elf1 perturbation simulation#
Notebook for analyzing elf1 perturbation effects and regulatory circut
Library imports#
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
import mplscience
import seaborn as sns
import cellrank as cr
import scanpy as sc
import scvelo as scv
from regvelo import REGVELOVI
from rgv_tools import DATA_DIR, FIG_DIR
from rgv_tools.benchmarking import set_output
from rgv_tools.perturbation import (
abundance_test,
get_list_name,
in_silico_block_simulation,
inferred_GRN,
RegulationScanning,
)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_excel from `anndata` is deprecated. Import anndata.io.read_excel instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_hdf from `anndata` is deprecated. Import anndata.io.read_hdf instead.
warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/dynamo/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_umi_tools from `anndata` is deprecated. Import anndata.io.read_umi_tools instead.
warnings.warn(msg, FutureWarning)
General settings#
%matplotlib inline
plt.rcParams["svg.fonttype"] = "none"
sns.reset_defaults()
sns.reset_orig()
scv.settings.set_figure_params("scvelo", dpi_save=400, dpi=80, transparent=True, fontsize=14, color_map="viridis")
Constants#
DATASET = "zebrafish"
SAVE_DATA = True
if SAVE_DATA:
(DATA_DIR / DATASET / "results").mkdir(parents=True, exist_ok=True)
SAVE_FIGURES = False
if SAVE_FIGURES:
(FIG_DIR / DATASET).mkdir(parents=True, exist_ok=True)
TERMINAL_STATES = [
"mNC_head_mesenchymal",
"mNC_arch2",
"mNC_hox34",
"Pigment",
]
Data loading#
adata = sc.read_h5ad(DATA_DIR / DATASET / "processed" / "adata_run_regvelo.h5ad")
elf1 perturbation simulation#
Model loading#
model = DATA_DIR / DATASET / "processed" / "rgv_model"
vae = REGVELOVI.load(model, adata)
set_output(adata, vae)
INFO File /ictstr01/home/icb/weixu.wang/regulatory_velo/data/zebrafish/processed/rgv_model/model.pt already
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Calculate cell fate probabilities on original vector field#
vk = cr.kernels.VelocityKernel(adata)
vk.compute_transition_matrix()
ck = cr.kernels.ConnectivityKernel(adata).compute_transition_matrix()
estimator = cr.estimators.GPCCA(0.8 * vk + 0.2 * ck)
## evaluate the fate prob on original space
estimator.compute_macrostates(n_states=7, cluster_key="cell_type")
estimator.set_terminal_states(TERMINAL_STATES)
estimator.compute_fate_probabilities()
estimator.plot_fate_probabilities(same_plot=False)
WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying
2024-11-25 17:12:33,406 - INFO - Using pre-computed Schur decomposition
WARNING: Unable to import petsc4py. For installation, please refer to: https://petsc4py.readthedocs.io/en/stable/install.html.
Defaulting to `'gmres'` solver.
Calculate cell fate probabilities on perturbed vector field#
adata_target_perturb, reg_vae_perturb = in_silico_block_simulation(model, adata, "elf1")
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vk = cr.kernels.VelocityKernel(adata_target_perturb)
vk.compute_transition_matrix()
ck = cr.kernels.ConnectivityKernel(adata_target_perturb).compute_transition_matrix()
estimator_p = cr.estimators.GPCCA(0.8 * vk + 0.2 * ck)
## evaluate the fate prob on original space
estimator_p.compute_macrostates(n_states=7, cluster_key="cell_type")
estimator_p.set_terminal_states(TERMINAL_STATES)
estimator_p.compute_fate_probabilities()
## visualize coefficient
cond1_df = pd.DataFrame(
adata_target_perturb.obsm["lineages_fwd"], columns=adata_target_perturb.obsm["lineages_fwd"].names.tolist()
)
cond2_df = pd.DataFrame(adata.obsm["lineages_fwd"], columns=adata.obsm["lineages_fwd"].names.tolist())
## abundance test
data = abundance_test(cond2_df, cond1_df)
data = pd.DataFrame(
{
"Score": data.iloc[:, 0].tolist(),
"p-value": data.iloc[:, 1].tolist(),
"Terminal state": data.index.tolist(),
"TF": ["elf1"] * (data.shape[0]),
}
)
WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
WARNING: For `method='brandts'`, dense matrix is required. Densifying
2024-11-25 17:12:42,665 - INFO - Using pre-computed Schur decomposition
final_df = data.copy()
final_df["Score"] = 0.5 - final_df["Score"]
color_label = "cell_type"
df = pd.DataFrame(final_df["Score"])
df.columns = ["Depletion score"]
df["Cell type"] = final_df["Terminal state"]
order = df["Cell type"].tolist()
palette = dict(zip(adata.obs[color_label].cat.categories, adata.uns[f"{color_label}_colors"]))
subset_palette = {name: color for name, color in palette.items() if name in final_df.loc[:, "Terminal state"].tolist()}
with mplscience.style_context():
sns.set_style(style="whitegrid")
fig, ax = plt.subplots(figsize=(2, 2))
sns.barplot(
data=df,
y="Depletion score",
x="Cell type",
palette=subset_palette,
order=order,
ax=ax,
)
ax.set(ylim=(-0.05, 0.05))
ax.tick_params(axis="x", rotation=90)
plt.title("$\\mathit{" + "elf1" + "}$ regulon knock out simulation")
if SAVE_FIGURES:
plt.savefig(
FIG_DIR / DATASET / "elf1_perturbation_simulation.svg", format="svg", transparent=True, bbox_inches="tight"
)
# Show the plot
plt.show()
GRN computation#
GRN = inferred_GRN(vae, adata, label="cell_type", group="all", data_frame=True)
Computing global GRN...
