# 统计代写|网络分析代写Network Analysis代考|CSCl5352

## 统计代写|网络分析代写Network Analysis代考|Network module detection

A set of correlated and coexpressed genes, often referred as a functional module, play a synergistic role during any disease or any biological activity. Genes participating in a common module may cause clinically similar diseases and shares the common genetic origin of their associated disease phenotypes. Identifying such modules may be helpful in system-level understanding of biological and cellular processes or the pathophysiologic basis of associated diseases. Formally, we can define a network module as follows:

Definition 6.5.1 (Network module). Given a network $\mathcal{G}$, a network module $\mathcal{M}_i=\left{\mathcal{V}^{\prime}, \mathcal{E}^{\prime}\right}$ is a densely connected subgraph of $\mathcal{G}$ $\left(\mathcal{M}_i \subseteq \mathcal{G}\right)$, where interconnectivity of $\mathcal{V}^{\prime}$ with respect to $\mathcal{E}^{\prime} \subseteq \mathcal{E}$ is higher in comparison to the rest of $\mathcal{V}$, i.e., $\mathcal{V}-\mathcal{V}^{\prime}$.

The first step in this analysis is the building of (weighted or unweighted) graph starting from experimental data. Next, a network module or community detection method is applied. Community discovery algorithm may be categorized using different parameters [24], e.g., on the nature of discovered modules (overlapping or not), on their structure (densely connected subgraph, graphlet-based). Here, we do not propose any other classification, and we selected some state-of-the-art algorithms, and we categorized them into two broad classes: (i) algorithms developed specifically for gene expression analysis, and (ii) algorithm for network analysis that may be used for such networks.

WGCNA [37] is a popular method to detect modules from gene networks. It receives the coexpression network as input representing correlations, and it applies a soft thresholding to remove the possibility of non-relevant edges under the hypothesis that communities are made of relevant edges. After the thresholding, it employs a fuzzy approach to extract (possibly overlapping) mod- ules without any hypothesis on the internal structure.

## 统计代写|网络分析代写Network Analysis代考|Ranking key diseased genes using network analysis

To study the causes of complex diseases, researchers focus on detecting subnetwork of functionally interrelated genes forming a functional module. However, not all the genes within a module play key roles in disease formation. Rather, a very few genes are the pivotal genes. The latter are called marker genes. They are responsible for disrupting the normal cellular functionalities, causing diseases. They are often identified as transcription factor (TF) genes. TF binds with the promoter region of target genes and lead to abnormal expression of the genes. Identifying such key genes responsible for the formation of disease networks may help in designing disease-specific drugs. A number of prioritization schemes have been proposed in different literature. Majority of them adopt centrality analysis of the disease subnetworks. It has been observed that the outcome of such biomarker ranking or prioritization scheme is sensitive towards the input network.
Detection of marker genes responsible for a genetic disease is a difficult task. Many researchers have dedicated their work in detecting such genes using various ranking techniques. Cluvian [43] identifies key genes that are possibly responsible for Alzheimer’s disease by analyzing modules derived from Alzheimer’s disease (AD) coexpression networks. The networks first extract AD submodules and rank them based on $\mathrm{AD}$ pathway enrichment scores. Top ranked modules are further analyzed topologically to identify central or hub genes, which are the potential key genes responsible for AD. In [39,48], they devised a ranking scheme using varied correlation measurements for the improvement of microarray and RNA-seq-based global and targeted coexpression networks. In addition to ordering genes based on fold change across the data, they also consider all three cell type-associated measures.

## 统计代写|网络分析代写网络分析代考|网络模块检测

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WGCNA[37]是一种从基因网络中检测模块的常用方法。该算法将共表达式网络作为表示相关性的输入，在假设社区是由相关边组成的前提下，采用软阈值去除非相关边的可能性。阈值化后，在不对内部结构作任何假设的情况下，采用模糊方法提取(可能重叠的)模。

## 统计代写|网络分析代写网络分析代考|使用网络分析对关键患病基因进行排序

.使用网络分析对关键患病基因进行排序

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