Trajectory Inference with VIA¶
VIA is a single-cell Trajectory Inference method that offers topology construction, pseudotimes, automated terminal state prediction and automated plotting of temporal gene dynamics along lineages. Here, we have improved the original author's colouring logic and user habits so that users can use the anndata object directly for analysis。
We have completed this tutorial using the analysis provided by the original VIA authors.
Paper: Generalized and scalable trajectory inference in single-cell omics data with VIA
Code: https://github.com/ShobiStassen/VIA
Colab_Reproducibility：https://colab.research.google.com/drive/1A2X23z_RLJaYLbXaiCbZa-fjNbuGACrD?usp=sharing
In [1]:
Copied!
import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
ov.utils.ov_plot_set()
import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
ov.utils.ov_plot_set()
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
Data loading and preprocessing¶
We have used the dataset scRNA_hematopoiesis provided by the authors for this analysis, noting that the data have been normalized and logarithmicized but not scaled.
In [2]:
Copied!
adata = ov.single.scRNA_hematopoiesis()
sc.tl.pca(adata, svd_solver='arpack', n_comps=200)
adata
adata = ov.single.scRNA_hematopoiesis()
sc.tl.pca(adata, svd_solver='arpack', n_comps=200)
adata
computing PCA
with n_comps=200
finished (0:00:14)
Out[2]:
AnnData object with n_obs × n_vars = 5780 × 14651
obs: 'clusters', 'palantir_pseudotime', 'palantir_diff_potential', 'label'
uns: 'cluster_colors', 'ct_colors', 'palantir_branch_probs_cell_types', 'pca'
obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs', 'X_pca'
varm: 'PCs'
Model construct and run¶
We need to specify the cell feature vector adata_key used for VIA inference, which can be X_pca, X_scVI or X_glue, depending on the purpose of your analysis, here we use X_pca directly. We also need to specify how many components to be used, the components should not larger than the length of vector.
Besides, we need to specify the clusters to be colored and calculate for VIA. If the root_user is None, it will be calculated the root cell automatically.
We need to set basis argument stored in adata.obsm. An example setting tsne because it stored in obsm: 'tsne', 'MAGIC_imputed_data', 'palantir_branch_probs', 'X_pca'
We also need to set clusters argument stored in adata.obs. It means the celltype key.
Other explaination of argument and attributes could be found at https://pyvia.readthedocs.io/en/latest/Parameters%20and%20Attributes.html
In [3]:
Copied!
v0 = ov.single.pyVIA(adata=adata,adata_key='X_pca',adata_ncomps=80, basis='tsne',
clusters='label',knn=30,random_seed=4,root_user=[4823],)
v0.run()
v0 = ov.single.pyVIA(adata=adata,adata_key='X_pca',adata_ncomps=80, basis='tsne',
clusters='label',knn=30,random_seed=4,root_user=[4823],)
v0.run()
2023-04-08 16:07:56.457694 Running VIA over input data of 5780 (samples) x 80 (features)
2023-04-08 16:07:56.457759 Knngraph has 30 neighbors
2023-04-08 16:07:59.563166 Finished global pruning of 30-knn graph used for clustering at level of 0.15. Kept 46.3 % of edges.
2023-04-08 16:07:59.580468 Number of connected components used for clustergraph is 1
2023-04-08 16:07:59.757457 Commencing community detection
2023-04-08 16:07:59.825471 Finished running Leiden algorithm. Found 207 clusters.
2023-04-08 16:07:59.826890 Merging 189 very small clusters (<10)
2023-04-08 16:07:59.828542 Finished detecting communities. Found 18 communities
2023-04-08 16:07:59.828767 Making cluster graph. Global cluster graph pruning level: 0.15
2023-04-08 16:07:59.839755 Graph has 1 connected components before pruning
2023-04-08 16:07:59.840584 Graph has 2 connected components after pruning
2023-04-08 16:07:59.841028 Graph has 1 connected components after reconnecting
2023-04-08 16:07:59.841242 0.0% links trimmed from local pruning relative to start
2023-04-08 16:07:59.841249 64.1% links trimmed from global pruning relative to start
2023-04-08 16:07:59.842281 Starting make edgebundle viagraph...
