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Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.

de Tayrac M, Lê S, Aubry M, Mosser J, Husson F - BMC Genomics (2009)

Bottom Line: Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge.When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings.Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS UMR 6061, Université de Rennes 1, IFR 140, Faculté de Médecine, CS 34317, 35043 Rennes, France. marie.de-tayrac@univ-rennes1.fr

ABSTRACT

Background: Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.

Results: Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.

Conclusion: When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.

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Related in: MedlinePlus

Single glioma data set: MFA highlights a good separation between glioblastomas and lower grade gliomas. Tumors are presented as points on the scatter plot created with the first two main dimensions of MFA. Each sample (dot) is colored following the glioma subtype (WHO classification); mean individual are also displayed (squares). Projection of the tumors onto PC1 underlines a separation between glioblastomas (GBM) and lower grade gliomas (oligodendrogliomas, astrocytomas, oligoastrocytomas). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (oligodendrogliomas, astrocytomas, GBM). PC1 is linked to characteristics of glioblastoma stating transcriptional differences between grade IV and lower grade gliomas. PC2 is related to characteristics of oligodendrogliomas highlighting OA particular signatures.
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Figure 6: Single glioma data set: MFA highlights a good separation between glioblastomas and lower grade gliomas. Tumors are presented as points on the scatter plot created with the first two main dimensions of MFA. Each sample (dot) is colored following the glioma subtype (WHO classification); mean individual are also displayed (squares). Projection of the tumors onto PC1 underlines a separation between glioblastomas (GBM) and lower grade gliomas (oligodendrogliomas, astrocytomas, oligoastrocytomas). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (oligodendrogliomas, astrocytomas, GBM). PC1 is linked to characteristics of glioblastoma stating transcriptional differences between grade IV and lower grade gliomas. PC2 is related to characteristics of oligodendrogliomas highlighting OA particular signatures.

Mentions: We focus on the first two principal components that explain 47.1% of the total variability carried by the 615 genes. The corresponding individuals factor map is provided in Figure 6. Mean observations are added for each glioma subtype to help with the interpretation of the plot. This map shows a relatively well-defined partition of tumors into WHO classification. It also shows that the position of the samples belonging to a glioma subtype varies from one to another. This variability could be assigned to the well known cellular heterogeneity of gliomas and particularly of glioblastomas. It could also be the result of the WHO classification that is somehow controversial: this standard classification is said to suffer from a lack of reproducibility among pathologists [25]. The projections on PC1 of the mean observations underline that the maximum of variability captured in the analysis separates glioblastomas (GBM) from lower grade gliomas (O, A, OA). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (O, A, GBM). As a result, PC1 is linked to glioblastoma characteristics stating transcriptional differences between grade IV and lower grade gliomas and PC2 is related to oligoastrocytoma characteristics, highlighting OA particular signature.


Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.

de Tayrac M, Lê S, Aubry M, Mosser J, Husson F - BMC Genomics (2009)

Single glioma data set: MFA highlights a good separation between glioblastomas and lower grade gliomas. Tumors are presented as points on the scatter plot created with the first two main dimensions of MFA. Each sample (dot) is colored following the glioma subtype (WHO classification); mean individual are also displayed (squares). Projection of the tumors onto PC1 underlines a separation between glioblastomas (GBM) and lower grade gliomas (oligodendrogliomas, astrocytomas, oligoastrocytomas). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (oligodendrogliomas, astrocytomas, GBM). PC1 is linked to characteristics of glioblastoma stating transcriptional differences between grade IV and lower grade gliomas. PC2 is related to characteristics of oligodendrogliomas highlighting OA particular signatures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2636827&req=5

Figure 6: Single glioma data set: MFA highlights a good separation between glioblastomas and lower grade gliomas. Tumors are presented as points on the scatter plot created with the first two main dimensions of MFA. Each sample (dot) is colored following the glioma subtype (WHO classification); mean individual are also displayed (squares). Projection of the tumors onto PC1 underlines a separation between glioblastomas (GBM) and lower grade gliomas (oligodendrogliomas, astrocytomas, oligoastrocytomas). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (oligodendrogliomas, astrocytomas, GBM). PC1 is linked to characteristics of glioblastoma stating transcriptional differences between grade IV and lower grade gliomas. PC2 is related to characteristics of oligodendrogliomas highlighting OA particular signatures.
Mentions: We focus on the first two principal components that explain 47.1% of the total variability carried by the 615 genes. The corresponding individuals factor map is provided in Figure 6. Mean observations are added for each glioma subtype to help with the interpretation of the plot. This map shows a relatively well-defined partition of tumors into WHO classification. It also shows that the position of the samples belonging to a glioma subtype varies from one to another. This variability could be assigned to the well known cellular heterogeneity of gliomas and particularly of glioblastomas. It could also be the result of the WHO classification that is somehow controversial: this standard classification is said to suffer from a lack of reproducibility among pathologists [25]. The projections on PC1 of the mean observations underline that the maximum of variability captured in the analysis separates glioblastomas (GBM) from lower grade gliomas (O, A, OA). PC2 differentiates oligo-astrocytomas (OA) from the other subtypes (O, A, GBM). As a result, PC1 is linked to glioblastoma characteristics stating transcriptional differences between grade IV and lower grade gliomas and PC2 is related to oligoastrocytoma characteristics, highlighting OA particular signature.

Bottom Line: Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge.When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings.Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS UMR 6061, Université de Rennes 1, IFR 140, Faculté de Médecine, CS 34317, 35043 Rennes, France. marie.de-tayrac@univ-rennes1.fr

ABSTRACT

Background: Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.

Results: Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.

Conclusion: When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.

Show MeSH
Related in: MedlinePlus