<p>That is a good question. I have never done any Microarray experiments or data analysis myself. But I am very familiar with RNA-seq library workflows and data analysis. Library prep methods for RNA-seq involves many steps including use of several enzymes, which introduce certain level of bias. For example, reverse transcriptase used for making cDNA may copy certain mRNA sequences better than others. Other steps that involve enzymes: second strand synthesis, end-repair, ligation, and PCR amplification. Non-enzymatic steps could also introduce biases. I imagine Microarray and probe designs have also significant biases. There could be additional reasons I have not mentioned above, such as improper data analysis. If global gene expression levels are significantly different, I would suspect that it could be experimental reasons e.g. bad lab practices, unreliable reagents etc. I hope this helps.</p>

CC BY-SA 4.0
2022-02-08T08:29:58.717Z
And the biological state of the cell :-)
Pallie
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2022-02-08T14:00:42.100Z
Pallie:  I assumed that question is asked about the differences between using two different methods using the same RNA sample. If that is not the case, I agree with you it's not even fair comparison. And question should be edited to prevent confusion.
Supertech
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2022-02-08T01:58:51.040Z
2022-02-08T02:09:48.800Z
2022-02-08T02:09:48.800Z
Supertech
<p>It depends on the microarray and the cDNA sequencing sample prep.</p>
<p>One difference that I know about is that microarrays can be designed to target specific isoforms (e.g. spanning a isoform-specific splice boundary), whereas the shotgun sequencing approach of short-read cDNA sequencing makes this difficult to achieve (although in many cases isoform counting can be done to some degree by observing differences in coverage across transcripts).</p>
<p>However, microarrays are very heavily dependent on a good annotation. They can only detect existing known sequences, so it's possible that multiple isoforms are covered by a sequence that was previously believed to be isoform-unique, or that a sequence picked up by shotgun cDNA sequencing is not detected at all by a microarray.</p>

CC BY-SA 4.0
2022-02-08T21:07:35.023Z
2022-02-08T21:07:35.023Z
gringer
<p>What are some possible reasons why some trends observed in Microarray expression levels is not observed in RNA-seq. Example the difference between 2 cell types for a gene of interest show major differences when microarray is used with one cell type clearly having higher expression but rna-seq for the same cell types shows equal expression levels for the gene of interest?</p>
<p>What are possible explanations for the discrepancy between such results ? Shouldn't they have relatively identical trends otherwise gene expression in one of them is unreliable?</p>

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2022-02-08T02:26:49.007Z
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Community
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2022-02-09T18:17:25.813Z
I think you need to supply examples to support your observations, you are making a very sweeping statement there. For a start are you considering a relative analysis?
M__
<rna-seq><gene-expression><microarray>
What could explain the difference in RNA-seq and Microarray expression levels?
# What could explain the difference in RNA-seq and Microarray expression levels?

(Asked by: username_0 on 2022-02-07)

What are some possible reasons why some trends observed in Microarray expression levels is not observed in RNA-seq. Example the difference between 2 cell types for a gene of interest show major differences when microarray is used with one cell type clearly having higher expression but rna-seq for the same cell types shows equal expression levels for the gene of interest?

What are possible explanations for the discrepancy between such results ? Shouldn't they have relatively identical trends otherwise gene expression in one of them is unreliable?

**Tags:** rna-seq, gene-expression, microarray
> **Comments:**
> **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
> **username_2**: I think you need to supply examples to support your observations, you are making a very sweeping statement there. For a start are you considering a relative analysis?  

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## 2 answers

### Answer by username_3 on 2022-02-08. Score: 1

That is a good question. I have never done any Microarray experiments or data analysis myself. But I am very familiar with RNA-seq library workflows and data analysis. Library prep methods for RNA-seq involves many steps including use of several enzymes, which introduce certain level of bias. For example, reverse transcriptase used for making cDNA may copy certain mRNA sequences better than others. Other steps that involve enzymes: second strand synthesis, end-repair, ligation, and PCR amplification. Non-enzymatic steps could also introduce biases. I imagine Microarray and probe designs have also significant biases. There could be additional reasons I have not mentioned above, such as improper data analysis. If global gene expression levels are significantly different, I would suspect that it could be experimental reasons e.g. bad lab practices, unreliable reagents etc. I hope this helps.

> **Comments:**
> **username_4**: And the biological state of the cell :-)  
> **username_3**: Pallie:  I assumed that question is asked about the differences between using two different methods using the same RNA sample. If that is not the case, I agree with you it's not even fair comparison. And question should be edited to prevent confusion.  

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### Answer by username_5 on 2022-02-08. Score: 0

It depends on the microarray and the cDNA sequencing sample prep.

One difference that I know about is that microarrays can be designed to target specific isoforms (e.g. spanning a isoform-specific splice boundary), whereas the shotgun sequencing approach of short-read cDNA sequencing makes this difficult to achieve (although in many cases isoform counting can be done to some degree by observing differences in coverage across transcripts).

However, microarrays are very heavily dependent on a good annotation. They can only detect existing known sequences, so it's possible that multiple isoforms are covered by a sequence that was previously believed to be isoform-unique, or that a sequence picked up by shotgun cDNA sequencing is not detected at all by a microarray.


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