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那些教程都是针对传统的bulk转录组测序的表达矩阵，其实单细胞转录组也是拿到表达矩阵，只不过是有一些特性，比如非常多的0值等等。那么有没有这样的研究尝试把WGCNA融入单细胞转录组数据分析呢？ 答案是有的，Posted March 04, 2019. 丢在预印本的文章，题目是：[Single-Cell RNA Sequencing Reveals Regulatory Mechanism for Trophoblast Cell-Fate Divergence in Human Peri-Implantation Embryo](Single-Cell RNA Sequencing Reveals Regulatory Mechanism for Trophoblast Cell-Fate Divergence in Human Peri-Implantation Embryo) 就这样做了，让我们一起来看看吧。 背景To obtain transcriptomic profiles of human trophoblast cells during peri-implantation development, we harvested single cells from 19 embryos from day 6 to day 10, complement with 25 endometrial cells. Transcriptomes from 614 single cells were successfully profiled, with 0.7 million uniquely mapped reads and 24,011 detected transcripts per cell on average.数据都是在：https://www.ncbi.nlm./geo/query/acc.cgi?acc=GSE125616 主要样品是人类着床前胚胎的 Trophoblasts 进行单细胞转录组测序，其中516 embryonic cells 可以分成476 TE-, 14 EPI-and 26 PE-lineage cells. 最后的分析重点是 476 individual trophoblast cells isolated from 19 human embryos
当然了，还有少量的endometrial cells,第一主成分就可以区分开来它们，如下：  Embryonic cells were assigned into three lineages, namely TE, EPI and PE, based on their expression of 300 previous identified lineage marker genes. 需要相关生物学知识。 其中时间这个属性也是在PCA上面反映到：  不管是时间这个属性天然对单细胞分组，还是整体的表达矩阵进入单细胞数据分析流程后分组， 都是可以看基因表达量情况的小提琴图等等。分析其实仍然是我们一直讲解的R包及基础流程，分别是: scater,monocle,Seurat,scran,M3Drop 需要熟练掌握它们的对象，：一些单细胞转录组R包的对象 流程也大同小异：
WGCNA步骤To systematically investigate the genetic program dynamics, we performed Weighted Gene Co-expression Network Analysis (WGCNA) on 2,464 genes that were variably expressed in trophoblast cells between different developmental stages. WGCNA identified eight gene modules, each of which contains a set of genes that tend to be coexpressed at a certain development stage！ 可以看到WGCAN其实大家需要注意的是挑选基因，然后判断模块，最后关联起来性状即可!  研究者感兴趣的生物学组别其实是：
所以才会有如下图表：  让我意外的是，文章里面仅仅是提到了 Seurat 流程，没有monocle，但是却有lineage分析 ！其实这个小鼠发育研究，跟我前面的视频课程非常类似，可以作为一个练习题，考核一下大家！ |
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