text: user16445: I am new to proteomics analysis, so apologies if my question is silly! For context, I am working with proteome samples from the postmortem human brain for a case-control study.
  Our lab generally receives an output file of Proteome Discoverer* (v2.5) from the proteomics center that we work with. From what I understand, PD perform t-tests on the normalized data, but it is a background-based t-test, so there is a weighting factor is applied that takes into account the distribution of ratios for all other proteins and/or the protein abundances themselves? As such, I am able to see an "Abundance Ratio Adj. P-Value".
  My problem is that my data does not meet the assumptions for a parametric test and it has a small sample size, so the t-test is not valid (as far as I can tell, unless there is some sort of log transformation that PD does to the data before the t-test). Therefore, I would like to run a Mann-Whitney U Test. Can this be done in the PD software?
  If not, how can I do this background adjustment manually such that I can run a Mann-Whitney U Test but still take into protein abundance like in PD? I do most of my stats in R in case that is relevant.
  Thank you so much in advance for your help!
  Kelly
  
  Package distributed by ThermoFisher
  
  user16448: Small samples size and parametric tests are not mutually exclusive. Specialized software has been developed to leverage sharing information across  measurements to boost power, that is called Empirical Bayes. You can use the limma package for this sort of analysis, contrasting groups. Input is expected to be normalized and on log2-scale. It's an R package hosted at Bioconductor. Don't try a MWU, with small sample size that is massively underpowered.
  
original_text: 16445: <p>I am new to proteomics analysis, so apologies if my question is silly! For context, I am working with proteome samples from the postmortem human brain for a case-control study.</p>
  <p>Our lab generally receives an output file of <a href="https://www.thermofisher.com/uk/en/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/proteome-discoverer-software.html" rel="nofollow noreferrer">Proteome Discoverer</a><strong>*</strong> (v2.5) from the proteomics center that we work with. From what I understand, PD perform t-tests on the normalized data, but it is a background-based t-test, so there is a weighting factor is applied that takes into account the distribution of ratios for all other proteins and/or the protein abundances themselves? As such, I am able to see an &quot;Abundance Ratio Adj. P-Value&quot;.</p>
  <p>My problem is that my data <strong>does not meet the assumptions for a parametric test</strong> and it has a small sample size, so the t-test is not valid (as far as I can tell, unless there is some sort of log transformation that PD does to the data before the t-test). Therefore, I would like to run a Mann-Whitney U Test. Can this be done in the PD software?</p>
  <p>If not, <strong>how can I do this background adjustment manually such that I can run a Mann-Whitney U Test but still take into protein abundance like in PD?</strong> I do most of my stats in R in case that is relevant.</p>
  <p>Thank you so much in advance for your help!</p>
  <p>Kelly</p>
  <hr />
  <p>Package distributed by ThermoFisher</p>
   16448: <p>Small samples size and parametric tests are not mutually exclusive. Specialized software has been developed to leverage sharing information across  measurements to boost power, that is called Empirical Bayes. You can use the limma package for this sort of analysis, contrasting groups. Input is expected to be normalized and on log2-scale. It's an R package hosted at Bioconductor. Don't try a MWU, with small sample size that is massively underpowered.</p>
  