Figure S2a

Import libraries

import cellplots as cp
import larry
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats
import seaborn as sns

Read data

This version of the LARRY dataset has not been split for test and train

in_vitro = larry.datasets.inVitroData()
adata = in_vitro.compose_adata()

/home/mvinyard/.anaconda3/envs/sdq/lib/python3.9/site-packages/anndata/_core/anndata.py:522: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.
  warnings.warn(

- [ INFO ] | Added lineage x fate counts to: adata.uns['fate_counts']
- [ INFO ] | Added lineage-time occupance to: adata.uns['time_occupance']
- [ INFO ] | Fated cells annotated at: adata.obs['fate_observed']
- [ INFO ] | Fated cells (t=t0) annotated at: adata.obs['t0_fated']
- [ INFO ] | Added cell x fate counts to: adata.obsm['cell_fate_df']

Compute correlation across mean gene values

grouped = adata.obs.groupby(['Well', 'Time point'])

def _apply(df):
    return adata[df.index].X.mean(0).A.flatten()

gene_means = grouped.apply(_apply).to_dict()

Corr = {}
for i, x_i in gene_means.items():
    Corr[i] = {}
    for j, x_j in gene_means.items():
        if i != j:
            Corr[i][j], _ = scipy.stats.pearsonr(x_i, x_j)

corr_df = pd.DataFrame(Corr).sort_index()
corr_df = corr_df[[0, 2]].loc[[0, 1]].fillna(1)
corr_df

		0	2
		2.0	4.0	6.0
0	2.0	1.000000	0.964734	0.833761
1	4.0	0.972180	0.996787	0.925579
	6.0	0.815005	0.928177	0.997465

Plot correlation matrix

fig, axes = cp.plot(height = 0.5, width = 0.5)
sns.heatmap(corr_df, cmap = "Blues", vmin = 0.8, vmax = 1, annot=True, annot_kws={"size": 6}, fmt = ".3f", ax=axes[0])
plt.savefig("larry.figure_s2a.svg")
plt.savefig("larry.figure_s2a.png")