2021.02.14
今天收集了三个与谱系分化有关的单细胞高级算法:MAGIC(2018 Cell),PHATE(2019 NBT),Palantir(2019 NBT)。
它们与传统的伪时序分析方法(pseudotime analysis)略有所不同,用到了流形(manifold)学习的原理。这里推荐开发前两个包的实验室推出的一个培训,介绍机器学习在单细胞数据处理中的应用,适合已经熟悉单细胞基本处理方法并且想要进阶的选手:https://www.krishnaswamylab.org/workshop;https://github.com/KrishnaswamyLab/SingleCellWorkshop。
另外,今天摸鱼的时候看到了Broad Institute的一个单细胞培训,内容非常详细,也一起分享一下:https://broadinstitute.github.io/2019_scWorkshop/index.html。
MAGIC
处理单细胞dropout
文献
David van Dijk, et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. 2018. Cell.
https://linkinghub.elsevier.com/retrieve/pii/S0092867418307244
Krishnaswamy Lab
文档
R版本:https://github.com/KrishnaswamyLab/magic#r
python版本tutorial:https://nbviewer.jupyter.org/github/KrishnaswamyLab/MAGIC/blob/master/python/tutorial_notebooks/bonemarrow_tutorial.ipynb
文献阅读笔记
利用流形学习还原单细胞的基因表达
配合kNN-DREMI:看基因-基因关系
DREVI plot:基因聚类(基于拟时序)
基于拟时序预测TF的靶基因(changed with EMT and peaked along with or after TF,DREMI > = 1)
选取转录因子(kNN-DREMI with VIM is >0.5)
推荐的DEG方法:
earth-mover distance (EMD) used in the archetype analysis
We recommend running diffusion map analysis directly on the raw data (otherwise this could lead to over smoothing). On the other hand, MAGIC imputed data are well-suited to visualize trends along the diffusion components.
PHATE
降维展示的方法,适合发育分化
与MAGIC同一个实验室开发,适合作为MAGIC的下游使用
文献
Visualizing structure and transitions in high-dimensional biological data. 2019 NBT
https://www.nature.com/articles/s41587-019-0336-3
Krishnaswamy Lab
相关资料
推文教程:https://mp.weixin.qq.com/s/JJQfKul1uvO8XGdTE4mPeA
Github:https://github.com/KrishnaswamyLab/PHATE/
文档:https://phate.readthedocs.io/en/stable/tutorial.html#
python版本tutorial:https://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb
Palantir
一种新的谱系分化推断和展示方法
文献
文献:Characterization of cell fate probabilities in single-cell data with Palantir. 2019 NBT.
https://www.nature.com/articles/s41587-019-0068-4
Dana Pe'er Lab
相关资料
推文教程:https://mp.weixin.qq.com/s/SfOfw0CRujw2KPvVTxl3Fw (其中调用到了MAGIC)
Github: https://github.com/dpeerlab/Palantir/
Python版本tutorial:https://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/Palantir_sample_notebook.ipynb