patrick glenisson integrating scientific literature with large scale gene expression analysis...

Genes and Microarrays

Patrick Glenisson

Integrating Scientific Literature With

Large Scale Gene Expression Analysis

December 21th 2004

Overview

Overview

Genes & microarrays

Gene expression data analysis

Text mining in biology: principles

Text mining in practice: TXTGate

Combining text and gene expression data

Conclusion

Overview M-score

Overview

Genes & microarrays





Conclusion

Cluster analysis

Overview

Overview

Genes & microarrays





Conclusion

Literature analysis

Overview

Overview

Genes & microarrays





Conclusion

TXTGate

Overview

Overview

Genes & microarrays





Conclusion

&

Integrated clustering

Overview

Overview

Genes & microarrays





Conclusion

&


DNA, genes, proteins and cells


DNA, genes, proteins and cells

protein


Genes are expressed and regulated


Microarrays measure gene expression

Laser

excitation

Gen

es

Gene expression measurement

Conditions

G1G2G3

..

C1

C2

C3 ..

Sample annotations

Gen

e an

no

tati

on

s


Representing expression information

Gene expression experiments are complex : Too verbose to include in a scientific publication Too important to compromise on reproducibility Too valuable for post-genome research to have it scattered

around on various websites

Necessary level detail for reproducibility / data mining ? Hence, standard for reporting on MA experiments As a guideline for databases hosting expression

compendia

Conditions in which

expression occurs


Storing gene expression data


MIAME standard

Minimum Information About a MicroArray Experiment Internationally proposed standard Published in Dec 2001 by International consortium MGED prominent journals (Nature, Lancet, EMBO, Cell) require

MIAME-compliant submissions of data

Some hurdles: Significant overhead in filling out the questionnaire Scooping of leads (!) Proprietary information about probe sequences Query-enabled >< comparable (cfr. Affy vs cDNA)


Impression on MIAME’s content


Dissemination of gene expression data

publications

repositories

Overview

Overview

Genes & microarrays





Conclusion

&


Questions asked with microarrays

Fundamental Functional roles of genes (and transcriptional regulation) Genetic network reconstruction

Clinical Correlation of genes with a given disease Diagnosis of disease stage with patients

Pharmacological Toxicological drug response assessment


Microarray data analysis

Fundamental Functional roles of genes (and transcriptional regulation) Genetic network reconstruction

Clinical Correlation of genes with a given disease Diagnosis of disease stage with patients

Pharmacological Toxicological drug response assessment


ClusteringConditions

Gen

es

Expression data C1

C3

C2G

enes

Genes

Distance matrix

Clustering

Hierarchical clustering

k - Means


Data-centered statistical scores

Coherence vs separation of clusters

Stability of a cluster solution when leaving out data

Cluster validation

Define `optimal’ ?

Optimal number of clusters ?

C1

C3

C2

E.g. SILHOUETTE


Cluster validation – stability method



Knowledge-based scores

Enrichment of GO annotations in clusters

Literature-based scoring

Cluster validation




Cluster validation




Knowledge-based scores

Motif-based

DNA patterns in regulatory regions of gene groups

Regulatory DNApatterns (motifs)

Gene

Genes expression data analysis

DNA patterns in expression clusters

‘Significant’ occurrences of known motifs in cluster

Mo

tifs

Clusters

Cluster-by-Motif(motif enrichment matrix)

1 2 3 ..

A

B

C

..

-log(p-value)

M-score

Gene clusters


Cluster-by-motif matrix

cluster

mot

if M-Score for the entire clustering solution one-shot estimate of the `biological relevance’


M-score

A motif is less interesting when it (significantly) occurs in many clusters

A cluster that contains a large portion of (significant) motifs is less likely to be biologically relevant.

A `too large' number of clusters is less likely to reflect the true biological

diversity underlying the experiment.


M-score validation

A simplification of reality

No absolute quantification of biological relevance.

