Hi All,
I am working on HT-MS. I want to compare MS data(proteomic) from two samples/experiments.
Any suggestions are very welcome
Harman In order to compare High Throughput MS data from different samples, begin by organizing protein IDs and intensity values (from tools like MaxQuant or Proteome Discoverer) into a table. Normalize the data using log2 transformation and total or median normalization. Perform statistical analysis (t-test or non-parametric tests) and adjust for multiple testing using FDR. Identify differentially expressed proteins based on fold-change (e.g., ±1.5) and significance (e.g., p ≤ 0.05). Visualize results using volcano plots and perform PCA or clustering to assess sample separation. For functional insights, analyze significant proteins using DAVID, g:Profiler, or Metascape to extract GO terms and pathway associations. Use STRING-db or Cytoscape to explore interaction networks. Optionally, apply machine learning (e.g., random forest) to identify key discriminative proteins. This approach helps you uncover biological meaning from proteomic differences and highlight potential pathogenic or biomarker proteins between conditions. Tools like Perseus, R (limma, MSstats), or Python can support the full analysis pipeline.
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Thank you for helping and for guiding.
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