课前准备-单细胞新版velocity（cellrank 2）
原创课前准备-单细胞新版velocity（cellrank 2）
原创
追风少年i
发布于 2024-06-17 16:24:15
发布于 2024-06-17 16:24:15
作者，Evil Genius
参考文章CellRank 2: unified fate mapping in multiview single-cell data(nature methods)，2024年6月。
官网示例在cellrank documentation
分析框架
CellRank 2为使用马尔可夫链研究单细胞命运决策提供了统一的框架
- 自动确定初始和最终状态，计算命运概率，绘制轨迹特异性基因表达趋势图表，并识别谱系相关基因
- 采用概率系统描述，其中每个细胞构成马尔可夫链中的一个状态，边缘表示细胞-细胞转移概率
- CellRank 2提供了一组基于基因表达、RNA速率、伪时间、发育潜力、实验时间点和代谢标记数据的转换概率的不同kernels 。
- 引入了一种random walk-based的可视化方案
CellRank’s Key Applications
- Estimate differentiation direction based on a varied number of biological priors, including pseudotime, developmental potential, RNA velocity, experimental time points, and more.
- Compute initial, terminal and intermediate macrostates [="https://links.jianshu.com/go?to=https%3A%2F%2Fcellrank.readthedocs.io%2Fen%2Flatest%2Freferences.html%23id21">Reuter et al., 2019, ="https://links.jianshu.com/go?to=https%3A%2F%2Fcellrank.readthedocs.io%2Fen%2Flatest%2Freferences.html%23id4">Reuter et al., 2022].
- Infer fate probabilities and driver genes.
- Visualize and cluster gene expression trends
基于velocyto
- use
scveloto compute RNA velocity - set up CellRank’s
VelocityKerneland compute a transition matrix based on RNA velocity. - combine the
VelocityKernelwith theConnectivityKernelto emphasize gene expression similarity. - visualize the transition matrix in a low-dimensional embedding.
#######示例
import sys
if "google.colab" in sys.modules:
!pip install -q git+https://github.com/theislab/cellrank
import numpy as np
import cellrank as cr
import scanpy as sc
import scvelo as scv
scv.settings.verbosity = 3
scv.settings.set_figure_params("scvelo")
cr.settings.verbosity = 2
import warnings
warnings.simplefilter("ignore", category=UserWarning)
adata = cr.datasets.pancreas()
scv.pl.proportions(adata)
adata
Preprocess the data
scv.pp.filter_and_normalize(
adata, min_shared_counts=20, n_top_genes=2000, subset_highly_variable=False
)
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_pcs=30, n_neighbors=30, random_state=0)
scv.pp.moments(adata, n_pcs=None, n_neighbors=None)
scv.tl.recover_dynamics(adata, n_jobs=8)
scv.tl.velocity(adata, mode="dynamical")Combine RNA velocity with expression similarity in high dimensions
- Set up the VelocityKernel
vk.compute_transition_matrix() 默认情况下，使用确定性模式来计算transiton matrix。如果要传播速率向量中的不确定性，查看随机模式和蒙特卡罗模式。随机模式使用KNN图估计速率向量上的分布，并使用分析近似将该分布传播到过渡矩阵中。
Combine with gene expression similarity
RNA velocity can be a very noise quantity; to make our computations more robust, we combine the VelocityKernel with the similarity-based ConnectivityKernel
ck = cr.kernels.ConnectivityKernel(adata)
ck.compute_transition_matrix()
combined_kernel = 0.8 * vk + 0.2 * ck 可视化
vk.plot_projection() 
vk.plot_random_walks(start_ixs={"clusters": "Ngn3 low EP"}, max_iter=200, seed=0) 
基于Diffusion pseudotime (DPT)
import sys
if "google.colab" in sys.modules:
!pip install -q git+https://github.com/theislab/cellrank
import numpy as np
import cellrank as cr
import scanpy as sc
import scvelo as scv
scv.settings.verbosity = 3
scv.settings.set_figure_params("scvelo")
sc.settings.set_figure_params(frameon=False, dpi=100)
cr.settings.verbosity = 2
import warnings
warnings.simplefilter("ignore", category=UserWarning)
adata = cr.datasets.bone_marrow()Check RNA velocity on this data
scv.pl.proportions(adata) 
scv.pp.filter_and_normalize(
adata, min_shared_counts=20, n_top_genes=2000, subset_highly_variable=False
)
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_pcs=30, n_neighbors=30, random_state=0)
scv.pp.moments(adata, n_pcs=None, n_neighbors=None)
scv.tl.recover_dynamics(adata, n_jobs=8)
scv.tl.velocity(adata, mode="dynamical")
vk = cr.kernels.VelocityKernel(adata)
vk.compute_transition_matrix()
vk.plot_projection(basis="tsne")
top_genes = adata.var["fit_likelihood"].sort_values(ascending=False).index
scv.pl.scatter(adata, basis=top_genes[:10], ncols=5, frameon=False)
Use pseudotime to recover directed differentiation
Choosing the right pseudotime
sc.tl.diffmap(adata)
root_ixs = 2394 # has been found using `adata.obsm['X_diffmap'][:, 3].argmax()`
scv.pl.scatter(
adata,
basis="diffmap",
c=["clusters", root_ixs],
legend_loc="right",
components=["2, 3"],
)
adata.uns["iroot"] = root_ixs
sc.tl.dpt(adata)
sc.pl.embedding(
adata,
basis="tsne",
color=["dpt_pseudotime", "palantir_pseudotime"],
color_map="gnuplot2",
)
Compute a transition matrix
pk = cr.kernels.PseudotimeKernel(adata, time_key="palantir_pseudotime")
pk.compute_transition_matrix()
print(pk)
pk.plot_projection(basis="tsne", recompute=True)
生活很好，有你更好
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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