<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>

CC BY-SA 4.0
2020-04-12T08:17:03.600Z
2020-04-12T08:17:03.600Z
gringer
<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>

CC BY-SA 4.0
2020-04-09T14:32:05.217Z
2020-04-09T14:32:05.217Z
Phoenix Mu
<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>

CC BY-SA 4.0
2020-04-08T17:54:50.567Z
“I know they're considered obsolete for statistical analysis of the data” — No, they’re considered obsolete *for every purpose*. Just don’t use them. Read https://bioinformatics.stackexchange.com/a/69/29.
Konrad Rudolph
<rna-seq><transcriptome><microarray><fpkm>
What RNA-Seq expression value would be closest to Microarray equivalent?
# What RNA-Seq expression value would be closest to Microarray equivalent?

(Asked by: username_0 on 2020-04-08)

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.

**Tags:** rna-seq, transcriptome, microarray, fpkm
> **Comments:**
> **username_1**: “I know they're considered obsolete for statistical analysis of the data” — No, they’re considered obsolete *for every purpose*. Just don’t use them. Read https://bioinformatics.stackexchange.com/a/69/29.  

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## 2 answers

### Answer by username_2 on 2020-04-12. Score: 2

I did a comparison of cDNA count data against microarray data that was [published](https://doi.org/10.1084/jem.20160470) 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](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#variance-stabilizing-transformation) 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.


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### Answer by username_3 on 2020-04-09. Score: 2

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.


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