text: user7266: I am interested in identifying gene pairs (or better: sets of genes) whose expression is mutually exclusive.
  Ideally, both genes (or gene sets) would be widely expressed but I am also interested in cases that are very cell type specific.
  Familiar examples of the former would include some of the cyclin genes, in humans e.g. cyclin E and cyfclin B, which are expressed during S-phase and M-phase, respectively. Another example would be some of the clock-genes, where Per1 and Per2 are often highly expressed in the suprachiasmatic nucleus (SCN) before midday, and whereas Bmal1 is expressed after midday.
  However, such genes need not be cyclically expressed, they can simply be very specific markers for certain cell types. For example, GAD65 and GAD67 are very specific for interneurons, whereas CamKIIa is very specific for excitatory glutamatergic cells.
  I have been able to find a single paper so far that systematically tries to find such genes from gene expression data, namely: Tzanis & Vlahavas (2010) Mining for Mutually Exclusive Gene Expressions. According to Google Scholar, this paper is cited by a single other paper that does not seem to be relevant. In Tzanis & Vlahavas (2010), they use a SAGE library (from 2002) of short expression sequence tags to find these strong negative associations. By today's standards, the data set is pretty small (basically, it contains 90 samples). The paper itself is hence anything but a canonical source for such negative interactions.
  Questions:
  
  Is there a curated database of mutually exclusive gene pairs or gene sets?
  If not, is there a curated database that would allow me to find such genes?
  
  Otherwise, my current plan would be to use the human or mouse cell atlas single cell data to define a signed, weighted gene expression network.
  I would then search for pairs of clusters/modules, where genes have predominantly positive correlations within a cluster but predominantly negative correlations between clusters.
  So essentially, signed, weighted gene correlation network analysis (WGCNA) with extra steps.
  
  Has somebody published such an analysis already (the WGCNA literature is pretty substantial)?
  Is there other (recent) related work that I am unaware off?
  
  I am fairly new to bioinformatics, so the lack of papers and resources that I am able to find is likely mostly a reflection of my lack of ability to find the correct search term.
  Addendum
  Similar biostars thread from 5 months ago, also without an answer.
  
  user3848: "Mutually exclusive" is not a precise statistical term because it could be seen as independence. 
  Anyway if truly mutually exclusive AND their behaviour follows periodicity, i.e. sine waves, which it might do in a cell cycle, i.e. the cells divide at periodic intervals, you can do this via asynchronicity. 
  If the gene expression can be described under periodicity analysis you simply sum the amplitude between two genes. When the amplitude is zero ... they are expression is antogonistic - when one is expressed the other is silent. 
  Amplitude simply means the height of the period.
  Periodicity analysis is quite simple if you have uniform time measurements. 
  If you have 'missing data', i.e. you time intervals are not uniform, for example you skipped a reading then this is called missing data in periodicity analysis. Missing data periodicity analysis is complicated, but its doable.
  So in this context its quite simple, you would identify the periodicity of all genes and construct a 2x2 matrix subtracting their respective amplitudes. It would be kinda juicy to do. 
  
  The above is cool if the genes are periodic, however covariance is a better solution in general terms. 
  What you want is negative co-variance. If two genes had a covariance of -1 ... that will mutually exclusive. As above 2x2 matrix and the co-variance across the grid. You do need to be careful that the program you are using doesn't remove negative values of the covariance.
  Covariance = 1, meaning two genes are co-expressed
  Covariance = 0, random distribution
  Covariance = -1 mutually exclusive expression
  
  
original_text: 7266: <p><strong>I am interested in identifying gene pairs (or better: sets of genes) whose expression is mutually exclusive.</strong>
  Ideally, both genes (or gene sets) would be widely expressed but I am also interested in cases that are very cell type specific.</p>
  
