To add statistical tests and determine whether there is a significant difference between your two samples, db2_ratio (wild type) and db1_ratio (mutants), we can use a statistical test like the t-test or Mann-Whitney U test, depending on the data's distribution.

Here’s how you can modify your existing plot_cleavage_ratio function and your plotting script to include statistical analysis:

Key Modifications:

1. Statistical test: We will add a t-test to check if there is a significant difference between the two samples (db1_ratio and db2_ratio). If the data is not normally distributed, consider using a non-parametric test like the **Mann-Whitney U test.

2. Results display: We'll annotate the plot with the p-value from the statistical test to visually indicate whether the difference between the samples is statistically significant.

3. Statistical test logic:

   • t-test: We assume that the data follows a normal distribution. If not, a non-parametric test such as Mann-Whitney U test can be performed instead.
   • p-value interpretation: If the p-value is less than a certain threshold (commonly 0.05), we reject the null hypothesis, indicating that the difference is statistically significant.

Updated plot_cleavage_ratio function:

python
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import seaborn as sns
from mpl_toolkits.axes_grid1.inset_locator import make_axes_locatable
from scipy.stats import gaussian_kde

def plot_cleavage_ratio(data1, label1):
    x = data1['db2_ratio']  # Wild type
    y = data1['db1_ratio']  # Mutants
    
    # Perform t-test to compare the two samples (wild type vs mutants)
    t_stat, p_value = ttest_ind(x, y, equal_var=False)  # Welch’s    # Display the p-value on the plot
    p_value_text = f"P-value: {p_value:.3e}"
    
    # Density plot
    xy = np.vstack([x, y])
    z = gaussian_kde(xy)(xy)
    idx = z.argsort()
    x, y, z = x.iloc[idx], y.iloc[idx], z[idx]
    
    plt.xlabel(label1)
    ax = plt.gca()
    plt.plot([0, 1], [0, 1], ls='--', color='#555555')  # Diagonal line

    xticks = np.array([0.00, 0.25, 0.5, 0.75, 1.00])
.xticks(xticks)
    
    scatter = ax.scatter(x, y, marker='o', c=z, s=5, cmap="RdYlBu_r", label='LST')

    # Add statistical annotation (p-value) on the plot
    ax.text(0.5, 0.05, p_value_text, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=12, color='black')
    
    sns.despine(top=True, right=True)
    
    return ax

Changes to the main plotting script:

You can keep most of your plotting script as-is. The major update is just the inclusion of the statistical test and p-value annotation in the function plot_cleavage_ratio.

Here's how to update your main script to call the updated plotting function:

python
plt.figure(figsize=(12, 12))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)

plt.subplot(4,4,1)
plot_cleavage_ratio(fy_rep2_cleavage, 'Sample 1')

plt.subplot(4,4,2)
plot_cleavage_ratio(cpsf30_rep2_cleavage, 'Sample 2')

plt.subplot(4,4,3)
plot_cleavage_ratio(fip1_rep2_cleavage, 'Sample 3')

plt.subplot(4,4,4)
plot_cleavage_ratio(esp5_rep2_cleavage, 'Sample 4')

plt.subplot(4,4,5)
plot_cleavage_ratio(fpa_rep1_c, 'Sample 5')

plt.subplot(4,4,6)
plot_cleavage_ratio(fca_rep2_cleavage, 'Sample 6')

plt.subplot(4,4,7)
plot_cleavage_ratio(paps1_rep2_cleavage, 'Sample 7')

plt.subplot(4,4,8)
plot_cleavage_ratio(paps24_rep2_cleavage, 'Sample 8')

plt.subplot(4,4,9)
plot_cleavage_ratio(cstf64_rep2_cleavage, 'Sample 9')

plt.subplot(4,4,10)
plot_cleavage_ratio(cstf77_rep2_cleavage, 'Sample 10')

plt.subplot(4,4,11)
plot_cleavage_ratio(pcfs4_rep2_cleavage, 'Sample 11')

plt.subplot(4,4,12)
plot_cleavage_ratio(bdr123_rep2_cleavage, 'Sample 12')

plt.subplot(4,4,13)
plot_cleavage_ratio(ssu72_rep2_cleavage, 'Sample 13')

plt.subplot(4,4,14)
plot_cleavage_ratio(xrn3_rep2_GB_cleavage, 'Sample 14')

sns.despine(top=True, right=True)

Explanation of Updates:

• The function plot_c performs a **Welch’s t-test** (for unequal variances), compares the db2_ratio(wild type) anddb1_ratio` (mutants), and displays the p-value on the plot.
• The p-value helps you assess if there is a statistically significant difference between the two samples. If the p-value is below a threshold (typically 0.05), it suggests a significant difference.