proteomics-analysis

To identify differentially expressed proteins in mass spectrometry proteomics data, you provide a protein intensity matrix and sample metadata. The tool applies normalization, imputation, and Welch's t-test with FDR correction to output DE results.

Yes, this proteomics analysis workflow supports TMT, LFQ, and DIA datasets. It processes your protein intensity matrices to perform batch correction and generate QC metrics for treated vs control experimental designs.

To generate volcano plots and heatmaps for upregulated proteins, run the differential expression analysis on your normalized data. The tool outputs publication-ready visualizations directly from the computed DE results.

Available normalization methods for DIA proteomics data include median, quantile, log2-median, and VSN-like workflows. The tool also handles missing value imputation to ensure comparable protein abundances across samples.

Yes, the tool provides QC metrics per sample including missing value patterns, coefficient of variation (CV), and correlations. These metrics assess data quality and reliability before running differential expression analysis.