elf1 target screening#
targets = GRN.loc[:, "elf1"]
targets = np.array(targets.index.tolist())[np.array(targets) != 0]
print("inferring perturbation...")
perturb_screening = RegulationScanning(model, adata, 7, "cell_type", TERMINAL_STATES, "elf1", targets, 0)
coef = pd.DataFrame(np.array(perturb_screening["coefficient"]))
coef.index = perturb_screening["target"]
coef.columns = get_list_name(perturb_screening["coefficient"][0])
inferring perturbation...
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WARNING: Unable to import `petsc4py` or `slepc4py`. Using `method='brandts'`
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2024-11-25 17:12:55,891 - INFO - Using pre-computed Schur decomposition
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2024-11-25 17:13:09,564 - INFO - Using pre-computed Schur decomposition
Done ENSDARG00000024966
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2024-11-25 17:13:22,520 - INFO - Using pre-computed Schur decomposition
Done ENSDARG00000042329
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2024-11-25 17:13:35,339 - INFO - Using pre-computed Schur decomposition
Done alcama
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2024-11-25 17:13:48,245 - INFO - Using pre-computed Schur decomposition
Done aopep
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2024-11-25 17:14:01,784 - INFO - Using pre-computed Schur decomposition
Done apc
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2024-11-25 17:14:15,353 - INFO - Using pre-computed Schur decomposition
Done atp6v0ca
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2024-11-25 17:14:28,892 - INFO - Using pre-computed Schur decomposition
Done baz1b
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2024-11-25 17:14:43,256 - INFO - Using pre-computed Schur decomposition
Done calr3b
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2024-11-25 17:14:56,582 - INFO - Using pre-computed Schur decomposition
Done ccny
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2024-11-25 17:15:10,068 - INFO - Using pre-computed Schur decomposition
Done cdk1
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2024-11-25 17:15:23,049 - INFO - Using pre-computed Schur decomposition
Done cdkn1ca
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2024-11-25 17:15:35,974 - INFO - Using pre-computed Schur decomposition
Done celf2
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2024-11-25 17:15:48,925 - INFO - Using pre-computed Schur decomposition
Done cenpf
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2024-11-25 17:16:01,766 - INFO - Using pre-computed Schur decomposition
Done cpeb4b
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2024-11-25 17:16:15,175 - INFO - Using pre-computed Schur decomposition
Done cxxc5b
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2024-11-25 17:16:29,623 - INFO - Using pre-computed Schur decomposition
Done diaph2
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2024-11-25 17:16:42,563 - INFO - Using pre-computed Schur decomposition
Done dlg1
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2024-11-25 17:16:56,979 - INFO - Using pre-computed Schur decomposition
Done dnajb2
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2024-11-25 17:17:11,137 - INFO - Using pre-computed Schur decomposition
Done dnmt1
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2024-11-25 17:17:24,700 - INFO - Using pre-computed Schur decomposition
Done dusp5
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2024-11-25 17:17:37,726 - INFO - Using pre-computed Schur decomposition
Done ebf3a
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2024-11-25 17:17:51,116 - INFO - Using pre-computed Schur decomposition
Done emc2
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2024-11-25 17:18:04,107 - INFO - Using pre-computed Schur decomposition
Done ephb3a
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2024-11-25 17:18:17,957 - INFO - Using pre-computed Schur decomposition
Done erbb3b
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2024-11-25 17:18:31,203 - INFO - Using pre-computed Schur decomposition
Done esco2
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2024-11-25 17:18:44,101 - INFO - Using pre-computed Schur decomposition
Done ets1
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2024-11-25 17:18:57,224 - INFO - Using pre-computed Schur decomposition
Done eva1ba
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2024-11-25 17:19:10,181 - INFO - Using pre-computed Schur decomposition
Done fam49a
