text: user3851: I'm analyzing single cell rna-seq data and trying to compute the correlation score between different clusters. Wondering how to choose the correlation method("pearson" (default), "kendall", or "spearman") of the Cor function?
  I'm a new comer in bioinformatics and know little about these arithmetics. Can anyone give me some advice or show me the code to decide which method to choose for my data matrix?
  Below is my gene expression matrix: rows are gene names,and columns are clusters.I use the default Peasrson method to do analysis,but I really don't know if it is right.
  
  CorOb.cor.exp <- as.data.frame(cor(CorOb.av.exp))
  CorOb.cor.exp$x <- rownames(CorOb.cor.exp)
  neupeptide.cor.df <- tidyr::gather(data = CorOb.cor.exp, y, correlation,CorOb.cor.exp$x)
  a <- ggplot(neupeptide.cor.df, aes(x, y, fill = correlation)) + geom_raster()+scale_fill_gradientn(colors = c("blue", "white", "red"))+ 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                     size = 12, hjust = 1))+
    coord_fixed()
  
  a
  
  user3541: Disclaimer: This is not a complete answer but more a comment, posting as an answer because of character restrictions for comments. Also, I am not a statistican but a molecular biologist who did quite some reading/courses on stats.
  Two scRNA-seq tools, namely SingleR and RCA (Reference Component Analysis, Li et. al, 2017) compute correlations of bulk transcriptomic data to single cells; for this they use Spearman correlation and Pearson correlation respectively (The two tools use the same bulk transcriptome reference). I am using these two tools for different purposes so I did not really compare their results, however, single cell clusters that I compute using the reference components (calcuated with RCA) is in good agreement with single cells labels computed with RCA (adjusted rand index of 0.81).
  In addition to that, please take a look at scMatch: a single-cell gene expression profile annotation tool using reference datasets, they claim (scMatch computes bulk - single cell correlations like SingleR and RCA):
  
  From this comparison we found Spearman’s correlation coefficient
    outperformed Pearson’s correlation for all six libraries.
  
  
original_text: 3851: <p>I'm analyzing single cell rna-seq data and trying to compute the correlation score between different clusters. Wondering how to choose the correlation method("pearson" (default), "kendall", or "spearman") of the Cor function?</p>
  
  <p>I'm a new comer in bioinformatics and know little about these arithmetics. Can anyone give me some advice or show me the code to decide which method to choose for my data matrix?</p>
  
  <p>Below is my gene expression matrix: rows are gene names,and columns are clusters.I use the default Peasrson method to do analysis,but I really don't know if it is right.
  <a href="https://user-images.githubusercontent.com/30619010/66716137-313b7600-edfd-11e9-810b-5bf7e58959ff.png" rel="nofollow noreferrer"><img src="https://user-images.githubusercontent.com/30619010/66716137-313b7600-edfd-11e9-810b-5bf7e58959ff.png" alt="my data matrix"></a></p>
  
  <pre><code>CorOb.cor.exp &lt;- as.data.frame(cor(CorOb.av.exp))
  CorOb.cor.exp<span class="math-container">$x &lt;- rownames(CorOb.cor.exp)
  neupeptide.cor.df &lt;- tidyr::gather(data = CorOb.cor.exp, y, correlation,CorOb.cor.exp$</span>x)
  a &lt;- ggplot(neupeptide.cor.df, aes(x, y, fill = correlation)) + geom_raster()+scale_fill_gradientn(colors = c("blue", "white", "red"))+ 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                     size = 12, hjust = 1))+
    coord_fixed()
  </code></pre>
  
  <p>a</p>
   3541: <p>Disclaimer: This is not a complete answer but more a comment, posting as an answer because of character restrictions for comments. Also, I am not a statistican but a molecular biologist who did quite some reading/courses on stats.</p>
  
  <p>Two scRNA-seq tools, namely <code>SingleR</code> and <code>RCA</code> (Reference Component Analysis, Li et. al, 2017) compute correlations of bulk transcriptomic data to single cells; for this they use <code>Spearman correlation</code> and <code>Pearson correlation</code> respectively (The two tools use the same bulk transcriptome reference). I am using these two tools for different purposes so I did not really compare their results, however, single cell clusters that I compute using the <code>reference components</code> (calcuated with <code>RCA</code>) is in good agreement with single cells labels computed with <code>RCA</code> (adjusted rand index of 0.81).</p>
  
  <p>In addition to that, please take a look at <a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz292/5480299" rel="nofollow noreferrer">scMatch: a single-cell gene expression profile annotation tool using reference datasets</a>, they claim (scMatch computes bulk - single cell correlations like SingleR and RCA):</p>
  
  <blockquote>
    <p>From this comparison we found Spearman’s correlation coefficient
    outperformed Pearson’s correlation for all six libraries.</p>
  </blockquote>
  