听说课程配笔记，学习无压力

构建文库

综述：Comparative Analysis of Single-Cell RNA Sequencing Methods. 2017,  （doi: 10.1016/j.molcel.2017.01.023.）

涉及到了6中文库构建方法（CEL-seq2, Drop-seq, MARS-seq, SCRB-

seq, Smart-seq, and Smart-seq2），可以再结合相关的每一个文库找6篇文章

文章发现：Smart-seq2可以在每个细胞中找到最多的基因，同样费用比较高；检测少量细胞时，MARS-seq、SCRB-seq、Smart-seq2更有效

 归一化  

文献1：Assessment of Single Cell RNA-Seq Normalization Methods，2017 (doi: 10.1534/g3.117.040683)

评价了几种归一化方法：

fragments per kilobase of transcript per million mapped

reads (FPKM)(Mortazavi et al., 2008)

upper quartile (UQ)(Bullard et al., 2010)

Trimmed mean of M-values (TMM)(Robinson and Oshlack, 2010)

DESeq(Love et al.,2014)

removed unwanted variation (RUV)(Risso et al., 2014)

gamma regression model (GRM)(Ding et al., 2015).

---

文献2：Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq, 2019 (DOI:https://doi.org/10.1016/j.cels.2019.03.010)

主要研究了scone方法：a flexible framework for assessing performance based

on a comprehensive panel of data-driven metrics

(http://bioconductor.org/packages/scone/)

另外方法还有很多，比如：LSF(Lun Sum Factors)，BigNorm, Scnorm, BASiCS, RLE(size factor relative log expression)

 

降维    

PDF: https://lib.ugent.be/fulltxt/RUG01/002/349/740/RUG01-002349740_2017_0001_AC.pdf 

值得好好阅读，讲了许多关于降维原理和应用的知识

文中1.5.1部分（Clustering high-dimension to identify subtypes）写出：

Importantly, the reduced dimensionality data are less noisy than the high-dimensional data bust lose some of the biological variance.

文章1：PCA, MDS, k-means, Hierarchical clustering and heatmap.

文章2：Outlier Preservation by Dimensionality Reduction Techniques

"MDS best choice for preserving outliers, PCA for variance, & T-SNE for clusters"

鉴定细胞群

每个术语都对应一篇文献

降维：PCA、tSNE、DM(Diffusion maps)

feature selection：M3Drop(Michaelis-Menten Modelling of Dropouts)、HVG(Highly variable genes)、Spike-in based methods、Correalated expression

Seurat：is an R package designed for the analysis and visualization of single cell RNA-seq data. It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.

SC3：SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells.

tSNE+kmeans

SNN-Clip: doi: 10.1093/bioinformatics/btv088

SINCERA: SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.

综述：A systematic performance evaluation of clustering methods for single-cell RNA-seq data (SC3 and Seurat show the most favorable results)

关于各种单细胞工具：https://www.scrna-tools.org/

文章在：Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

在单细胞天地的公众号里面有

#第一期单细胞视频笔记汇总

根据目录内容，里面大多数是教学如何实现代码得到想要的结果，所以在这里我选择先花两天时间补充背景知识【12.15-12.16】而后再根据里面的内容来进行具象实现。

#第一期单细胞视频笔记汇总 (qq.com)