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From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems.

Geistlinger L, Csaba G, Küffner R, Mulder N, Zimmer R - Bioinformatics (2011)

Bottom Line: Current gene set enrichment approaches do not take interactions and associations between set members into account.Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods.Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de.

View Article: PubMed Central - PubMed

Affiliation: Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de

ABSTRACT

Motivation: Current gene set enrichment approaches do not take interactions and associations between set members into account. Mutual activation and inhibition causing positive and negative correlation among set members are thus neglected. As a consequence, inconsistent regulations and contextless expression changes are reported and, thus, the biological interpretation of the result is impeded.

Results: We analyzed established gene set enrichment methods and their result sets in a large-scale investigation of 1000 expression datasets. The reported statistically significant gene sets exhibit only average consistency between the observed patterns of differential expression and known regulatory interactions. We present Gene Graph Enrichment Analysis (GGEA) to detect consistently and coherently enriched gene sets, based on prior knowledge derived from directed gene regulatory networks. Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods. Secondly, GGEA yields result sets where a large fraction of relevant expression changes can be explained by nearby regulators, such as transcription factors, again improving on set-based methods. Thirdly, we demonstrate in additional case studies that GGEA can be applied to human regulatory pathways, where it sensitively detects very specific regulation processes, which are altered in tumors of the central nervous system. GGEA significantly increases the detection of gene sets where measured positively or negatively correlated expression patterns coincide with directed inducing or repressing relationships, thus facilitating further interpretation of gene expression data.

Availability: The method and accompanying visualization capabilities have been bundled into an R package and tied to a grahical user interface, the Galaxy workflow environment, that is running as a web server.

Contact: Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de.

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Related in: MedlinePlus

Key Steps of GGEA. Subsequent to differential expression analysis dea of expression data Expr, yielding fuzzified measures de of differential expression, target gene sets are first mapped onto the GRN. The de-values are assigned to corresponding places in resulting induced nets (de-nets). Second, consistency scores are computed for each de-net and third, significance of the scores is estimated via re-sampling and exploited to rank the gene sets.
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Figure 1: Key Steps of GGEA. Subsequent to differential expression analysis dea of expression data Expr, yielding fuzzified measures de of differential expression, target gene sets are first mapped onto the GRN. The de-values are assigned to corresponding places in resulting induced nets (de-nets). Second, consistency scores are computed for each de-net and third, significance of the scores is estimated via re-sampling and exploited to rank the gene sets.

Mentions: Given gene regulatory information, for example extracted from biochemical pathways or a global transcriptional network, a gene set under investigation and gene expression data sampling different conditions, GGEA performs three essential steps (Fig. 1): first, the gene set is mapped onto the underlying regulatory network, yielding an induced subnetwork. That is the affected part of the network, which consists of edges that involve members of the gene set. Second, each edge of the induced network is scored for consistency with the expression data, i.e. the signs of the expression changes of two interaction partners are evaluated for agreement with the regulation type (activation/inhibition) of the link that connects both genes. Third, the edge consistencies are summed up over the induced network, normalized and estimated for significance using a permuation procedure.


From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems.

Geistlinger L, Csaba G, Küffner R, Mulder N, Zimmer R - Bioinformatics (2011)

Key Steps of GGEA. Subsequent to differential expression analysis dea of expression data Expr, yielding fuzzified measures de of differential expression, target gene sets are first mapped onto the GRN. The de-values are assigned to corresponding places in resulting induced nets (de-nets). Second, consistency scores are computed for each de-net and third, significance of the scores is estimated via re-sampling and exploited to rank the gene sets.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117393&req=5

Figure 1: Key Steps of GGEA. Subsequent to differential expression analysis dea of expression data Expr, yielding fuzzified measures de of differential expression, target gene sets are first mapped onto the GRN. The de-values are assigned to corresponding places in resulting induced nets (de-nets). Second, consistency scores are computed for each de-net and third, significance of the scores is estimated via re-sampling and exploited to rank the gene sets.
Mentions: Given gene regulatory information, for example extracted from biochemical pathways or a global transcriptional network, a gene set under investigation and gene expression data sampling different conditions, GGEA performs three essential steps (Fig. 1): first, the gene set is mapped onto the underlying regulatory network, yielding an induced subnetwork. That is the affected part of the network, which consists of edges that involve members of the gene set. Second, each edge of the induced network is scored for consistency with the expression data, i.e. the signs of the expression changes of two interaction partners are evaluated for agreement with the regulation type (activation/inhibition) of the link that connects both genes. Third, the edge consistencies are summed up over the induced network, normalized and estimated for significance using a permuation procedure.

Bottom Line: Current gene set enrichment approaches do not take interactions and associations between set members into account.Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods.Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de.

View Article: PubMed Central - PubMed

Affiliation: Institute for Informatics, Ludwig-Maximilians-Universität Münchchen, Amalienstrasse 17, 80333 München, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de

ABSTRACT

Motivation: Current gene set enrichment approaches do not take interactions and associations between set members into account. Mutual activation and inhibition causing positive and negative correlation among set members are thus neglected. As a consequence, inconsistent regulations and contextless expression changes are reported and, thus, the biological interpretation of the result is impeded.

Results: We analyzed established gene set enrichment methods and their result sets in a large-scale investigation of 1000 expression datasets. The reported statistically significant gene sets exhibit only average consistency between the observed patterns of differential expression and known regulatory interactions. We present Gene Graph Enrichment Analysis (GGEA) to detect consistently and coherently enriched gene sets, based on prior knowledge derived from directed gene regulatory networks. Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods. Secondly, GGEA yields result sets where a large fraction of relevant expression changes can be explained by nearby regulators, such as transcription factors, again improving on set-based methods. Thirdly, we demonstrate in additional case studies that GGEA can be applied to human regulatory pathways, where it sensitively detects very specific regulation processes, which are altered in tumors of the central nervous system. GGEA significantly increases the detection of gene sets where measured positively or negatively correlated expression patterns coincide with directed inducing or repressing relationships, thus facilitating further interpretation of gene expression data.

Availability: The method and accompanying visualization capabilities have been bundled into an R package and tied to a grahical user interface, the Galaxy workflow environment, that is running as a web server.

Contact: Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de.

Show MeSH
Related in: MedlinePlus