3):306-13.

Extensions to gene set enrichment)


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What is the goal of GSEA?

oGSEA has been developed to capture changes in theexpression of pre-defined set of genes (Zhen Jiang and

Robert Gentleman)

o

GSEA aggregates per gene statistics across geneswithin a gene set

Are there any advantages to usingGSEA over the traditional gene-centricanalysis?


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Advantagescontd

GSEA has been found to perform reasonably in situationswhere:

oAll genes in a predefined gene set change in a small butsignificant way.

oThe effect is due to large changes in a relatively few genes


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GSEA (Gene Set Enrichment Analysis)- Method

Given a data set with N probes and k samples

2) Collapse the probes to genes. If multiple probes map to a gene, use max or median.

I have used Maxhere


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Remove probes with no gene symbols associated

5) Filter out the genes from your gene set that are

NOT present in the data.

.which brings us to What on earth aregene-sets?

GSEA (Gene Set Enrichment Analysis) Method.contd


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Gene sets can be, say a list of genes, which are:

C)Present on a chromosomeD)Annotated by the same GO termE)Present in the pathway of your interest

F) Significant genes from other experiment, ETC

MIT provides a publicly available collection of gene sets(Molecular Signatures Database --MSigDB)

..You can create your own gene sets relevant to theprocess you are investigating



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3) Calculate the per-gene statistic using LinearModel(just like you would calculate t test statistic on a gene by

gene basis)

7) Calculate the per gene-set statistic

.If you are already scratching your head..wait till you see

the ugly formula!


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Genes Gene-

sets

Matrix containing the pergene statistic ; calculatedearlier

Row sum of A = = number ofgenes/gene-set

Association Matrix

MatrixcontainingGene-setstatistic


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6) Estimate significance Permute factor labels Compute per-gene statistic for every permutation Compute the gene-set statistic Calculate and report the unadjusted p values


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GSEA software implementation


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What all would you need to run GSEA ?

o Expression data

o Group assignments

o Gene sets

o Chip annotations


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GSEA- Expression data

oExpression data should be in MeV compatible format.

oCollapse probes to genes. Current implementation uses genesymbols as the gene identifier

oRemoves probes with no gene symbols associated with them

oGSEA does NOT impute any missing values or filter out geneswith too many missing values.

oGSEA does NOT filter any genes with low expression values


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Group assignments

o Categorical labelsCurrently GSEA assumes that all the group labels are

categorical.


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GSEA Gene Sets Database

o

Available from the MIT MSigDB data base

o 3000 gene sets available- C1: Cytogenetic bands

- C2: Curated gene sets- C3: Motif gene sets- C4: Computational gene sets

o GSEA removes the genes in the gene set thatare not in the expression data


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File extension: .GMX (Tab delimited format)

MeV recognizes thisformat


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File extension: .GMT (Tab delimited)

And this one,too


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GSEA Annotation file

o MeV by default uses annotations provided byResourcerer

o User can create his/her own file and load it.as long as

it complies to the standard annotation file format used byMeV.


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Existing screen shots of GSEA


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Screen shots of GSEA


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Current status of the project

Have completed implementing all the math

Currently implementing the viewers. Plan to implement thebasic table viewer with gene set names as rows and two columnscontaining p values

Plan to release a working version some time soon


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References

o Jiang, Z., Gentleman, R., (2007). Bioinformatics. 2007 Feb1;23(3):306-13.Extensions to gene set enrichment

o GSEA User Guidehttp://www.broad.mit.edu/cancer/software/gsea/doc/GSEAUse
http://www.broad.mit.edu/cancer/software/gsea/doc/GSEAUserGuideFrame.htmlhttp://www.broad.mit.edu/cancer/software/gsea/doc/GSEAUserGuideFrame.html

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Presentation July15 08

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