  <p>Familiar examples of the former would include some of the cyclin genes, in humans e.g. cyclin E and cyfclin B, <a href="https://en.wikipedia.org/wiki/File:Cyclin_Expression.svg" rel="nofollow noreferrer">which are expressed during S-phase and M-phase, respectively.</a> Another example would be some of the clock-genes, where <a href="https://www.researchgate.net/publication/232233128_Early_Chronotype_and_Tissue-Specific_Alterations_of_Circadian_Clock_Function_in_Spontaneously_Hypertensive_Rats" rel="nofollow noreferrer">Per1 and Per2 are often highly expressed in the suprachiasmatic nucleus (SCN) before midday, and whereas Bmal1 is expressed after midday</a>.
  However, such genes need not be cyclically expressed, they can simply be very specific markers for certain cell types. For example, GAD65 and GAD67 are very specific for interneurons, whereas CamKIIa is very specific for excitatory glutamatergic cells.</p>
  
  <p>I have been able to find a single paper so far that systematically tries to find such genes from gene expression data, namely: <a href="https://www.researchgate.net/publication/221239044_Mining_for_Mutually_Exclusive_Gene_Expressions" rel="nofollow noreferrer">Tzanis &amp; Vlahavas (2010) Mining for Mutually Exclusive Gene Expressions</a>. According to Google Scholar, this paper is cited by a single other paper that does not seem to be relevant. In Tzanis &amp; Vlahavas (2010), they use a <a href="http://mlkd.csd.auth.gr/mutex/datasets.html" rel="nofollow noreferrer">SAGE library</a> (from 2002) of short expression sequence tags to find these strong negative associations. By today's standards, the data set is pretty small (basically, it contains 90 samples). The paper itself is hence anything but a canonical source for such negative interactions.</p>
  
  <h3>Questions:</h3>
  
  <ol>
  <li>Is there a curated database of mutually exclusive gene pairs or gene sets?</li>
  <li>If not, is there a curated database that would allow me to find such genes?</li>
  </ol>
  
  <p>Otherwise, my current plan would be to use the human or mouse cell atlas single cell data to define a signed, weighted gene expression network.
  I would then search for pairs of clusters/modules, where genes have predominantly positive correlations within a cluster but predominantly negative correlations between clusters.
  So essentially, signed, weighted gene correlation network analysis (WGCNA) with extra steps.</p>
  
  <ol start="3">
  <li><p>Has somebody published such an analysis already (the WGCNA literature is pretty substantial)?</p></li>
  <li><p>Is there other (recent) related work that I am unaware off?</p></li>
  </ol>
  
  <p>I am fairly new to bioinformatics, so the lack of papers and resources that I am able to find is likely mostly a reflection of my lack of ability to find the correct search term.</p>
  
  <h3>Addendum</h3>
  
  <p>Similar biostars <a href="https://www.biostars.org/p/397493/" rel="nofollow noreferrer">thread</a> from 5 months ago, also without an answer.</p>
   3848: <p>"Mutually exclusive" is not a precise statistical term because it could be seen as independence. </p>
  
  <p>Anyway if truly mutually exclusive AND their behaviour follows periodicity, i.e. sine waves, which it might do in a cell cycle, i.e. the cells divide at periodic intervals, you can do this via asynchronicity. </p>
  
  <p>If the gene expression can be described under periodicity analysis you simply sum the amplitude between two genes. When the amplitude is zero ... they are expression is antogonistic - when one is expressed the other is silent. </p>
  
  <p>Amplitude simply means the height of the period.</p>
  
  <p>Periodicity analysis is quite simple if you have uniform time measurements. </p>
  
  <p>If you have 'missing data', i.e. you time intervals are not uniform, for example you skipped a reading then this is called missing data in periodicity analysis. Missing data periodicity analysis is complicated, but its doable.</p>
  
  <p>So in this context its quite simple, you would identify the periodicity of all genes and construct a 2x2 matrix subtracting their respective amplitudes. It would be kinda juicy to do. </p>
  
  <hr>
  
  <p>The above is cool if the genes are periodic, however covariance is a better solution in general terms. </p>
  
  <p>What you want is <strong>negative co-variance</strong>. If two genes had a covariance of -1 ... that will mutually exclusive. As above 2x2 matrix and the co-variance across the grid. You do need to be careful that the program you are using doesn't remove negative values of the covariance.</p>
  
  <pre><code>Covariance = 1, meaning two genes are co-expressed
  Covariance = 0, random distribution
  Covariance = -1 mutually exclusive expression
  </code></pre>
  