2023-04-08 16:07:59.842287 Make via clustergraph edgebundle
2023-04-08 16:08:00.833500 Hammer dims: Nodes shape: (18, 2) Edges shape: (74, 3)
2023-04-08 16:08:00.834631 component number 0 out of [0]
2023-04-08 16:08:00.841176 The root index, 4823 provided by the user belongs to cluster number 1 and corresponds to cell type HSC1
2023-04-08 16:08:00.842439 Computing lazy-teleporting expected hitting times
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:08:12.614602 Identifying terminal clusters corresponding to unique lineages...
2023-04-08 16:08:12.614770 Closeness:[2, 3, 4, 6, 9, 12, 17]
2023-04-08 16:08:12.614784 Betweenness:[0, 1, 2, 3, 6, 9, 12, 13, 14, 16]
2023-04-08 16:08:12.614788 Out Degree:[2, 3, 4, 7, 9, 12, 15, 16, 17]
2023-04-08 16:08:12.614968 Cluster 4 had 3 or more neighboring terminal states [6, 9, 17] and so we removed cluster 6
2023-04-08 16:08:12.615076 Terminal clusters corresponding to unique lineages in this component are [2, 3, 4, 9, 12, 16, 17]
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:08:23.656094 From root 1, the Terminal state 2 is reached 7 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:08:35.170358 From root 1, the Terminal state 3 is reached 134 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:08:46.097037 From root 1, the Terminal state 4 is reached 225 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:08:56.817234 From root 1, the Terminal state 9 is reached 178 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:09:07.684097 From root 1, the Terminal state 12 is reached 135 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:09:19.214835 From root 1, the Terminal state 16 is reached 504 times.
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
/Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/phate/__init__.py
2023-04-08 16:09:30.900939 From root 1, the Terminal state 17 is reached 115 times.
2023-04-08 16:09:30.918651 Terminal clusters corresponding to unique lineages are {2: 'PRE_B2', 3: 'CMP', 4: 'ERY1', 9: 'ERY1', 12: 'MONO1', 16: 'pDC', 17: 'ERY1'}
2023-04-08 16:09:30.918677 Begin projection of pseudotime and lineage likelihood
2023-04-08 16:09:32.249802 Graph has 1 connected components before pruning
2023-04-08 16:09:32.251276 Graph has 4 connected components after pruning
2023-04-08 16:09:32.252610 Graph has 1 connected components after reconnecting
2023-04-08 16:09:32.252915 52.7% links trimmed from local pruning relative to start
2023-04-08 16:09:32.252926 67.6% links trimmed from global pruning relative to start
2023-04-08 16:09:32.254915 Start making edgebundle milestone...
2023-04-08 16:09:32.254959 Start finding milestones
2023-04-08 16:09:32.703011 End milestones
2023-04-08 16:09:32.703102 Will use via-pseudotime for edges, otherwise consider providing a list of numeric labels (single cell level) or via_object
2023-04-08 16:09:32.721873 Recompute weights
2023-04-08 16:09:32.742234 pruning milestone graph based on recomputed weights
2023-04-08 16:09:32.743046 Graph has 1 connected components before pruning
2023-04-08 16:09:32.743442 Graph has 1 connected components after pruning
2023-04-08 16:09:32.743520 Graph has 1 connected components after reconnecting
2023-04-08 16:09:32.744045 68.8% links trimmed from global pruning relative to start
2023-04-08 16:09:32.744059 regenerate igraph on pruned edges
2023-04-08 16:09:32.750338 Setting numeric label as single cell pseudotime for coloring edges
2023-04-08 16:09:32.763595 Making smooth edges
2023-04-08 16:09:33.526512 Time elapsed 95.6 seconds
Visualize and analysis¶
Before the subsequent analysis, we need to specify the colour of each cluster. Here we use sc.pl.embedding to automatically colour each cluster, if you need to specify your own colours, specify the palette parameter
In [4]:
Copied!
