Comparing immune infiltration across groups involves analyzing differences in immune cell presence or activity in distinct sample categories, such as healthy vs. diseased tissues. Statistical tests like t-tests, ANOVA, or non-parametric methods (e.g., Mann-Whitney U or Kruskal-Wallis) assess variations in infiltration levels derived from gene expression or imaging data. These analyses determine whether observed differences are statistically significant, considering variables like sample size and distribution. Advanced methods, such as multivariate regression or machine learning, may integrate additional factors. Accurate interpretation requires robust data preprocessing, normalization, and validation. Such comparisons aid in understanding immune dynamics, biomarker discovery, and therapeutic targeting.

🌵Python snippet

To compare immune infiltration levels across groups using statistical tests such as ANOVA or Kruskal-Wallis, we can follow these steps in Python:

1. Data Preparation: Import the data and structure it with immune infiltration levels as the dependent variable and groups as the independent variable.
2. Descriptive Statistics: Summarize the data with mean and standard deviation for each group.
3. Statistical Tests:
   • Use ANOVA if the data meets the assumptions of normality and homogeneity of variances.
   • Use the Kruskal-Wallis test as a non-parametric alternative when assumptions are not met.
4. Post-Hoc Testing: If the test indicates significant differences, perform post-hoc pairwise comparisons to identify which groups differ.

Here's a Python implementation:

 import pandas as pd
 import numpy as np
 from scipy.stats import f_oneway, kruskal
 from statsmodels.stats.multicomp import pairwise_tukeyhsd
 from scipy.stats import shapiro, levene
 ​
 # Example Data
 # Replace this with your actual data
 data = {
     'group': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
     'immune_infiltration': [5.1, 5.5, 6.0, 7.2, 6.9, 7.5, 8.0, 7.8, 8.2]
 }
 df = pd.DataFrame(data)
 ​
 # Descriptive Statistics
 print(df.groupby('group')['immune_infiltration'].agg(['mean', 'std']))
 ​
 # Test Assumptions
 # Normality
 for group in df['group'].unique():
     stat, p = shapiro(df[df['group'] == group]['immune_infiltration'])
     print(f"Shapiro-Wilk test for group {group}: p = {p}")

 # Homogeneity of variances
 stat, p = levene(
     *[df[df['group'] == g]['immune_infiltration'] for g in df['group'].unique()]
 )
 print(f"Levene's test for homogeneity of variances: p = {p}")
 ​
 # Choose Test
 if p > 0.05:  # Homogeneity of variances
     stat, p = f_oneway(
         *[df[df['group'] == g]['immune_infiltration'] for g in df['group'].unique()]
     )
     print(f"ANOVA result: p = {p}")
 else:
     stat, p = kruskal(
         *[df[df['group'] == g]['immune_infiltration'] for g in df['group'].unique()]
     )
     print(f"Kruskal-Wallis result: p = {p}")
 ​
 # Post-Hoc Testing if Significant
 if p < 0.05:
     tukey = pairwise_tukeyhsd(
         endog=df['immune_infiltration'], groups=df['group'], alpha=0.05
     )
     print(tukey)

Key Points:

1. Assumption Testing:
   • Shapiro-Wilk test checks normality for each group.
   • Levene’s test checks homogeneity of variances.
2. Statistical Test:
   • If the data is normally distributed and variances are equal, use ANOVA.
   • If assumptions are violated, use Kruskal-Wallis.
3. Post-Hoc Analysis:
   • Tukey's HSD test for ANOVA.
   • Pairwise comparisons (e.g., Dunn's test) for Kruskal-Wallis, which can be done using scipy or statsmodels.

🌵 R snippet

To compare immune infiltration across groups using statistical tests like ANOVA and Kruskal-Wallis in R, follow these steps. These tests assess whether there are statistically significant differences in immune infiltration values across groups (e.g., tumor types, treatment groups). Here’s a detailed guide:

1. Prepare Your Data

Ensure your data is in a format with the following structure:

• SampleID: Unique identifier for each sample.