Human hematopoiesis analysis with the CytoTRACEKernel

Contents

Human hematopoiesis analysis with the CytoTRACEKernel#

In this analysis, we use the CytoTRACEKernel to recover human bone marrow development. To run the analysis, ensure that the processed data is either already saved, or generate it using the corresponding notebook in the pseudotime/ directory.

The corresponding data can be generated through the preprocessing notebooks pseudotime/preprocessing.ipynb, or downloaded driectly from here and should be saved in data/bone_marrow/processed/adata.h5ad, i.e.,

mkdir -p ../../data/bone_marrow/processed/
wget https://figshare.com/ndownloader/files/53394941 -O ../../data/bone_marrow/processed/adata.h5ad

Library imports#

from tqdm import tqdm

import pandas as pd

import matplotlib.pyplot as plt
import mplscience
import seaborn as sns

import anndata as ad
import cellrank as cr
import scanpy as sc
import scvelo as scv

from crp import DATA_DIR, FIG_DIR

General settings#

cr.settings.verbosity = 4
sc.settings.verbosity = 2
scv.settings.verbosity = 3

scv.settings.set_figure_params("scvelo", dpi_save=400, dpi=80, transparent=True, fontsize=20, color_map="viridis")

Constants#

SAVE_FIGURES = False
if SAVE_FIGURES:
    (FIG_DIR / "cytotracekernel").mkdir(parents=True, exist_ok=True)

FIGURE_FORMAT = "svg"

SAVE_RESULTS = True
if SAVE_RESULTS:
    (DATA_DIR / "bone_marrow" / "results").mkdir(parents=True, exist_ok=True)

TERMINAL_STATES = ["Ery", "CLP", "cDC", "pDC", "Mono"]

STATE_TRANSITIONS = [
    ("HSC", "MEP"),
    ("MEP", "Ery"),
    ("HSC", "HMP"),
    ("HMP", "Mono"),
    ("HMP", "CLP"),
    ("HMP", "DCPre"),
    ("DCPre", "pDC"),
    ("DCPre", "cDC"),
]

Function definitions#

Data loading#

adata = ad.io.read_h5ad(DATA_DIR / "bone_marrow" / "processed" / "adata.h5ad")
adata

AnnData object with n_obs × n_vars = 6881 × 1500
    obs: 'leiden', 'celltype', 'n_counts', 'palantir_pseudotime'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'celltype_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'umap'
    obsm: 'X_diffmap', 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

Data preprocessing#

with mplscience.style_context():
    fig, ax = plt.subplots(figsize=(6, 6))
    scv.pl.scatter(adata, basis="umap", color=["celltype"], size=25, dpi=100, title="", ax=ax)
    plt.show()

../_images/f246455b62111b5acf49a9ffaaf7bf3ee087de0a699082e4e172be804fbf28d7.png

adata.layers["spliced"] = adata.X
adata.layers["unspliced"] = adata.X

scv.pp.moments(adata, n_pcs=None, n_neighbors=None)

computing moments based on connectivities
    finished (0:00:00) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)

CytoTRACEKernel#

Kernel#

ctk = cr.kernels.CytoTRACEKernel(adata)
ctk.compute_cytotrace()

Computing CytoTRACE score with `1500` genes. Consider using more than `10000` genes
DEBUG: Correlating all genes with number of genes expressed per cell
Adding `adata.obs['ct_score']`
       `adata.obs['ct_pseudotime']`
       `adata.obs['ct_num_exp_genes']`
       `adata.var['ct_gene_corr']`
       `adata.var['ct_correlates']`
       `adata.uns['ct_params']`
    Finish (0:00:00)

CytoTRACEKernel[n=6881]

with mplscience.style_context():
    scv.pl.scatter(
        adata, basis="umap", color=["celltype", "ct_pseudotime"], legend_loc="right", color_map="viridis", size=25
    )
    plt.show()

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:656: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(

../_images/1ee66e96762748620086ee374ddc3bd9c46fc0231b709f0895c0e305edb3b884.png

ctk.compute_transition_matrix(threshold_scheme="soft", nu=0.5)

Computing transition matrix based on pseudotime

    Finish (0:00:00)

CytoTRACEKernel[n=6881, dnorm=False, scheme='soft', b=10.0, nu=0.5]

Estimator#

estimator = cr.estimators.GPCCA(ctk)
estimator.compute_schur(n_components=20)
with mplscience.style_context():
    if SAVE_FIGURES:
        save = FIG_DIR / "cytotracekernel" / f"spectrum.{FIGURE_FORMAT}"
    else:
        save = False
    estimator.plot_spectrum(real_only=True, figsize=(6, 3), save=save)
    plt.show()