Useful tool when experimenting with

• Multiple clustering methods

• Multiple parameterizations

To economize on biological validations

Optimal k in yeast cell cycle expression data

Original studies by Tavazoie et al. used k=30

Overestimation confirmed by analyses of

• De Smet et al. (AQBC)

• Gibbons et al. (GO-based scoring)

k

M-s

core

Overview

Overview

Genes & microarrays





Conclusion

Text Mining: principles

Problem setting

Given a set of documents,

compute a representation, called index

to retrieve, summarize, classify or cluster them

<1 0 0 1 0 1>

<1 1 0 0 0 1>

<0 0 0 1 1 0>


Problem setting

Given a set of genes (and their literature),

compute a representation, called gene index

to retrieve, summarize, classify or cluster them

<1 0 0 1 0 1>

<1 1 0 0 0 1>

<0 0 0 1 1 0>


Vector space model Document processing

Remove punctuation & grammatical structure (`Bag of words’) Define a vocabulary

• Identify Multi-word terms (e.g., tumor suppressor) (phrases)• Eliminate words low content (e.g., and, thus, gene, ...) (stopwords)• Map words with same meaning (synonyms)• Strip plurals, conjugations, ... (stemming)

Define weighing scheme and/or transformations (tf-idf,svd,..)

Compute index of textual resources:

T 1

T 3

T 2

vocabulary

gene


Validity of gene index

Genes that are functionally related should be close in text space:

Modeled wrt a background distribution of through random and permuted gene groups

Text-based coherence score



Genes that are functionally related should be close in text space:



“Simple word vector representations are competitive also in terms of classification task with respect to more elaborate approaches ..”

..despite unaddressed issues such as phrases homonyms neglected grammatical structure

A. Seewald: Ranking for BioMinT: Investigating performance, local search and homonymy recognition.

>> www.biomint.org

Overview

Overview

Genes & microarrays





Conclusion

TXTGate

TXTGate - a platform to profile groups of genes

Motivation 1

“ Until now it has been largely overlooked that there is little difference between retrieving a MEDLINE abstract and downloading an

entry from a biological database ” (M. Gerstein, 2001)

12133521VEGF is associated with the development and prognosis of colorectal cancer.

12168088PTEN modulates angiogenesis in prostate cancer by regulating VEGF expression.

11866538 Vascular endothelial growth factor modulates the Tie-2:Tie-1 receptor complex

GeneRIFGO

• cell proliferation

• heparin binding

• growth factor activity


Motivation 2

Controlled vocabularies are of great value when constructing interoperable and computer-parsable systems.

A number of structured vocabularies have already arisen:• Gene Ontology (GO)• MeSH• eVOC

Standards are systematically being adopted to store biological concepts or annotations: • HUGO• GOA@EBI


Motivation 3

(Figure courtesy: S. Van Vooren)


Development of text mining platform

a platform that offers multiple ‘views’ on vast amounts of (gene-based) free-text information available in selected curated database entries & linked scientific publications.

incorporates term-based indices ..

.. and use them as a starting point to explore the text through the eyes of different domain vocabularies to link out to other resources by query building, or to sub-cluster genes based on text.


Illustration: sub-clustering Eisen et al. (1998)


Illustration: profiling Chaussabel et al. (2003)


TXTGate: towards closing the KD loop

Profile

Distance matrix &Clustering

Other vocabulary


TXTGate – a case study Gene modules over various expression data sets

Reported two sub modules of TCA cycle

Two ‘new’ genes ACN9 & CAT8 in module 2

Visualize with BioLayout / LGL

Overview

Overview

Genes & microarrays





Conclusion

&

Fusion of text and expression data

Problem setting

“How can we analyze data in an integrated fashion to extract more information than solely from

expression data ? ”


Various ways to integrate data


In each information space

Appropriate preprocessing Choice of distance measures

Integration of text and data



Combine data:

confidence attributed to either of the two data types

in case of distance, we can see it as a scaling constant between the norms of the data- and text representations.



