text: user796: I have two different sets of internal standards in a sample (1), and a sample (2) with these internal standards as well as about 30 peptides. Each peptide has one of each of these kind of standards. From sample 1, I can get the true ratio between standard 1 (S1) and standard 2 (S2), and from sample 2 I can get the ratio between the analyte (L) to each of the standards:
  Sample 1: S1/S2
  Sample 2: L/S1, L/S2
  I can thus calculate the ratio S1/S2 from the measured ratios in sample 2. Each sample is run in 5 technical replicates (that is; 5 injections into the LC-mass spectrometer), so S1/S2 ratios for one particular analyte could look like this (mock data):
  measured    calculated
  0.967       0.987
  1.007       0.967
  1.044       1.012
  1.041       1.025
  1.048       1.046
  
  I want to compare the measured ratios to the calculated, to see if there are any bias in my acquisition of data. As the calculated and the measured ratio are from different samples, the replicates aren't "linked" so I could not use for example Bland Altman analysis. Should I use some kind of ANOVA? Or do you have any other recommendation?
  I would also like to make an overall assessment of the ratios of all 30 peptides to see if the bias is different for different peptides. How could I do that?
  Any input would be greatly appreciated.
  
  user492: From a statistical perspective ratios are icky because the variance of the sampling distribution of a ratio decreases with the size of the numerator and denominator being sampled to create the ratio. If you can get at the numerator and denominator separately, then you should absolutely do that and look at count based tests like the Chi-square test, Fisher's exact test, or Poisson regression. 
  That said, a t-test would be appropriate in the two-sample case if its assumptions are met.  You need to check that both samples are normally distributed, ideally via a qq-plot. You also need to check the variance of both samples and choose a variant of the t-test that does or does not assume equal variances.
  If the samples are not normally distributed, then the next approach would be a Mann-Whitney U test.
  
original_text: 796: <p>I have two different sets of internal standards in a sample (1), and a sample (2) with these internal standards as well as about 30 peptides. Each peptide has one of each of these kind of standards. From sample 1, I can get the true ratio between standard 1 (S1) and standard 2 (S2), and from sample 2 I can get the ratio between the analyte (L) to each of the standards:</p>
  
  <p>Sample 1: S1/S2</p>
  
  <p>Sample 2: L/S1, L/S2</p>
  
  <p>I can thus calculate the ratio S1/S2 from the measured ratios in sample 2. Each sample is run in 5 technical replicates (that is; 5 injections into the LC-mass spectrometer), so S1/S2 ratios for one particular analyte could look like this (mock data):</p>
  
  <pre><code>measured    calculated
  0.967       0.987
  1.007       0.967
  1.044       1.012
  1.041       1.025
  1.048       1.046
  </code></pre>
  
  <p>I want to compare the measured ratios to the calculated, to see if there are any bias in my acquisition of data. As the calculated and the measured ratio are from different samples, the replicates aren't "linked" so I could not use for example Bland Altman analysis. Should I use some kind of ANOVA? Or do you have any other recommendation?</p>
  
  <p>I would also like to make an overall assessment of the ratios of all 30 peptides to see if the bias is different for different peptides. How could I do that?</p>
  
  <p>Any input would be greatly appreciated.</p>
   492: <p>From a statistical perspective ratios are icky because the variance of the sampling distribution of a ratio decreases with the size of the numerator and denominator being sampled to create the ratio. If you can get at the numerator and denominator separately, then you should absolutely do that and look at count based tests like the Chi-square test, Fisher's exact test, or Poisson regression. </p>
  
  <p>That said, a t-test would be appropriate in the two-sample case if its assumptions are met.  You need to check that both samples are normally distributed, ideally via a qq-plot. You also need to check the variance of both samples and choose a variant of the t-test that does or does not assume equal variances.</p>
  
  <p>If the samples are not normally distributed, then the next approach would be a <a href="https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test" rel="nofollow noreferrer">Mann-Whitney U</a> test.</p>
  