Computing Schur decomposition
Adding `adata.uns['eigendecomposition_fwd']`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:01)

../_images/5e6a6ddba21e1eb41d84a6b0552e3b355843d55630c8a93977ae30a7d530970d.png

estimator.compute_macrostates(4, cluster_key="celltype")

with mplscience.style_context():
    estimator.plot_macrostates(which="all", basis="umap", legend_loc="right", title="", size=100)
    if SAVE_FIGURES:
        fig, ax = plt.subplots(figsize=(6, 6))
        estimator.plot_macrostates(which="all", basis="umap", legend_loc=False, title="", size=100, ax=ax)
        fig.savefig(
            FIG_DIR / "cytotracekernel" / "4_macrostates.png",
            dpi=400,
            transparent=True,
            bbox_inches="tight",
        )

    plt.show()

Computing `4` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:656: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:694: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1396: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1397: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

../_images/20107002e38bd1fae08eb2f06d8a7bb0bc8c6d5590689ec03246c35402172e00.png

estimator.compute_macrostates(7, cluster_key="celltype")

with mplscience.style_context():
    estimator.plot_macrostates(which="all", basis="umap", legend_loc="right", title="", size=100)
    if SAVE_FIGURES:
        fig, ax = plt.subplots(figsize=(6, 6))
        estimator.plot_macrostates(which="all", basis="umap", legend_loc=False, title="", size=100, ax=ax)
        fig.savefig(
            FIG_DIR / "cytotracekernel" / "7_macrostates.png",
            dpi=400,
            transparent=True,
            bbox_inches="tight",
        )

    plt.show()

Computing `7` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:656: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:694: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1396: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1397: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

../_images/928ce8392a7ed146b2c375be800872f390c1de67d4c8894008d3b959b62475d0.png

estimator.compute_macrostates(15, cluster_key="celltype")

with mplscience.style_context():
    estimator.plot_macrostates(which="all", basis="umap", legend_loc="right", title="", size=100)
    if SAVE_FIGURES:
        fig, ax = plt.subplots(figsize=(6, 6))
        estimator.plot_macrostates(which="all", basis="umap", legend_loc=False, title="", size=100, ax=ax)
        fig.savefig(
            FIG_DIR / "cytotracekernel" / "15_macrostates.png",
            dpi=400,
            transparent=True,
            bbox_inches="tight",
        )

    plt.show()

Computing `15` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
WARNING: The following terminal states have different number of cells than requested (30): {'cDC_2': 27}
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:16)

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:656: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:694: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1396: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1397: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

../_images/d31e28e85b648c680ab0143aa3427d00ebbaa760bd595e7c462980757eb77830.png

cluster_key = "celltype"

tsi_score = estimator.tsi(n_macrostates=15, terminal_states=TERMINAL_STATES, cluster_key=cluster_key)
print(f"TSI score: {tsi_score:.2f}")

if SAVE_RESULTS:
    estimator._tsi.to_df().to_parquet(DATA_DIR / "bone_marrow" / "results" / "ct_tsi.parquet")

Computing `15` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
WARNING: The following terminal states have different number of cells than requested (30): {'cDC_2': 27}
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:16)
Computing `14` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:12)
Computing `13` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
WARNING: The following terminal states have different number of cells than requested (30): {'cDC_1': 28}
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:06)
Computing `12` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Computing `11` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:07)
Computing `10` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
WARNING: The following terminal states have different number of cells than requested (30): {'cDC_1': 27, 'cDC_2': 7}
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:05)
Computing `9` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:02)
Computing `8` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `7` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `6` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `5` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `4` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `3` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Computing `2` macrostates
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
For 1 macrostate, stationary distribution is computed
Computing eigendecomposition of the transition matrix
DEBUG: Computing top `20` eigenvalues of a sparse matrix
DEBUG: Sorting eigenvalues by their real part
Adding `adata.uns['eigendecomposition_fwd']`
       `.eigendecomposition`
    Finish (0:00:00)
DEBUG: Setting the macrostates using macrostates memberships
DEBUG: Raising an exception if there are less than `6` cells.
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
TSI score: 0.85

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/anndata/utils.py:311: UserWarning: X converted to numpy array with dtype float64
  warnings.warn(f"{name} converted to numpy array with dtype {arr.dtype}")
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.
  warnings.warn("Transforming to str index.", ImplicitModificationWarning)

palette = {"CytoTRACEKernel": "#DE8F05", "Optimal identification": "#000000"}

if SAVE_FIGURES:
    save = FIG_DIR / "cytotracekernel" / f"tsi_ranking.{FIGURE_FORMAT}"
else:
    save = None

with mplscience.style_context():
    sns.set_style(style="whitegrid")
    estimator.plot_tsi(palette=palette, save=save)
    plt.show()

