转录组denovo流程及自己写的WGCNA的Methods

终于开始新的学习了~

 

首先将几个样本用trinity进行组装，这个软件还要单独写一篇出来，类似soap的组装原理；

将组装的config当成该物种的基因组文件，使用RSEM进行比对，类似tophat的功能，它的优势在于如果不评估可变剪切的情况下，ＰＥ和ＳＥ的reads比对的效果差不多；还有一点，有没有质量值也是差不多；其原理和cufflinks，scripture等评估表达量的算法差不多；

随后的流程差不多了，用contig进行blast；用edgeR或DESeq进行差异表达，然后进行GO，KEGG的富集。

 

原来trinity有自己的一键化流程，不过有好处也有缺点，好处在于小菜们比如我就可以直接使用，缺点在于不知道原理，不知道结果，不知道适用范围，不知道软件的劣势等等，这个等几天再说，看着比较难懂。

 

WGCNA方法：

We first constructed a signed weighted correlation network by creating a matrix of pairwise correlations between all genes across the measured samples [1].

Next, we choose the power β = 6 as default soft-threshold of the correlation matrix to construct the adjacency matrix.

Then we calculated the topological overlap which is a measure of strength of two gene’s co-expression relationship with respect to all other genes in the network [2].

After that genes with highly similar co-expression relationships were grouped together to identify as module by average linkage hierarchical clustering. The first principal component of expression profile of each module is regard as module eigengene which were highly correlated (correlation above 0.7) were merged.

Last, Genes with highest module membership (known as module eigengene based connectivity kME) values are referred to as intramodular hub genes. We used VisANT [3] to visualize the top 20 gene connections with the highest kME.

1.      Zhang, B. & Horvath, S. Ageneral framework for weighted gene co-expression network analysis. Stat. Appl.Genet. Mol. Biol. http://dx.doi.org/10.2202/1544-6115.1128 (2005).

2.      Li, H. & Durbin, R. Fastand accurate short read alignment with Burrows-Wheeler transform.Bioinformatics 25, 1754–1760 (2009).

3.      Hu, Z., Mellor, J., Wu, J.& DeLisi, C. VisANT: an online visualization and analysis tool forbiological interaction data. BMC Bioinformatics 5, 17 (2004).