fig, ax = plt.subplots(1,1,figsize=(4,4))
sc.pl.embedding(
adata,
basis="tsne",
color=['label'],
frameon=False,
ncols=1,
wspace=0.5,
show=False,
ax=ax
)
fig.savefig('figures/via_fig1.png',dpi=300,bbox_inches = 'tight')
fig, ax = plt.subplots(1,1,figsize=(4,4))
sc.pl.embedding(
adata,
basis="tsne",
color=['label'],
frameon=False,
ncols=1,
wspace=0.5,
show=False,
ax=ax
)
fig.savefig('figures/via_fig1.png',dpi=300,bbox_inches = 'tight')
VIA graph¶
To visualize the results of the Trajectory inference in various ways. Via offers various plotting functions.We first show the cluster-graph level trajectory abstraction consisting of two subplots colored by annotated (true_label) composition and by pseudotime
In [5]:
Copied!
fig, ax, ax1 = v0.plot_piechart_graph(clusters='label',cmap='Reds',dpi=80,
show_legend=False,ax_text=False,fontsize=4)
fig.savefig('figures/via_fig2.png',dpi=300,bbox_inches = 'tight')
fig, ax, ax1 = v0.plot_piechart_graph(clusters='label',cmap='Reds',dpi=80,
show_legend=False,ax_text=False,fontsize=4)
fig.savefig('figures/via_fig2.png',dpi=300,bbox_inches = 'tight')
In [ ]:
Copied!
#you can use `v0.model.single_cell_pt_markov` to extract the pseudotime
v0.get_pseudotime(v0.adata)
v0.adata
#you can use `v0.model.single_cell_pt_markov` to extract the pseudotime
v0.get_pseudotime(v0.adata)
v0.adata
Visualise gene/feature graph¶
View the gene expression along the VIA graph. We use the computed HNSW small world graph in VIA to accelerate the gene imputation calculations (using similar approach to MAGIC) as follows:
In [6]:
Copied!
gene_list_magic = ['IL3RA', 'IRF8', 'GATA1', 'GATA2', 'ITGA2B', 'MPO', 'CD79B', 'SPI1', 'CD34', 'CSF1R', 'ITGAX']
fig,axs=v0.plot_clustergraph(gene_list=gene_list_magic[:4],figsize=(12,3),)
fig.savefig('figures/via_fig2_1.png',dpi=300,bbox_inches = 'tight')
gene_list_magic = ['IL3RA', 'IRF8', 'GATA1', 'GATA2', 'ITGA2B', 'MPO', 'CD79B', 'SPI1', 'CD34', 'CSF1R', 'ITGAX']
fig,axs=v0.plot_clustergraph(gene_list=gene_list_magic[:4],figsize=(12,3),)
fig.savefig('figures/via_fig2_1.png',dpi=300,bbox_inches = 'tight')
shape of transition matrix raised to power 3 (5780, 5780)
Trajectory projection¶
Visualize the overall VIA trajectory projected onto a 2D embedding (UMAP, Phate, TSNE etc) in different ways.
- Draw the high-level clustergraph abstraction onto the embedding;
- Draws a vector field plot of the more fine-grained directionality of cells along the trajectory projected onto an embedding.