However, distribution of distances invoke a bias Scaling problem

Therefore, use technique from statistical meta-analysis (so-called omnibus procedure)

Expression Distance

histogram Text Distance

histogram


Overview meta-clustering

M-score

Clustering


Integration improves M-score

M-score expression data only

M-s

core

int e

gra t

ed c

lust

e rin

gVarious cutoffs k of the cluster tree

Optimal k ?


A look inside the integration


A look inside the integration

Expression Profile Text Profile

Strongre-enforcement

Overview

Overview

Genes & microarrays





Conclusion

&

Conclusion

Contributions

Representation of a gene expression experiment MIAME Laboratory Information Management System v. at the VIB

MicroArray Facility Gene expression analysis

Iterative clustering to determine optimal k M-score

Text-based gene representation To represent functional information about genes To score gene groups based on literature To cluster genes based on literature

TXTGate text mining application To profile, in an flexible and interactive manner, gene groups from

different ‘views’ Integration of text and expression data in clustering

Conclusion

Semantically-oriented text mining representations Algorithm-based: ( cfr Bioinformatics)

• Improved phrases (word co-locations)• Latent Semantic Indexing• concept clustering, bi-clustering

Knowledge based: ( cfr. PSB 2005 conference)• Gene Ontology distance in a taxonomy• Basic natural language processing + statistics = Shallow Parsing

Advanced ways of integrating data Combine link information with term information Ways to determine

Future work

Conclusion

References

http://www.esat.kuleuven.ac.be/~sistawww/cgi-bin/pub.pl

http://www.esat.kuleuven.ac.be/~dna/BioI/

PapersTechnical ReportsPhD thesis (references to datasets, tools,..)

Questions

??


TXTGate – final considerations

Flexible tool for analyzing gene groups (~200 genes) due to various term- and gene-centric vocabularies

… that allow some level of interoperability with external annotation databases

Sub-clustering gene groups useful to detect biological sub-patterns, or, shortcomings of the text representation.

Reasonably robust to corrupted groups

Gene index normalizes for unbalanced references and handles multiple gene function by ‘overruling’


Representing expression information

Rationale: Gene expression experiments are a chain of biotechnological

operations, protocols and data processing steps Too verbose to include in a scientific publication Too important to compromise on reproducibility Too valuable for post-genome research to have it scattered

around on various websites

Standards for reporting on MA experiments MIAME-compliant databases hosting expression

compendia

Conditions in which

expression occurs


Clustering parameterization

Clustering

Hierarchical clustering

k - Means



Data-centered statistical scores exist (Gap-statistic, FOM, Silhouette coefficient,…)

… but built on data that produced the result, not necessarily biologically

relevant

Knowledge-based (GO- or text-based) scores (Neighborhood divergence, Gibbons et al.)

… but cyclic confirmations of truth ( As will be explained later

on…)


Optimal k by looking at DNA patterns

Evaluation : we constructed a motif-based heuristic

in terms of upstream regulatory sequence patterns in clusters,

To have a one-shot estimate of the `biological relevance’ ofa clustering result.


TXTGate

multiple ‘views’ (through use of different vocabularies) on vast amounts of (gene-based) free-text information available in selected curated

database entries & linked scientific publications.


TXTGate

incorporates term-based indices ..(cfr before)

.. and use them as a starting point

to explore terms generated through different domain vocabularies to link out to other resources by query building, or to sub-cluster genes based on text.


TXTGate – case 2


How to construct a gene index

Gene index

Documentindex

Gene-literature

associations


TXTGate – case 1

Gene clusters from microarray experiment on human immune response

Comparative study with Chaussabel et al.

TXTGate’s disease vocabulary


Various ways to integrate data

patrick glenisson integrating scientific literature with large scale gene expression analysis...

Documents

gene expression data

microarrays genes

principles text mining

reproducibility data

microarrays dna

microarrays impression

regulated slide

cells protein slide