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2024-11-25 17:19:23,768 - INFO - Using pre-computed Schur decomposition
Done fhl3a
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2024-11-25 17:19:37,305 - INFO - Using pre-computed Schur decomposition
Done fhod1
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2024-11-25 17:19:51,016 - INFO - Using pre-computed Schur decomposition
Done fli1a
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2024-11-25 17:20:04,057 - INFO - Using pre-computed Schur decomposition
Done foxo1a
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2024-11-25 17:20:17,544 - INFO - Using pre-computed Schur decomposition
Done fzd3a
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2024-11-25 17:20:31,331 - INFO - Using pre-computed Schur decomposition
Done glb1l
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2024-11-25 17:20:44,414 - INFO - Using pre-computed Schur decomposition
Done glulb
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2024-11-25 17:20:57,600 - INFO - Using pre-computed Schur decomposition
Done gpd2
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2024-11-25 17:21:10,697 - INFO - Using pre-computed Schur decomposition
Done hat1
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2024-11-25 17:21:24,392 - INFO - Using pre-computed Schur decomposition
Done hexb
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2024-11-25 17:21:38,170 - INFO - Using pre-computed Schur decomposition
Done hivep1
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2024-11-25 17:21:51,188 - INFO - Using pre-computed Schur decomposition
Done hivep3b
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2024-11-25 17:22:04,155 - INFO - Using pre-computed Schur decomposition
Done hmgn2
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2024-11-25 17:22:17,260 - INFO - Using pre-computed Schur decomposition
Done hnrnpabb
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2024-11-25 17:22:30,930 - INFO - Using pre-computed Schur decomposition
Done hpcal4
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2024-11-25 17:22:43,842 - INFO - Using pre-computed Schur decomposition
Done hsp70.2
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2024-11-25 17:22:57,057 - INFO - Using pre-computed Schur decomposition
Done hspa5
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2024-11-25 17:23:10,085 - INFO - Using pre-computed Schur decomposition
Done hspb8
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2024-11-25 17:23:23,254 - INFO - Using pre-computed Schur decomposition
Done id2a
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2024-11-25 17:23:36,992 - INFO - Using pre-computed Schur decomposition
Done ildr2
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2024-11-25 17:23:50,141 - INFO - Using pre-computed Schur decomposition
Done inka1a
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2024-11-25 17:24:03,229 - INFO - Using pre-computed Schur decomposition
Done itga3a
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2024-11-25 17:24:17,712 - INFO - Using pre-computed Schur decomposition
Done itga8
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2024-11-25 17:24:33,000 - INFO - Using pre-computed Schur decomposition
Done ivns1abpa
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2024-11-25 17:24:46,854 - INFO - Using pre-computed Schur decomposition
Done klf6a
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2024-11-25 17:25:00,293 - INFO - Using pre-computed Schur decomposition
Done kntc1
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2024-11-25 17:25:13,596 - INFO - Using pre-computed Schur decomposition
Done mbnl2
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2024-11-25 17:25:26,944 - INFO - Using pre-computed Schur decomposition
Done metrn
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2024-11-25 17:25:40,431 - INFO - Using pre-computed Schur decomposition
Done mibp
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2024-11-25 17:25:53,708 - INFO - Using pre-computed Schur decomposition
Done myo10l1
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2024-11-25 17:26:06,849 - INFO - Using pre-computed Schur decomposition
Done nr6a1b
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2024-11-25 17:26:20,018 - INFO - Using pre-computed Schur decomposition
Done pcdh2g28
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2024-11-25 17:26:33,074 - INFO - Using pre-computed Schur decomposition
Done pdgfba
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2024-11-25 17:26:47,129 - INFO - Using pre-computed Schur decomposition