- Draw high-edge resolution directed graph
In [7]:
Copied!
fig,ax1,ax2=v0.plot_trajectory_gams(basis='tsne',clusters='label',draw_all_curves=False)
fig.savefig('figures/via_fig3.png',dpi=300,bbox_inches = 'tight')
fig,ax1,ax2=v0.plot_trajectory_gams(basis='tsne',clusters='label',draw_all_curves=False)
fig.savefig('figures/via_fig3.png',dpi=300,bbox_inches = 'tight')
100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00 100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00
2023-04-08 16:10:39.152008 Super cluster 2 is a super terminal with sub_terminal cluster 2 2023-04-08 16:10:39.152033 Super cluster 3 is a super terminal with sub_terminal cluster 3 2023-04-08 16:10:39.152038 Super cluster 4 is a super terminal with sub_terminal cluster 4 2023-04-08 16:10:39.152045 Super cluster 9 is a super terminal with sub_terminal cluster 9 2023-04-08 16:10:39.152050 Super cluster 12 is a super terminal with sub_terminal cluster 12 2023-04-08 16:10:39.152056 Super cluster 16 is a super terminal with sub_terminal cluster 16 2023-04-08 16:10:39.152061 Super cluster 17 is a super terminal with sub_terminal cluster 17
In [8]:
Copied!
fig,ax=v0.plot_stream(basis='tsne',clusters='label',
density_grid=0.8, scatter_size=30, scatter_alpha=0.3, linewidth=0.5)
fig.savefig('figures/via_fig4.png',dpi=300,bbox_inches = 'tight')
fig,ax=v0.plot_stream(basis='tsne',clusters='label',
density_grid=0.8, scatter_size=30, scatter_alpha=0.3, linewidth=0.5)
fig.savefig('figures/via_fig4.png',dpi=300,bbox_inches = 'tight')
In [9]:
Copied!
fig,ax=v0.plot_stream(basis='tsne',density_grid=0.8, scatter_size=30, color_scheme='time', linewidth=0.5,
min_mass = 1, cutoff_perc = 5, scatter_alpha=0.3, marker_edgewidth=0.1,
density_stream = 2, smooth_transition=1, smooth_grid=0.5)
fig.savefig('figures/via_fig5.png',dpi=300,bbox_inches = 'tight')
fig,ax=v0.plot_stream(basis='tsne',density_grid=0.8, scatter_size=30, color_scheme='time', linewidth=0.5,
min_mass = 1, cutoff_perc = 5, scatter_alpha=0.3, marker_edgewidth=0.1,
density_stream = 2, smooth_transition=1, smooth_grid=0.5)
fig.savefig('figures/via_fig5.png',dpi=300,bbox_inches = 'tight')
Probabilistic pathways¶
Visualize the probabilistic pathways from root to terminal state as indicated by the lineage likelihood. The higher the linage likelihood, the greater the potential of that particular cell to differentiate towards the terminal state of interest.
In [10]:
Copied!
fig,axs=v0.plot_lineage_probability(figsize=(8,4),)
fig.savefig('figures/via_fig6.png',dpi=300,bbox_inches = 'tight')
fig,axs=v0.plot_lineage_probability(figsize=(8,4),)
fig.savefig('figures/via_fig6.png',dpi=300,bbox_inches = 'tight')
2023-04-08 16:10:43.368902 Marker_lineages: [2, 3, 4, 9, 12, 16, 17] 2023-04-08 16:10:43.370404 The number of components in the original full graph is 1 2023-04-08 16:10:43.370414 For downstream visualization purposes we are also constructing a low knn-graph 2023-04-08 16:10:47.249552 Check sc pb [0. 0. 0.317 0.302 0.013 0.109 0.26 ] 2023-04-08 16:10:47.385340 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 2: [1, 8, 0, 10, 2] 2023-04-08 16:10:47.385368 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 3: [1, 8, 0, 3] 2023-04-08 16:10:47.385374 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 4: [1, 11, 6, 4] 2023-04-08 16:10:47.385379 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 9: [1, 11, 6, 4, 9] 2023-04-08 16:10:47.385383 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 12: [1, 8, 0, 3, 12] 2023-04-08 16:10:47.385388 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 16: [1, 8, 0, 7, 15, 16] 2023-04-08 16:10:47.385392 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 17: [1, 11, 6, 4, 17] 2023-04-08 16:10:47.429439 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 2 along path: [1, 1, 1, 1, 10, 2, 2, 2, 2, 2] 2023-04-08 16:10:47.440022 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 3 along path: [1, 1, 8, 0, 3] 2023-04-08 16:10:47.450675 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 4 along path: [1, 1, 0, 4, 4, 4, 4] 2023-04-08 16:10:47.461971 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 9 along path: [1, 1, 1, 1, 6, 9, 9] 2023-04-08 16:10:47.473958 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 12 along path: [1, 1, 8, 12, 12, 12, 12, 12] 2023-04-08 16:10:47.485842 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 16 along path: [1, 1, 8, 14, 16, 16, 16, 16] 2023-04-08 16:10:47.497613 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 17 along path: [1, 1, 1, 1, 11, 17, 17, 17, 17]
We can specify a specific linkage for visualisation
In [11]:
Copied!
