RNAvelocity系列教程9:# scVelo应用-动力学模型

动力学模型

在这里,我们使用通用动力学模型来解释完整的转录动态。

这产生了一些额外的见解,如潜在时间和假定驱动基因的识别。

与以前的教程一样,应用胰腺内分泌发育数据集来展示。

[ ]:
# update to the latest version, if not done yet.
!pip install scvelo --upgrade --quiet
[1]:
import scvelo as scv
scv.logging.print_version()
Running scvelo 0.2.0 (python 3.8.2) on 2020-05-15 00:27.
[2]:
scv.settings.verbosity = 3  # show errors(0), warnings(1), info(2), hints(3)
scv.settings.presenter_view = True  # set max width size for presenter view
scv.settings.set_figure_params('scvelo')  # for beautified visualization

准备数据

处理包括基因选择、按总大小标准化、log X 和计算速率估计时刻。有关进一步解释,请参阅以前的教程。

[3]:
adata = scv.datasets.pancreas()
[4]:
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
Filtered out 20801 genes that are detected in less than 20 counts (shared).
Normalized count data: X, spliced, unspliced.
Logarithmized X.
computing neighbors
    finished (0:00:03) --> added
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:00) --> added
    'Ms' and 'Mu', moments of spliced/unspliced abundances (adata.layers)

动力学模型

我们运行动力学模型来学习剪切动力的完整转录动力学。

它基于可能性的期望最大化框架,通过反复估计反应速率和潜在细胞特异变量的参数,即转录状态和细胞内部潜在时间,旨在了解每个基因的未剪切/剪切相轨迹。

[5]:
scv.tl.recover_dynamics(adata)
recovering dynamics
    finished (0:13:31) --> added
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
[6]:
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
computing velocities
    finished (0:00:04) --> added
    'velocity', velocity vectors for each individual cell (adata.layers)
computing velocity graph
    finished (0:00:08) --> added
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)

运行动力学模型可能需要一段时间。因此,可以存储结果以供重复使用。

[7]:
#adata.write('data/pancreas.h5ad', compression='gzip')
#adata = scv.read('data/pancreas.h5ad')
[8]:
scv.pl.velocity_embedding_stream(adata, basis='umap')
computing velocity embedding    finished (0:00:00) --> added    'velocity_umap', embedded velocity vectors (adata.obsm)
image-20210712203845802

动力率参数

RNA转录、拼接和降解的速率在不需要任何实验数据的情况下进行估计。

它们有助于更好地了解细胞身份和表型异质性。

[9]:df = adata.vardf = df[(df['fit_likelihood'] > .1) & df['velocity_genes'] == True]kwargs = dict(xscale='log', fontsize=16)with scv.GridSpec(ncols=3) as pl:    pl.hist(df['fit_alpha'], xlabel='transcription rate', **kwargs)    pl.hist(df['fit_beta'] * df['fit_scaling'], xlabel='splicing rate', xticks=[.1, .4, 1], **kwargs)    pl.hist(df['fit_gamma'], xlabel='degradation rate', xticks=[.1, .4, 1], **kwargs)scv.get_df(adata, 'fit*', dropna=True).head()
image-20210712203907863
[9]:
fit_r2 fit_alpha fit_beta fit_gamma fit_t_ fit_scaling fit_std_u fit_std_s fit_likelihood fit_u0 fit_s0 fit_pval_steady fit_steady_u fit_steady_s fit_variance fit_alignment_scaling
index
Sntg1 0.401981 0.015726 0.005592 0.088792 23.404254 42.849447 1.029644 0.030838 0.406523 0.0 0.0 0.159472 2.470675 0.094304 0.149138 5.355590
Sbspon 0.624803 0.464865 2.437113 0.379645 3.785993 0.154771 0.058587 0.178859 0.252135 0.0 0.0 0.182088 0.164805 0.430623 0.674312 1.193015
Mcm3 0.292389 3.096367 39.995796 0.638543 2.049463 0.013943 0.016253 0.673142 0.228207 0.0 0.0 0.467683 0.051432 1.927742 0.687468 0.887607
Fam135a 0.384662 0.172335 0.118088 0.204538 11.239574 1.124040 0.356525 0.149868 0.283343 0.0 0.0 0.387921 1.345830 0.393197 0.671096 3.390194
Adgrb3 0.384552 0.046828 0.006750 0.196856 6.992542 71.850736 2.153206 0.030417 0.250195 0.0 0.0 0.068851 5.214500 0.093570 0.556878 1.893389

估计的基因特定参数包括转录比率(fit_alpha)、拼接率(fit_beta)、降解率(fit_gamma)、切换时间点(fit_t_)、用于校正代表性不足的未剪切读数(fit_scaling)、未剪切和拼接读数的标准偏差(fit_std_u ,fit_std_s),基因可能性(fit_likelihood),推断稳定状态水平(fit_steady_u)与其相应的p值(fit_pval_steady_s),整体模型方差(fit_variance),和一个缩放系数,以比对基因的潜在时间到普遍的基因共享潜在时间(fit_alignment_scaling)。

潜在时间

动力学模型可恢复细胞过程的潜在时间。这个潜伏时间代表细胞的内部时钟,并接近细胞在分化时所经历的实时,分析仅基于其转录动力学。

[10]:scv.tl.latent_time(adata)scv.pl.scatter(adata, color='latent_time', color_map='gnuplot', size=80)
computing terminal states    identified 2 regions of root cells and 1 region of end points    finished (0:00:00) --> added    'root_cells', root cells of Markov diffusion process (adata.obs)    'end_points', end points of Markov diffusion process (adata.obs)computing latent time    finished (0:00:02) --> added    'latent_time', shared time (adata.obs)
image-20210712203928580
[11]:top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]scv.pl.heatmap(adata, var_names=top_genes, sortby='latent_time', col_color='clusters', n_convolve=100)
image-20210712203948230

top基因

驱动基因显示明显的动力学行为,并可通过动力学模型中特征系统地被检测到。

[12]:top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).indexscv.pl.scatter(adata, basis=top_genes[:15], ncols=5, frameon=False)
image-20210712204010854
[13]:var_names = ['Actn4', 'Ppp3ca', 'Cpe', 'Nnat']scv.pl.scatter(adata, var_names, frameon=False)scv.pl.scatter(adata, x='latent_time', y=var_names, frameon=False)
image-20210712204032208
image-20210712204051478

cluster特异top基因

此外,可以计算每个细胞群的部分基因可能性,以便对潜在驱动因素进行特定的cluster识别。

[14]:scv.tl.rank_dynamical_genes(adata, groupby='clusters')df = scv.get_df(adata, 'rank_dynamical_genes/names')df.head(5)
ranking genes by cluster-specific likelihoods    finished (0:00:03) --> added    'rank_dynamical_genes', sorted scores by group ids (adata.uns)[14]:
image-20210712204734268
[15]:for cluster in ['Ductal', 'Ngn3 high EP', 'Pre-endocrine', 'Beta']:    scv.pl.scatter(adata, df[cluster][:5], ylabel=cluster, frameon=False)
image-20210712204117050
image-20210712204224138

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