Welch's t-test: Identifying Genes with Significant Differences

This guide demonstrates how to perform a Welch's t-test in R to identify genes with significant expression differences between two groups (group_A and group_B).

Code Example:

result <- group_A %>%
  inner_join(group_B, by = 'ID') %>%
  t_test(Gene1.x ~ Gene1.y, p.adjust.method = 'bonferroni')  # Assuming the second column is Gene1

Understanding the Data:

Gene1.x and Gene1.y represent the expression levels of gene 1 in group_A and group_B, respectively.

Explanation:

inner_join(group_B, by = 'ID'): This step merges the data frames group_A and group_B based on a common identifier column named 'ID'.
t_test(Gene1.x ~ Gene1.y, p.adjust.method = 'bonferroni'): This performs a Welch's t-test comparing the expression levels of Gene1.x (group_A) against Gene1.y (group_B). The p.adjust.method = 'bonferroni' argument applies the Bonferroni correction for multiple testing, which adjusts p-values to control for false positives.

Interpreting the Results:

The output of the t_test function will provide p-values and other statistical information. If the p-value is below a predetermined significance threshold (e.g., 0.05), it indicates that the difference in gene expression between the two groups is statistically significant.

This approach can be applied to identify genes with significant differences in expression between various groups of interest, enabling you to uncover potential biological insights.