这一篇接上一篇，我们来分享Cellrank的基础分析代码，我们直接开始

使用 RNA 速度和转录组学相似性来估计细胞-细胞转换概率。即使没有 RNA 速度信息，也可以应用 CellRank（但还是推荐大家RNA 速率和相似度方法联合使用）。

加载

import sys

if "google.colab" in sys.modules:
    !pip install -q git+https://github.com/theislab/cellrank@dev
    !pip install python-igraph
import scvelo as scv
import scanpy as sc
import cellrank as cr
import numpy as np

scv.settings.verbosity = 3
scv.settings.set_figure_params("scvelo")
cr.settings.verbosity = 2
import warnings

warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=FutureWarning)
warnings.simplefilter("ignore", category=DeprecationWarning)

首先，需要获取数据。 以下命令将下载 adata 对象并将其保存在 datasets/endocrinogenesis_day15.5.h5ad 下(示例数据，大家可以下载下来自己研究)。 我们还将显示拼接/未拼接读数的比例，需要用它来估计 RNA 速度。

adata = cr.datasets.pancreas()
scv.pl.proportions(adata)
adata

图片.png

AnnData object with n_obs × n_vars = 2531 × 27998
obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta', 'palantir_pseudotime'
var: 'highly_variable_genes'
uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
obsm: 'X_pca', 'X_umap'
layers: 'spliced', 'unspliced'
obsp: 'connectivities', 'distances'

前处理数据

过滤掉没有足够剪接/未剪接计数的基因，对数据进行归一化和对数变换，并限制在高度可变的基因上。 此外，计算速度估计的主成分和矩。

scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_pcs=30, n_neighbors=30)
scv.pp.moments(adata, n_pcs=None, n_neighbors=None)

Run scVelo

我们将使用来自 scVelo 的动力学模型来估计速度。

scv.tl.recover_dynamics(adata, n_jobs=8)

一旦有了参数，就可以使用这些参数来计算速度和速度图。 速度图是一个加权图，它指定了两个cell在给定速度向量和相对位置的情况下转换为另一个cell的可能性。

scv.tl.velocity(adata, mode="dynamical")
scv.tl.velocity_graph(adata)
scv.pl.velocity_embedding_stream(
    adata, basis="umap", legend_fontsize=12, title="", smooth=0.8, min_mass=4
)

图片.png

运行Cellrank

CellRank 提供了多种将方向性注入单细胞数据的方法。 在这里，方向信息来自 RNA 速度，使用这些信息来计算胰腺发育动态过程的初始和终止状态以及fate probabilities。

Identify terminal states

cr.tl.terminal_states(adata, cluster_key="clusters", weight_connectivities=0.2)

The most important parameters in the above function are:

• estimator: this determines what’s going to behind the scenes to compute the terminal states. Options are cr.tl.estimators.CFLARE (“Clustering and Filtering of Left and Right Eigenvectors”) or cr.tl.estimators.GPCCA (“Generalized Perron Cluster Cluster Analysis, [Reuter et al., 2018] and [Reuter et al., 2019], see also our pyGPCCA implementation). The latter is the default, it computes terminal states by coarse graining the velocity-derived Markov chain into a set of macrostates that represent the slow-time scale dynamics of the process, i.e. it finds the states that you are unlikely to leave again, once you have entered them.

• cluster_key: takes a key from adata.obs to retrieve pre-computed cluster labels, i.e. ‘clusters’ or ‘louvain’. These labels are then mapped onto the set of terminal states, to associate a name and a color with each state.

• n_states: number of expected terminal states. This parameter is optional - if it’s not provided, this number is estimated from the so-called ‘eigengap heuristic’ of the spectrum of the transition matrix.

• method: This is only relevant for the estimator GPCCA. It determines the way in which we compute and sort the real Schur decomposition. The default, krylov, is an iterative procedure that works with sparse matrices which allows the method to scale to very large cell numbers. It relies on the libraries SLEPc and PETSc, which you will have to install separately, see our installation instructions. If your dataset is small (<5k cells), and you don’t want to install these at the moment, use method='brandts' [Brandts, 2002]. The results will be the same, the difference is that brandts works with dense matrices and won’t scale to very large cells numbers.

