 PhD defense Patrick Glenisson Integrating Scientific Literature With Large Scale Gene Expression Analysis Promotor Prof. Bart De Moor June 11 th 2004

 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  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  Cluster analysis

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  Literature analysis

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  TXTGate

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  & Integrated clustering

 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  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  &

 Genes and Microarrays DNA, genes, proteins and cells

 Genes and Microarrays DNA, genes, proteins and cells protein

 Genes and Microarrays Genes are expressed and regulated

 Genes and Microarrays Microarrays measure gene expression Laser excitation Genes Gene expression measurement Conditions G1 G2 G3.. C1 C2C3.. Sample annotations Gene annotations

 Genes and Microarrays 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  Hence, standard for reporting on MA experiments  As a guideline for databases hosting expression compendia Conditions in which expression occurs

 Genes and Microarrays MIAME standard  Minimum Information About a MicroArray Experiment  Internationally proposed standard  Published in Dec 2001 by International consortium MGED  Some 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

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion  &

 Gene expression data analysis 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

 Gene expression data analysis 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

 Gene expression data analysis Clustering Conditions Genes Expression data C1 C3 C2 Genes Distance matrix Clustering Hierarchical clustering k - Means

 Gene expression data analysis  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

 Gene expression data analysis  Data-centered statistical scores  Knowledge-based scores  Enrichment of GO annotations in clusters  Literature-based scoring Cluster validation Define `optimal’ ? Optimal number of clusters ?

 Gene expression data analysis Cluster validation Define `optimal’ ? Optimal number of clusters ?  Data-centered statistical scores  Knowledge-based scores  Motif-based  DNA patterns in regulatory regions of gene groups Regulatory DNA patterns (motifs) Gene

 Genes expression data analysis DNA patterns in expression clusters Significant occurrences of known motifs in cluster Motifs Clusters Cluster-by-Motif (motif enrichment matrix) A B C.. -log(p-value) M-score Gene clusters

 Genes expression data analysis Cluster-by-motif matrix cluster motif M-Score for the entire clustering solution  one-shot estimate of the `biological relevance’

 Gene expression data analysis 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.

 Gene expression data analysis 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-score

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  Conclusion 

 Text Mining: principles Problem setting  Given a set of documents,  compute a representation, called index  to retrieve, summarize, classify or cluster them 

 Text Mining: principles Problem setting  Given a set of genes (and their literature),  compute a representation, called gene index  to retrieve, summarize, classify or cluster them 

 Text Mining: principles 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

 Text Mining: principles 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

 Text Mining: principles Validity of gene index Genes that are functionally related should be close in text space:

 Text Mining: principles Validity of gene index Genes that are functionally related should be close in text space:

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  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) VEGF is associated with the development and prognosis of colorectal cancer PTEN modulates angiogenesis in prostate cancer by regulating VEGF expression Vascular endothelial growth factor modulates the Tie- 2:Tie-1 receptor complex GeneRIF GO cell proliferation heparin binding growth factor activity

 TXTGate - a platform to profile groups of genes 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

 TXTGate - a platform to profile groups of genes Motivation 3 (Figure courtesy: S. Van Vooren)

 TXTGate - a platform to profile groups of genes TXTGate Profile Distance matrix & Clustering Other vocabulary

 TXTGate - a platform to profile groups of genes 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

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  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 ? ”

 Fusion of text and expression data  In each information space  Appropriate preprocessing  Choice of distance measures Integration of text and data

 Fusion of text and expression data 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.

 Fusion of text and expression data Integration of text and data  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

 Fusion of text and expression data Overview meta-clustering M-score Clustering

 Fusion of text and expression data Integration improves M-score M-score expression data only M-score integrated clustering Various cutoffs k of the cluster tree Optimal k ?

 Fusion of text and expression data A look inside the integration

 Fusion of text and expression data A look inside the integration Expression Profile Text Profile Strong re-enforcement

 Overview  Genes & microarrays  Gene expression data analysis  Text mining in biology: principles  Text mining in practice: TXTGate  Combining text and gene expression data  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: Improved phrases (word co-locations) Latent Semantic Indexing concept clustering, bi-clustering  Knowledge based: 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 Publications

 Questions ? ?

 TXTGate - a platform to profile groups of genes 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’

 Genes and Microarrays 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

 Gene expression data analysis Clustering parameterization Clustering Hierarchical clustering k - Means Optimal number of clusters ?Define `optimal’ ?  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…)

 Genes expression data analysis 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’ of a clustering result.

 TXTGate - a platform to profile groups of genes 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 - a platform to profile groups of genes 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 - a platform to profile groups of genes TXTGate – case 2

 Text Mining: principles How to construct a gene index Gene index Document index Gene-literature associations

 TXTGate - a platform to profile groups of genes TXTGate – case 1  Gene clusters from microarray experiment on human immune response  Comparative study with Chaussabel et al.  TXTGate’s disease vocabulary

 Fusion of text and expression data Various ways to integrate data