Done pdlim4
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2024-11-25 17:27:00,375 - INFO - Using pre-computed Schur decomposition
Done pleca
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2024-11-25 17:27:13,479 - INFO - Using pre-computed Schur decomposition
Done plpp3
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2024-11-25 17:27:26,802 - INFO - Using pre-computed Schur decomposition
Done pmp22a
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2024-11-25 17:27:40,425 - INFO - Using pre-computed Schur decomposition
Done ppt1
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2024-11-25 17:27:53,707 - INFO - Using pre-computed Schur decomposition
Done prdm1a
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2024-11-25 17:28:06,761 - INFO - Using pre-computed Schur decomposition
Done prkceb
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2024-11-25 17:28:20,094 - INFO - Using pre-computed Schur decomposition
Done prkcsh
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2024-11-25 17:28:33,164 - INFO - Using pre-computed Schur decomposition
Done pttg1ipb
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2024-11-25 17:28:47,076 - INFO - Using pre-computed Schur decomposition
Done rabl6b
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2024-11-25 17:29:00,421 - INFO - Using pre-computed Schur decomposition
Done ralgps2
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2024-11-25 17:29:14,788 - INFO - Using pre-computed Schur decomposition
Done rgl1
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2024-11-25 17:29:28,337 - INFO - Using pre-computed Schur decomposition
Done rhbdf1a
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2024-11-25 17:29:41,513 - INFO - Using pre-computed Schur decomposition
Done rhoca
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2024-11-25 17:29:55,226 - INFO - Using pre-computed Schur decomposition
Done rxraa
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2024-11-25 17:30:08,472 - INFO - Using pre-computed Schur decomposition
Done sash1b
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2024-11-25 17:30:21,732 - INFO - Using pre-computed Schur decomposition
Done sema3d
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2024-11-25 17:30:34,977 - INFO - Using pre-computed Schur decomposition
Done sema4ba
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2024-11-25 17:30:48,848 - INFO - Using pre-computed Schur decomposition
Done sept12
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2024-11-25 17:31:02,019 - INFO - Using pre-computed Schur decomposition
Done sept9a
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2024-11-25 17:31:15,226 - INFO - Using pre-computed Schur decomposition
Done serinc5
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2024-11-25 17:31:28,458 - INFO - Using pre-computed Schur decomposition
Done shroom4
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2024-11-25 17:31:41,715 - INFO - Using pre-computed Schur decomposition
Done si:ch211-199g17.2
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2024-11-25 17:31:56,148 - INFO - Using pre-computed Schur decomposition
Done si:ch211-222l21.1
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2024-11-25 17:32:09,345 - INFO - Using pre-computed Schur decomposition
Done si:ch73-335l21.1
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2024-11-25 17:32:24,218 - INFO - Using pre-computed Schur decomposition
Done si:dkey-17m8.1
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2024-11-25 17:32:37,533 - INFO - Using pre-computed Schur decomposition
Done sipa1l2
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2024-11-25 17:32:51,187 - INFO - Using pre-computed Schur decomposition
Done slbp
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2024-11-25 17:33:04,493 - INFO - Using pre-computed Schur decomposition
Done slc12a9
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2024-11-25 17:33:17,852 - INFO - Using pre-computed Schur decomposition
Done sox6
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2024-11-25 17:33:31,108 - INFO - Using pre-computed Schur decomposition
Done src
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2024-11-25 17:33:44,344 - INFO - Using pre-computed Schur decomposition
Done stat3
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2024-11-25 17:33:57,886 - INFO - Using pre-computed Schur decomposition
Done tcf12
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2024-11-25 17:34:11,078 - INFO - Using pre-computed Schur decomposition
Done tes