fig,axs=v0.plot_lineage_probability(figsize=(6,3),marker_lineages = [2,3])
fig.savefig('figures/via_fig7.png',dpi=300,bbox_inches = 'tight')
fig,axs=v0.plot_lineage_probability(figsize=(6,3),marker_lineages = [2,3])
fig.savefig('figures/via_fig7.png',dpi=300,bbox_inches = 'tight')
2023-04-08 16:10:48.686467 Marker_lineages: [2, 3] 2023-04-08 16:10:48.688360 The number of components in the original full graph is 1 2023-04-08 16:10:48.688371 For downstream visualization purposes we are also constructing a low knn-graph 2023-04-08 16:10:52.579451 Check sc pb [0. 0. 0.317 0.302 0.013 0.109 0.26 ] 2023-04-08 16:10:52.711032 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 2: [1, 8, 0, 10, 2] 2023-04-08 16:10:52.711056 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 3: [1, 8, 0, 3] 2023-04-08 16:10:52.711062 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 4: [1, 11, 6, 4] 2023-04-08 16:10:52.711067 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 9: [1, 11, 6, 4, 9] 2023-04-08 16:10:52.711071 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 12: [1, 8, 0, 3, 12] 2023-04-08 16:10:52.711075 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 16: [1, 8, 0, 7, 15, 16] 2023-04-08 16:10:52.711080 Cluster path on clustergraph starting from Root Cluster 1 to Terminal Cluster 17: [1, 11, 6, 4, 17] 2023-04-08 16:10:52.730775 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 2 along path: [1, 1, 1, 1, 10, 2, 2, 2, 2, 2] 2023-04-08 16:10:52.741353 Revised Cluster level path on sc-knnGraph from Root Cluster 1 to Terminal Cluster 3 along path: [1, 1, 8, 0, 3]
Gene Dynamics¶
The gene dynamics along pseudotime for all detected lineages are automatically inferred by VIA. These can be interpreted as the change in gene expression along any given lineage.
In [12]:
Copied!
fig,axs=v0.plot_gene_trend(gene_list=gene_list_magic,figsize=(8,6),)
fig.savefig('figures/via_fig8.png',dpi=300,bbox_inches = 'tight')
fig,axs=v0.plot_gene_trend(gene_list=gene_list_magic,figsize=(8,6),)
fig.savefig('figures/via_fig8.png',dpi=300,bbox_inches = 'tight')
shape of transition matrix raised to power 3 (5780, 5780)
In [14]:
Copied!
fig,ax=v0.plot_gene_trend_heatmap(gene_list=gene_list_magic,figsize=(4,4),
marker_lineages=[2])
fig.savefig('figures/via_fig9.png',dpi=300,bbox_inches = 'tight')
fig,ax=v0.plot_gene_trend_heatmap(gene_list=gene_list_magic,figsize=(4,4),
marker_lineages=[2])
fig.savefig('figures/via_fig9.png',dpi=300,bbox_inches = 'tight')
shape of transition matrix raised to power 3 (5780, 5780)
In [ ]:
Copied!
In [ ]:
Copied!