我试图使用弗里德曼测试来识别统计学上显著的蛋白质。对于样本数据集，数值为中位数（因为进行了多项试验，但已进行了总结）。有两种单独的培养基和每种培养基的对照。已经收集了给定时间段内每种培养基中蛋白质的量的数据。我的目标是比较两种培养基中的蛋白质，并确定哪些蛋白质在两种培养基中的含量存在统计学差异。
为了做到这一点，我尝试了弗里德曼测试，然后使用事后邓恩测试。我有下面的代码，但我在让它工作时遇到了麻烦。我想知道是否有人可以提供任何帮助或见解，为什么我的代码不工作？如果您有其他建议的测试等。为我所用那也会很受欢迎

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.stats import friedmanchisquare
from scikit_posthocs import posthoc_dunn

# Sample data
data = {
    'Protein': ['Protein1', 'Protein2', 'Protein3', 'Protein4', 'Protein5'],
    'Control_Medium1': [1.0, 1.1, 1.2, 1.0, 1.3],
    'Control_Medium2': [1.1, 1.0, 1.1, 1.2, 1.0],
    'Medium1_T1': [1.5, 1.5, 1.8, 2.0, 1.6],
    'Medium1_T2': [1.2, 1.3, 1.7, 1.9, 2.1],
    'Medium1_T3': [1.1, 1.6, 1.9, 1.4, 1.7],
    'Medium2_T1': [0.1, 1.2, 1.7, 1.8, 1.5],
    'Medium2_T2': [0.2, 1.4, 1.8, 1.7, 1.9],
    'Medium2_T3': [0.3, 1.5, 1.3, 1.7, 1.4],
}

df = pd.DataFrame(data)

# Perform Friedman test
control_medium1 = df['Control_Medium1']
control_medium2 = df['Control_Medium2']
medium1_t1 = df['Medium1_T1']
medium1_t2 = df['Medium1_T2']
medium1_t3 = df['Medium1_T3']
medium2_t1 = df['Medium2_T1']
medium2_t2 = df['Medium2_T2']
medium2_t3 = df['Medium2_T3']

_, p_value = friedmanchisquare(control_medium1, control_medium2, medium1_t1, medium1_t2, medium1_t3,
                               medium2_t1, medium2_t2, medium2_t3)

# Print Friedman test result
print('Friedman Test:')
print('p-value:', p_value)

# Perform pairwise comparisons with posthoc test
data_for_posthoc = [medium1_t1, medium1_t2, medium1_t3, medium2_t1, medium2_t2, medium2_t3]
labels_for_posthoc = ['Medium1_T1', 'Medium1_T2', 'Medium1_T3', 'Medium2_T1', 'Medium2_T2', 'Medium2_T3']

posthoc_result = sp.posthoc_dunn(data_for_posthoc)

# Apply Bonferroni correction to p-values
alpha = 0.05
corrected_p_values = np.multiply(posthoc_result, len(posthoc_result.columns))

# Get the significantly different proteins
significant_proteins = []

for protein in df.columns[1:]:
    if any(corrected_p_values[protein] < alpha):
        significant_proteins.append(protein)

print('Significant Proteins:', significant_proteins)

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我试着在网上寻找关于这些统计测试代码的更多信息，但我一直无法找到为什么我的代码不工作。