VelocityKernel vs. PseudotimeKernel - TSI

Library imports

%load_ext autoreload
%autoreload 2

import sys

import pandas as pd

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

from cr2.analysis import plot_tsi

sys.path.extend(["../../../", "."])
from paths import DATA_DIR, FIG_DIR  # isort: skip  # noqa: E402

Global seed set to 0

General settings

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

FIGURE_FORMAT = "pdf"

Constants

Data loading

tsi_cr1 = pd.read_csv(DATA_DIR / "hematopoiesis" / "results" / "tsi-vk.csv")
tsi_cr1.head()

	number_of_macrostates	identified_terminal_states	optimal_identification
0	17	3	4
1	16	3	4
2	15	3	4
3	14	3	4
4	13	3	4

tsi_cr2 = pd.read_csv(DATA_DIR / "hematopoiesis" / "results" / "tsi-ptk.csv")
tsi_cr2.head()

	number_of_macrostates	identified_terminal_states	optimal_identification
0	7	4	4
1	6	4	4
2	5	3	4
3	4	3	4
4	3	3	3

Data preprocessing

tsi_cr1["method"] = "CellRank 1"
tsi_cr2["method"] = "CellRank 2"

df = pd.concat([tsi_cr1, tsi_cr2])

Plotting

palette = {"CellRank 1": "#0173b2", "CellRank 2": "#DE8F05", "Optimal identification": "#000000"}

if SAVE_FIGURES:
    fname = FIG_DIR / "pseudotime_kernel" / "hematopoiesis" / f"tsi_ranking.{FIGURE_FORMAT}"
else:
    fname = None

with mplscience.style_context():
    sns.set_style(style="whitegrid")
    plot_tsi(df=df, n_macrostates=7, palette=palette, fname=fname)
    plt.show()