../_images/b0ae65fb51afadc8399fb17354c54000f7aff963d15717d75186f39ec5df1c38.png

print(f"Identified macrostates: {', '.join(estimator.macrostates.cat.categories.sort_values())}")

Identified macrostates: CLP, DCPre_1, DCPre_2, Ery_1, Ery_2, Ery_3, HMP, HSC, MEP_1, MEP_2, Mono, cDC_1, cDC_2, pDC_1, pDC_2

estimator.set_terminal_states(["Ery_1", "CLP", "pDC_1", "cDC_1", "Mono"])
estimator.rename_terminal_states({"Ery_1": "Ery", "pDC_1": "pDC", "cDC_1": "cDC"})

celltype_palette = dict(zip(adata.obs["celltype"].cat.categories, adata.uns["celltype_colors"]))
terminal_state_colors = [
    celltype_palette[terminal_state] for terminal_state in estimator.adata.obs["term_states_fwd"].cat.categories
]
estimator._term_states = estimator._term_states.set(colors=terminal_state_colors)

with mplscience.style_context():
    estimator.plot_macrostates(which="terminal", basis="umap", legend_loc="right", title="", size=100)

    if SAVE_FIGURES:
        fig, ax = plt.subplots(figsize=(6, 6))
        estimator.plot_macrostates(which="terminal", basis="umap", legend_loc=False, title="", size=100, ax=ax)
        fig.savefig(
            FIG_DIR / "cytotracekernel" / "terminal_states.png",
            dpi=400,
            transparent=True,
            bbox_inches="tight",
        )

    plt.show()

DEBUG: Raising an exception if there are less than `6` cells.
Adding `adata.obs['term_states_fwd']`
       `adata.obs['term_states_fwd_probs']`
       `.terminal_states`
       `.terminal_states_probabilities`
       `.terminal_states_memberships
    Finish`

/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:656: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  smp = ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/scatter.py:694: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1396: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=bg_size, marker=".", c=bg_color, zorder=zord - 2, **kwargs)
/Users/philipp/miniforge3/envs/crp-py311/lib/python3.11/site-packages/scvelo/plotting/utils.py:1397: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(x, y, s=gp_size, marker=".", c=gp_color, zorder=zord - 1, **kwargs)

../_images/f0602b029e877456168e4e73b4c6e0849e823a0f25185d414027ab0b7f7e658f.png

estimator.compute_fate_probabilities()
with mplscience.style_context():
    estimator.plot_fate_probabilities(same_plot=False, basis="umap", ncols=5)
    plt.show()

if SAVE_FIGURES:
    bdata = adata.copy()
    bdata.obs[estimator.fate_probabilities.names.tolist()] = estimator.fate_probabilities.X

    for lineage in estimator.fate_probabilities.names.tolist():
        with mplscience.style_context():
            fig, ax = plt.subplots(figsize=(6, 6))
            scv.pl.scatter(bdata, basis="umap", color=lineage, cmap="viridis", title="", colorbar=False, ax=ax)
            fig.savefig(
                FIG_DIR / "cytotracekernel" / f"{lineage.lower()}_fate_terminal_set.png",
                dpi=400,
                transparent=True,
                bbox_inches="tight",
            )
            plt.show()
    del bdata

Computing fate probabilities
DEBUG: Solving the linear system using `PETSc` solver `'gmres'` on `1` core(s) with no preconditioner and `tol=1e-06`

Adding `adata.obsm['lineages_fwd']`
       `.fate_probabilities`
    Finish (0:00:00)

../_images/822ac3bc5351b10b0ccb45ddbf5718401e0a00bc1736249f2b7c2baf5e8b50c1.png

cluster_key = "celltype"
rep = "X_pca"

cbc = []
for source, target in tqdm(STATE_TRANSITIONS):
    _cbc = ctk.cbc(source=source, target=target, cluster_key=cluster_key, rep=rep)

    cbc.append(
        pd.DataFrame(
            {
                "state_transition": [f"{source} - {target}"] * len(_cbc),
                "cbc": _cbc,
            }
        )
    )
cbc = pd.concat(cbc)

if SAVE_RESULTS:
    cbc.to_parquet(DATA_DIR / "bone_marrow" / "results" / "ct_cbc.parquet")

cbc.head()

100%|██████████| 8/8 [00:00<00:00, 13.31it/s]

	state_transition	cbc
0	HSC - MEP	0.799256
1	HSC - MEP	0.572809
2	HSC - MEP	0.702549
3	HSC - MEP	0.409971
4	HSC - MEP	0.866234