Feng, Yaping
(Development and Cell Biology, Department of Genetics, Iowa State University)
,
Syrkin-Nikolau, Judith A.
(Macalester College)
,
Wurtele, Eve S.
(Development and Cell Biology, Department of Genetics, Iowa State University)
High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 h...
High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.
High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.
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제안 방법
In one approach, the overall 18637Hu-matrix is used to identify additional CNS-expressed genes. In a second approach, we derive a network from within the CNS samples in the context of the larger matrix, and evaluated the overrepresentation of functional significance of regulons calculated from this network relative to that of the regulons derived from the 18637Hu-matrix.
The combined set of outlier-networks for glioblastomas and astrocytoma was evaluated to identify the abnormally expressed genes, and the associated samples, in the context of the 18637Hu-matrix. In this subnetwork, 48 gene clusters are naturally separated, with 23 gene pairs having 3 or more genes (Figure 4).
To compare the quality of the correlations in the smaller CNS dataset relative to the overall 18637Hudataset, we used the CNS dataset to calculated pairwise Pearson correlations from the > 22,000 transcripts measured.
Figure 2. Using the brain-specific Regulon 56 as a hub within the context of the 18367Hu-matrix correlation network to identify genes that are preferentially expressed in the CNS. The 18367Hu-matrix is comprised of the pairwise Pearson correlations for 22,215 human genes correlated across 18,637 human tissue samples1.
대상 데이터
The X-axis shows the data from individual microarray samples. The data is visualized and sorted by sample metadata using MOG (http://www.metnetdb.org/MetNet_MetaOmGraph.htm).
데이터처리
To delineate unique perturbations in gene expression within a particular disease condition, in this case glioblastomas or astrocytomas, we combined the 18637Hu-dataset network with data from the glioblastoma or astrocytoma samples to identify genes that are expressed to unusually high levels in only particular of the samples. To do this, the expression data from the 324 glioblastoma samples in the 18637Hu-dataset (Figure 1) were normalized by MAD and Mean100 and used to calculate pairwise Pearson correlations; the resultant matrices are referred to as Pcorr.Glio324.
Two selection criteria were used to identify genes that represent gliocytoma outliers. A set of Pearson correlation coefficients was determined for the subset of samples used in this analysis. Selection Criterion 1: PcorrGlio324 > 0.
이론/모형
The samples are further classified according to more details within the sample metadata. The sorting and visualization was conducted using the publicly available software, MetaOmGraph (MOG http://www.metnetdb.org/MetNet_MetaOm-Graph.htm).
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