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2024-11-25 17:34:24,532 - INFO - Using pre-computed Schur decomposition
Done tle3b
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2024-11-25 17:34:37,771 - INFO - Using pre-computed Schur decomposition
Done tuba8l4
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2024-11-25 17:34:51,023 - INFO - Using pre-computed Schur decomposition
Done vash2
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2024-11-25 17:35:04,245 - INFO - Using pre-computed Schur decomposition
Done vav2
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2024-11-25 17:35:17,376 - INFO - Using pre-computed Schur decomposition
Done zgc:154093
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2024-11-25 17:35:30,736 - INFO - Using pre-computed Schur decomposition
Done zgc:165573
coef.sort_values("Pigment", ascending=False).iloc[:15,]
| mNC_head_mesenchymal | mNC_arch2 | mNC_hox34 | Pigment | |
|---|---|---|---|---|
| fli1a | 0.493243 | 0.474960 | 0.508461 | 0.514694 |
| dnmt1 | 0.497401 | 0.490594 | 0.496920 | 0.509698 |
| esco2 | 0.498142 | 0.487521 | 0.497025 | 0.509480 |
| baz1b | 0.497986 | 0.493608 | 0.496102 | 0.508951 |
| hat1 | 0.500184 | 0.489740 | 0.495598 | 0.508426 |
| si:ch73-335l21.1 | 0.497801 | 0.486049 | 0.499591 | 0.507288 |
| cpeb4b | 0.500248 | 0.491323 | 0.496722 | 0.506689 |
| hivep1 | 0.498229 | 0.489174 | 0.499295 | 0.506637 |
| metrn | 0.499143 | 0.487146 | 0.499159 | 0.506189 |
| vash2 | 0.498352 | 0.486737 | 0.501403 | 0.505310 |
| rxraa | 0.499764 | 0.503529 | 0.496063 | 0.505229 |
| rgl1 | 0.500238 | 0.492834 | 0.497344 | 0.505164 |
| kntc1 | 0.499017 | 0.497358 | 0.497949 | 0.504558 |
| serinc5 | 0.499229 | 0.489799 | 0.499768 | 0.504350 |
| slbp | 0.498465 | 0.493651 | 0.499795 | 0.504350 |
Pigment = coef.sort_values(by="Pigment", ascending=False)[:15]["Pigment"]
df = pd.DataFrame({"Gene": Pigment.index.tolist(), "Score": np.array(Pigment)})
# Sort DataFrame by -log10(p-value) for ordered plotting
df = df.sort_values(by="Score", ascending=False)
# Highlight specific genes
# Set up the plot
with mplscience.style_context():
sns.set_style(style="white")
fig, ax = plt.subplots(figsize=(3, 6))
sns.scatterplot(data=df, x="Score", y="Gene", palette="purple", s=200, legend=False)
for _, row in df.iterrows():
plt.hlines(row["Gene"], xmin=0.5, xmax=row["Score"], colors="grey", linestyles="-", alpha=0.5)
# Customize plot appearance
plt.xlabel("Score")
plt.ylabel("")
plt.title("Pigment")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["left"].set_color("black")
plt.gca().spines["bottom"].set_color("black")
# Show plot
if SAVE_FIGURES:
fig.savefig(FIG_DIR / DATASET / "elf1_driver.svg", format="svg", transparent=True, bbox_inches="tight")
plt.show()
Activity of positive regulated genes#
elf1_g = np.array(GRN.index.tolist())[np.array(GRN.loc[:, "elf1"]) != 0]
fli1a_g = np.array(GRN.index.tolist())[np.array(GRN.loc[:, "fli1a"]) != 0]
score = adata[:, elf1_g].layers["Ms"].mean(1) - adata[:, fli1a_g].layers["Ms"].mean(1)
score = scipy.stats.zscore(np.array(score))
sns.scatterplot(x=score, y=-adata.obs["latent_time"])
max_abs_x = max(abs(np.min(score)), abs(np.max(score)))
plt.xlim(-max_abs_x, max_abs_x)
# Display the plot
plt.axvline(0, color="grey", linestyle="--") # Optional: add a vertical line at x=0 for clarity
<matplotlib.lines.Line2D at 0x7f34e1175f00>
adata.obsm["X_togglesiwtch"] = np.column_stack((score, -adata.obs["latent_time"]))
sc.pl.embedding(adata, basis="togglesiwtch", color="cell_type", palette=sc.pl.palettes.vega_20, legend_loc="on data")
adata.obs["ToggleState"] = [i if i in ["mNC_head_mesenchymal", "Pigment"] else np.nan for i in adata.obs["macrostates"]]
adata.obs["ToggleState"] = adata.obs["ToggleState"].astype("category")
which = "ToggleState"
# adata.obs[which] = adata.obs["cell_type2"].copy()
state_names = adata.obs[which].cat.categories.tolist()
adata.obs[which] = adata.obs[which].astype(str).astype("category").cat.reorder_categories(["nan"] + state_names)
if which == "ToggleState":
adata.uns[f"{which}_colors"] = ["#dedede"] + list(subset_palette.values())
else:
adata.uns[f"{which}_colors"] = ["#dedede"] + list(subset_palette.values())
state_names = adata.obs[which].cat.categories.tolist()[1:]
with mplscience.style_context():
fig, ax = plt.subplots(figsize=(4, 3))
scv.pl.scatter(adata, basis="togglesiwtch", c=which, add_outline=state_names, ax=ax, size=60)
if SAVE_FIGURES:
fig.savefig(FIG_DIR / DATASET / "fli1a_elf1.svg", format="svg", transparent=True, bbox_inches="tight")
if SAVE_DATA:
adata.write_h5ad(DATA_DIR / DATASET / "results" / "elf1_screening.csv")