VelocityKernel vs. RealTimeKernel - 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 / "realtime_kernel" / "pharyngeal_endoderm").mkdir(parents=True, exist_ok=True)

FIGURE_FORMAT = "pdf"

Constants

Data loading

tsi_cr1_full = pd.read_csv(DATA_DIR / "pharyngeal_endoderm" / "results" / "tsi-full_data-vk.csv")
tsi_cr1_full.head()

	number_of_macrostates	identified_terminal_states	optimal_identification
0	20	4	11
1	19	4	11
2	18	4	11
3	17	4	11
4	16	4	11

tsi_cr1_subset = pd.read_csv(DATA_DIR / "pharyngeal_endoderm" / "results" / "tsi-subsetted_data-vk.csv")
tsi_cr1_subset.head()

	number_of_macrostates	identified_terminal_states	optimal_identification
0	10	4	4
1	9	4	4
2	8	4	4
3	7	4	4
4	6	4	4

tsi_cr2_full = pd.read_csv(DATA_DIR / "pharyngeal_endoderm" / "results" / "tsi-full_data-rtk.csv")
tsi_cr2_full.head()

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

tsi_cr2_subset = pd.read_csv(DATA_DIR / "pharyngeal_endoderm" / "results" / "tsi-subsetted_data-rtk.csv")
tsi_cr2_subset.head()

	number_of_macrostates	identified_terminal_states	optimal_identification
0	10	4	4
1	9	4	4
2	8	4	4
3	7	4	4
4	6	4	4

Data preprocessing

tsi_cr1_full["method"] = "CellRank 1"
tsi_cr1_subset["method"] = "CellRank 1"

tsi_cr2_full["method"] = "CellRank 2"
tsi_cr2_subset["method"] = "CellRank 2"

df_full = pd.concat([tsi_cr1_full, tsi_cr2_full])
df_subset = pd.concat([tsi_cr1_subset, tsi_cr2_subset])

Plotting

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

if SAVE_FIGURES:
    fname = FIG_DIR / "realtime_kernel" / "pharyngeal_endoderm" / f"tsi_ranking-full_data.{FIGURE_FORMAT}"
else:
    fname = None

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

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

if SAVE_FIGURES:
    fname = FIG_DIR / "realtime_kernel" / "pharyngeal_endoderm" / f"tsi_ranking-subsetted_data.{FIGURE_FORMAT}"
else:
    fname = None

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