In this blog, we address the differential expression analysis of proteomics data. This involves preprocessing the data, filtering for quality, normalizing to ensure comparability, imputing missing values, and finally performing statistical tests to identify significant differences. The pipeline also includes comprehensive visualization to facilitate the interpretation of the results.

Important Steps of the Pipeline:

1. Data Preparation: The data is preprocessed by filtering out contaminants, ensuring unique protein names, and handling missing values. A summarized experiment object is created, which organizes the data according to the experimental design.

2. Normalization and Imputation: The data is normalized to ensure that samples can be compared on the same scale. Missing values are imputed based on their distribution to avoid biases in the analysis.

3. Differential Expression Analysis: Statistical tests are conducted to identify proteins that are significantly differentially expressed between conditions. Thresholds for adjusted p-value and log2 fold change are used to define significance.

4. Visualization: Various plots are generated to visualize the data and the results of the analysis. This includes PCA plots, heatmaps, volcano plots, and bar plots, which help in understanding the overall trends and specific differences between conditions.

5. Result Compilation: Significant proteins are identified and compiled into result tables. Data frames in wide and long formats are created for further analysis, and the results are saved for future reference.

By following these steps, the pipeline ensures a rigorous and comprehensive analysis of proteomics data, facilitating the identification of key proteins involved in different biological conditions.