单细胞分析文献分享《Orchestrating single-c

《Orchestrating single-cell analysis with Bioconductor》这篇文章时2019年发表在nature method上的一篇单细胞分析流程方法类文章，主要介绍的利用Bioconductor进行下游的分析

其中最主要的是发表了一本单细胞下游分析教程：
教程地址链接：传送门
Github链接：传送门

教程最后一次修订是在12月份，恰好是在前不久，还算是比较新的了

文章先介绍了SingleCellExperiment对象的组成

然后介绍了单细胞的分析workflow：

上游分析主要分为：

1. Preprocessing
2. Quality control
3. Normalization
4. Imputation
5. Feature selection
6. Dimensionality reduction
7. Integrating datasets

下游分析：

1. Clustering

2. Differential expression

   1. The first approach retrofits well-supported and long-standing DE analysis frameworks initially designed for bulk RNA-sequencing (edgeR, DESeq2 and limma-voom) that have made the transition to scRNA-seq through various approaches, such as by creating pseudo-bulk RNA-seq profiles. Alternatively, approaches such as zinbwave can be used to downweight excess zeros observed in scRNA-seq data during the dispersion estimation and model fitting steps prior to assessing differential expression (DE), and consequently further enabling the adaptation of bulk RNA-seq-based DE methods for use with scRNA-seq data
   2. The second class of approaches is uniquely tailored for single-cell data because the statistical methods proposed directly model the zero-inflation component, frequently observed in scRNA-seq data. These methods explicitly separate gene expression into two components: the discrete component, which describes the frequency of a discrete component (zero versus non-zero expression); and the continuous component, where the level of gene expression is quantified. While all the methods mentioned herein can test for differences in the continuous component, only this second class of approaches can explicitly model the discrete component, and thus test for differences in the frequency of expression. To do this, the MAST²⁷ package utilizes a hurdle model framework, whereas the scDD⁶⁴, BASiCS⁴³ and SCDE¹⁴ use Bayesian mixture and hierarchical models, respectively. Together, these methods are able to provide a broader suite of testing functionality and can be directly utilized on scRNA-seq data contained within the SingleCellExperiment class.

3. Trajectory analysis

Annotation部分分为：

1. Gene-set enrichment
2. Automated classification of cells

最后是数据融合查询：

1. Benchmarking
2. Interactive data visualization