Computational Systems Biology Methods and Protocols.7z
Chapter 9 Differential Coexpression Network Analysis for Gene Expression Data Bao-Hong Liu Abstract Gene expression profiling by ...
disease genes [2] and drug discovery [3, 4]. Microarray is a form of high-throughput genomic data providing relative measurement ...
Fig. 1Workflow of gene differential coexpression network analysis. First, the gene coexpression network was constructed by calcu ...
1.Hard-Thresholding-Based Coexpression Network. Value basedThere are mainly three types of value-based meth- ods to construct th ...
2.2.1 Network Topological Characteristic Comparison There are many parameters for network topology measurement which are shown i ...
connected to everyone else through a very short path. And there are some other parameters to measure the structure of the networ ...
sets. The next step is to test the coexpression difference for the above gene sets in different experiments or conditions by cal ...
2.2.3 Differential Coexpression Genes and Gene Pair Identification Complementary to traditional differential expression analysis ...
condition and uses an expectation-maximization algorithm to esti- mate the posterior probability of each differential correlatio ...
understanding of disease mechanisms and leads to an improved diagnosis of lung cancer. Cao et al. designed two quantitative meth ...
to human T helper cell differentiation process. Bioinformatics 23(16):2096–2103 Jiang X, Zhang H, Quan X (2016) Differen- tiall ...
Chapter 10 iSeq: Web-Based RNA-seq Data Analysis and Visualization Chao Zhang, Caoqi Fan, Jingbo Gan, Ping Zhu, Lei Kong, and Ch ...
put and decreasing cost. The rise of RNA-seq methodologies has greatly deepened our understandings of embryonic development [2], ...
component analysis (PCA) [15], and t-distributed stochastic neigh- bor embedding (t-SNE) [16], are each applicable for different ...
1.3 iSeq: A Web-Based Server for RNA-seq Data Analysis and Visualization In this article, we introduce iSeq, a Web-based analysi ...
downstream of gene-expression profile. RNA-seq workflow involves a wide range of bioinformatics tools and requires a high level ...
other plots are realized by the “RColorBrewer” R package (https://cran.r-project.org/web/packages/RColorBrewer/ RColorBrewer.pdf ...
normalizing the data, the effectiveness can be evaluated by examin- ing the comparability of the gene expression distribution ac ...
Fig. 3Size factor normalization for the RNA-seq data. The box plot on the right shows the distribution of gene expression values ...
3.4 Detect Differentially Expressed Genes (DEGs) We use the R package, DESeq, to detect differentially expressed genes between ...
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