A matrix rank based concordance index for evaluating and detecting conditional specific co-expressed gene modules

Han, Zhi; Zhang, Jie; Sun, Guoyuan; Liu, Gang; Huang, Kun

doi:10.1186/s12864-016-2912-y

Cited by 16 publications

(20 citation statements)

References 25 publications

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“…1). To compare the modules identified by our method with the popularly used WGCNA [15], we also applied WGCNA to the same datasets in our work to mine densely correlated modules. Number of modules and module size range are listed in Table 2 for both lmQCM and WGCNA method.…”

Section: Resultsmentioning

confidence: 99%

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients

et al. 2018

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BackgroundGene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions.MethodsIn this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules.ResultsWe obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology.ConclusionsThrough our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0431-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients

et al. 2018

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“…The original z-score as well as the modified DC score only take the absolute value of a correlation coefficient into account. In order to determine if the module gains or loses correlation in AD versus normal condition, we also measured the correlation change using our previously developed metric Centered Concordance Index (CCI, Han et al, 2016). CCI ranges from 0 to 1, and the larger the CCI value, the stronger the genes are correlated within a module.…”

Section: Differential Co-expression (Dc) Measurement Of Gcn Modules Bmentioning

confidence: 99%

Combinatorial analyses reveal that cellular composition changes have different impact on transcriptomic changes of cell type specific genes in Alzheimer’s Disease brains

Xiang

Johnson

Dong

et al. 2020

Preprint

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Background Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis which involves mostly microglia and astrocytes. Comparative transcriptomic analysis on AD vs. normal brain tissues helps to identify key genes/pathways involved in AD initiation and progression. However, many such studies using bulk brain tissue samples have not considered cell composition changes in AD brains, which may lead to expression changes that are not due to transcriptional regulation. Methods Using five large transcriptomic datasets including 1,681 brain tissue samples (882 AD, 799 normal) in total, we first mined frequent co-expression network modules across them, then combined differential expression and differential co-expression analysis on the mined modules in AD versus normal brains. Integrated with cell type deconvolution analysis, we addressed the question of whether the module expression changes are due to altered cellular composition or transcriptional regulation. We then used four additional large AD/normal transcriptomic datasets to validate our findings. Results The integrative analysis revealed highly elevated expression level of microglia modules in AD without co-expression change. Decreased expression and elevated co-expression are observed for neuron modules in AD, while significant over-expression and co-expression perturbation are observed in astrocyte modules, all of which has not been previously reported. The expression levels of astrocyte modules also show the strongest correlation with the clinicopathological biomarkers among all cell type specific modules. Conclusion Further analysis indicated that the overall increased expression of the core microglia modules can be well explained by the increased microglia cell population in AD brains instead of bona fide microglia genes’ upregulation. In contrast, the decreased expression and perturbed co-expression in AD neuron modules are due to both neuron cell loss and expression regulation of neuronal pathways including differentially expressed transcription factors such as BCL6 and STAT3, which previous study was not able to identify from the shadow of the cellular composition change. Similarly, the strong changes in expression and co-expression in the astrocyte modules may be also due to a combinatory effect from astrogliosis and astrocyte gene activation in AD brains. In this work, we demonstrated that the combinatorial analyses not only provide a powerful approach to delineate the origin of transcriptomic changes in bulk tissue data, but also lead to a deeper understanding of genes in AD.

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“…To further verify the co-expression relationships among genes within the microglia module, we used another correlation metric, centered concordance index (CCI) (Han et al, 2016), to evaluate the microglia module's gene inter-correlation in each of the five datasets separately. The CCI values of AD versus control samples align very closely to the diagonal line in the upper right corner, which are high in both conditions, indicating these genes are highly correlated with each other in both AD and control samples in all of the five studies ( Figure 3b).…”

Section: The Highly Differential Expression In Microglia Modules In Hmentioning

confidence: 99%

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease

Johnson

Xiang

Dong

et al. 2020

Preprint

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Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies comparing AD versus control using bulk brain tissue samples have not considered cell composition changes in AD brains that can cause transcriptional changes not due to transcriptional regulation.Using five large transcriptomic datasets, we mined conserved gene co-expression network modules, and applied differential expression and differential co-expression analysis on the modules in AD versus control brains. Combined with cell type deconvolution analysis, we addressed the question of whether the module expression changes are due to altered cellular composition or transcriptional regulation. Our findings were validated using four additional datasets.We discovered that the increased expression of microglia modules can be explained by increased microglia population in AD brains rather than gene upregulation. In contrast, the decreased expression and perturbed co-expression in AD neuron modules are due to both neuron loss and regulation of neuronal pathways and several transcriptional factors are identified for such regulation. Similarly, the strong changes in expression and co-expression in astrocyte modules can also be attributed to a combinatory effect from astrogliosis and astrocyte gene activation in AD brains. The astrocyte modules expressions also strongly correlated with the clinicopathological biomarkers.In summary, we demonstrated that combinatorial analysis is a powerful approach to delineate the origin of transcriptomic changes in bulk tissue data, which leads to a deeper understanding of key genes/pathways in AD.

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A matrix rank based concordance index for evaluating and detecting conditional specific co-expressed gene modules

Cited by 16 publications

References 25 publications

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients

Combinatorial analyses reveal that cellular composition changes have different impact on transcriptomic changes of cell type specific genes in Alzheimer’s Disease brains

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease

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