• weight_connectivities: weight given to cell-cell similarities to account for noise in velocity vectors.

cr.pl.terminal_states(adata)

图片.png

Identify initial states

cr.tl.initial_states(adata, cluster_key="clusters")
cr.pl.initial_states(adata, discrete=True)

图片.png

Compute fate maps

一旦知道终端状态，就可以计算相关的命运图——对于每个细胞，寻求细胞朝着每个确定的终端状态发展的可能性有多大。

cr.tl.lineages(adata)
cr.pl.lineages(adata, same_plot=False)

图片.png

可以将上述内容聚合成一个单一的全局命运图，其中将每个终端状态与颜色相关联，并使用该颜色的强度来显示每个单个细胞的命运：

cr.pl.lineages(adata, same_plot=True)

图片.png

Directed PAGA

我们可以使用具有有向边的 [Wolf et al., 2019] 的改编版本将个体命运图进一步聚合成集群级别的命运图。 我们首先使用 CellRank 识别的 root_key 和 end_key 计算 scVelo 的潜伏时间，它们分别是初始状态或终止状态的概率。

scv.tl.recover_latent_time(
    adata, root_key="initial_states_probs", end_key="terminal_states_probs"
)

Next, we can use the inferred pseudotime along with the initial and terminal states probabilities to compute the directed PAGA.

scv.tl.paga(
    adata,
    groups="clusters",
    root_key="initial_states_probs",
    end_key="terminal_states_probs",
    use_time_prior="velocity_pseudotime",
)
cr.pl.cluster_fates(
    adata,
    mode="paga_pie",
    cluster_key="clusters",
    basis="umap",
    legend_kwargs={"loc": "top right out"},
    legend_loc="top left out",
    node_size_scale=5,
    edge_width_scale=1,
    max_edge_width=4,
    title="directed PAGA",
)

图片.png

Compute lineage drivers

可以计算所有谱系或部分谱系的驱动基因。 还可以通过指定cluster=...将其限制为某些cluster。 在生成的数据框中，还看到了 p 值、校正后的 p 值（q 值）和相关统计量的 95% 置信区间。

图片.png

Afterwards, we can plot the top 5 driver genes (based on the correlation), e.g. for the Alpha lineage

cr.pl.lineage_drivers(adata, lineage="Alpha", n_genes=5)

图片.png

Gene expression trends

上面演示的功能是 CellRank 的主要功能：计算初始和终止状态以及概率命运图。 现在可以使用计算出的概率来例如 沿谱系平滑基因表达趋势。

从细胞的时间顺序开始。 为了得到这个，可以计算 scVelo 的潜伏时间，如前所述，或者，我们可以只使用 CellRank 的初始状态来计算 。

# compue DPT, starting from CellRank defined root cell
root_idx = np.where(adata.obs["initial_states"] == "Ngn3 low EP")[0][0]
adata.uns["iroot"] = root_idx
sc.tl.dpt(adata)

scv.pl.scatter(
    adata,
    color=["clusters", root_idx, "latent_time", "dpt_pseudotime"],
    fontsize=16,
    cmap="viridis",
    perc=[2, 98],
    colorbar=True,
    rescale_color=[0, 1],
    title=["clusters", "root cell", "latent time", "dpt pseudotime"],
)

图片.png

We can plot dynamics of genes in pseudotime along individual trajectories, defined via the fate maps we computed above.

model = cr.ul.models.GAM(adata)
cr.pl.gene_trends(
    adata,
    model=model,
    data_key="X",
    genes=["Pak3", "Neurog3", "Ghrl"],
    ncols=3,
    time_key="latent_time",
    same_plot=True,
    hide_cells=True,
    figsize=(15, 4),
    n_test_points=200,
)

图片.png

还可以在热图中可视化上面计算的谱系驱动程序。 下面，对 Alpha 谱系执行此操作，即在伪时间中平滑假定的 Alpha 驱动程序的基因表达，将 Alpha 命运概率用作细胞级别的权重。 根据它们在伪时间中的峰值对基因进行排序，从而揭示了基因表达事件的级联。

cr.pl.heatmap(
    adata,
    model,
    genes=adata.varm['terminal_lineage_drivers']["Alpha_corr"].sort_values(ascending=False).index[:100],
    show_absorption_probabilities=True,
    lineages="Alpha",
    n_jobs=1,
    backend="loky",
)

图片.png

生活很好，有你更好