text: user8575: I know this question may seem strange.
  I'm using Spearman correlation between gene expression profiles for various reasons (I won't go into details here). As a result, I often compare RNA-Seq and Microarray samples.
  For preliminary analysis, I usually grab what version of the data is easily accessible (RPKM, FPKM,...), but I'd like to dig a bit more.
  Intuitively, I'd think a value similar to RPKM or such would make more sense than raw counts, which is why I usually convert raw counts to RPKM (I know they're considered obsolete for statistical analysis of the data, what I'm doing relies on the correlation)
  So I ask what RNA-Seq gene expression value would be closest to a microarray equivalent ?
  i.e. What would (theoretically) maximize correlation between gene expression of the  same sample profiled with RNA-seq and microarray. RPKM ? FPKM ? TPM ? etc.
  
  user4766: I think it is very hard to say which are the closest because they are not really comparable. But since you are using Spearman correlation, I guess RPKM, FPKM, and TPM do not change the order of gene expression levels. You might also want to normalize RNA-seq and microarray data so that they are more comparable.
  
  user73: I did a comparison of cDNA count data against microarray data that was published a few years ago:
  
  For comparisons to published data (Fig. S2; Miller et al., 2012), a generalized linear model was fitted to the relationship between log-transformed microarray and VSTPk expression levels obtained from the ImmGen Project database, and was used to transform the microarray data into values comparable to our VSTPks.
  
  I found that the Variance-Stabilizing Transformation that was carried out by DESeq2 was close to what I wanted, but there still seemed to be a length-based bias to the reads. I corrected this by dividing by the length of the longest gene isoform in kilobases (creating something that I called VSTPk).
  After doing this, there were range differences between the microarray and cDNA data, so I did an additional linear transformation to get the data fitting as close as possible.
  
original_text: 8575: <p>I know this question may seem strange.</p>
  
  <p>I'm using Spearman correlation between gene expression profiles for various reasons (I won't go into details here). As a result, I often compare RNA-Seq and Microarray samples.
  For preliminary analysis, I usually grab what version of the data is easily accessible (RPKM, FPKM,...), but I'd like to dig a bit more.</p>
  
  <p>Intuitively, I'd think a value similar to RPKM or such would make more sense than raw counts, which is why I usually convert raw counts to RPKM (<strong>I know they're considered obsolete for statistical analysis of the data</strong>, what I'm doing relies on the correlation)</p>
  
  <p>So I ask what RNA-Seq gene expression value would be closest to a microarray equivalent ?</p>
  
  <p><em>i.e.</em> What would (theoretically) maximize correlation between gene expression of the  same sample profiled with RNA-seq and microarray. RPKM ? FPKM ? TPM ? etc.</p>
   4766: <p>I think it is very hard to say which are the closest because they are not really comparable. But since you are using Spearman correlation, I guess RPKM, FPKM, and TPM do not change the order of gene expression levels. You might also want to normalize RNA-seq and microarray data so that they are more comparable.</p>
   73: <p>I did a comparison of cDNA count data against microarray data that was <a href="https://doi.org/10.1084/jem.20160470" rel="nofollow noreferrer">published</a> a few years ago:</p>
  
  <blockquote>
    <p>For comparisons to published data (Fig. S2; Miller et al., 2012), a generalized linear model was fitted to the relationship between log-transformed microarray and VSTPk expression levels obtained from the ImmGen Project database, and was used to transform the microarray data into values comparable to our VSTPks.</p>
  </blockquote>
  
  <p>I found that the Variance-Stabilizing Transformation that was carried out by <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#variance-stabilizing-transformation" rel="nofollow noreferrer">DESeq2</a> was close to what I wanted, but there still seemed to be a length-based bias to the reads. I corrected this by dividing by the length of the longest gene isoform in kilobases (creating something that I called <em>VSTPk</em>).</p>
  
  <p>After doing this, there were range differences between the microarray and cDNA data, so I did an additional linear transformation to get the data fitting as close as possible.</p>
  