text: user704: I'm running the pathifier approach against C2 pathway curated
  database for a specific microarray dataset.
  As I was reading the documentation of the pathifier, in order to configure it properly for my dataset, I saw that for the min_std argument they suggest to use the technical noise.
  
  min_std:   The minimal allowed standard deviation of each gene. Genes
  with lower standard deviation are divided by min_std instead of their
  actual standard deviation. (Recommended: set min_std to be the
  technical noise).
  
  But how am I going to calculate it ? At first run I just used the value 0.2254005 from the example they provide with that package, but this might not be the right for my dataset.
  Update:
  So from here it says: The minimal standard error (min_std) was set as the first quartile of the standard deviation in the data. How am I supposed to calculate this first quartile of the std? Any idea? Should I calculate the sd() for each gene and then find the first quartile?
  What do you think about this function?
  
  user73: Answer from @llrs, converted from comment:
  Probably is a question for the maintainer but I would guess that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such variation the altered ones should have these or more according to the article (if I understand it correctly).
  std <- apply(data[, controls], 1, sd, na.rm = TRUE); quantile(std, 0.25) should be much faster and efficient
  But  I would check it in support.bioconductor with the authors/maintainers.
  
original_text: 704: <p>I'm running the <a href="https://www.bioconductor.org/packages/release/bioc/html/pathifier.html" rel="nofollow noreferrer">pathifier</a> approach against C2 pathway curated
  database for a specific microarray dataset.</p>
  <p>As I was reading the documentation of the pathifier, in order to configure it properly for my dataset, I saw that for the <code>min_std</code> argument they suggest to use the technical noise.</p>
  <blockquote>
  <p>min_std:   The minimal allowed standard deviation of each gene. Genes
  with lower standard deviation are divided by min_std instead of their
  actual standard deviation. (Recommended: set min_std to be the
  technical noise).</p>
  </blockquote>
  <p>But how am I going to calculate it ? At first run I just used the value <code>0.2254005</code> from the example they provide with that package, but this might not be the right for my dataset.</p>
  <p>Update:</p>
  <p>So from <a href="http://www.weizmann.ac.il/complex/compphys/software/yotam/pathifier/help.htm" rel="nofollow noreferrer">here</a> it says: <code>The minimal standard error (min_std) was set as the first quartile of the standard deviation in the data</code>. How am I supposed to calculate this first quartile of the std? Any idea? Should I calculate the sd() for each gene and then find the first quartile?</p>
  <p>What do you think about this <a href="https://pastebin.com/ni5uKhpX" rel="nofollow noreferrer">function</a>?</p>
   73: <p><em>Answer from @llrs, converted from comment:</em></p>
  <p>Probably is a question for the maintainer but I would <em>guess</em> that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such variation the altered ones should have these or more according to the article (if I understand it correctly).</p>
  <p><code>std &lt;- apply(data[, controls], 1, sd, na.rm = TRUE); quantile(std, 0.25)</code> should be much faster and efficient</p>
  <p>But  I would check it in <a href="https://support.bioconductor.org" rel="nofollow noreferrer">support.bioconductor</a> with the authors/maintainers.</p>
  