<p>You ask about which &quot;genes are expressed&quot; and then you mention &quot;if a gene is up or down regulated&quot;. These are different, and given your application I think what you actually want to know if these marker genes are expressed. This is not a question of up/down-regulation relative to a control, so you do not need control samples for this.</p>
<p>It seems most of your datasets are Affymetrix, and there are R/Bioconductor packages that will give you the probability a gene is expressed. One such I found is frma (<a href="https://bioconductor.org/packages/release/bioc/html/frma.html" rel="nofollow noreferrer">https://bioconductor.org/packages/release/bioc/html/frma.html</a>), which returns an object &quot;weight&quot; that is &quot;a vector of weights which roughly correspond to the probability of expression for each gene.&quot; I haven't worked with microarrays in a while, but I recall many other R/Bioconductor packages offering this functionality.</p>
<p>A detection p-value is calculated for Illumina array probes in Illumina's native BeadStudio software.</p>

CC BY-SA 4.0
2021-01-21T01:09:41.153Z
2021-01-21T01:15:16.717Z
2021-01-21T01:15:16.717Z
user1799035
<p>I have a microarray gene expression dataset consisting of placenta samples. I want to check whether these placenta samples are mixed with maternal decidua tissue. I have marker genes for decidua samples SCARA5, PIGF, SLPI. How to check whether these genes are expressed in placenta samples? In other words, how to check if a gene is up or down regulated without control group?</p>

CC BY-SA 4.0
2021-01-14T11:05:55.563Z
Which microarray? Some have negative controls that can be used to assess the probability that a gene/transcript is expressed.
Devon Ryan
CC BY-SA 4.0
2021-01-14T21:45:23.087Z
I look at theese datasets: GSE73374, GSE73685, GSE9984, GSE122214, GSE22490, GSE35574, GSE37901. The microarray platforms are Affymetix and Illumina: affy_hugene_2_0_st_v1, affy_hugene_1_0_st_v1, affy_hg_u133_plus_2, illumina_humanwg_6_v2. Unfortunately I do not have access to illumina raw data, only to the preprocessed one. Can I still get negative controls from it?
Sashko Lykhenko
CC BY-SA 4.0
2021-01-16T14:23:04.687Z
At least some of those have the .CEL files included, so you have all of the data.
Devon Ryan
CC BY-SA 4.0
2021-01-16T16:04:25.300Z
@DevonRyan I've looked .CELL files' description and indeed there are control probes. Could you give me a hint, how do I move from these raw data to the lists of up- and down-regulated genes?
Sashko Lykhenko
CC BY-SA 4.0
2021-01-16T16:48:11.030Z
Finding up and down regulated genes requires using R packages like affy and limma. Please search for tutorials.
Devon Ryan
CC BY-SA 4.0
2021-01-16T19:48:43.717Z
Thank you @DevonRyan I will look them up more closely. But aren't these methods require having case and control groups of samples and not just one group like in my case?
Sashko Lykhenko
CC BY-SA 4.0
2021-01-16T19:50:08.337Z
Your previous comment asked for up and down-regulated genes. That requires some sort of treatment groups, yes.
Devon Ryan
CC BY-SA 4.0
2021-01-16T19:56:17.187Z
Oh, I see now. I said up- and down-regulated, but I meant expressed and unexpressed relative to control probes. My bad.
Sashko Lykhenko
<gene-expression><transcriptome><microarray>
How to check if a given gene is expressed in a group of microarray samples if I do not have control group to compare with?
# How to check if a given gene is expressed in a group of microarray samples if I do not have control group to compare with?

(Asked by: username_0 on 2021-01-13)

I have a microarray gene expression dataset consisting of placenta samples. I want to check whether these placenta samples are mixed with maternal decidua tissue. I have marker genes for decidua samples SCARA5, PIGF, SLPI. How to check whether these genes are expressed in placenta samples? In other words, how to check if a gene is up or down regulated without control group?

**Tags:** gene-expression, transcriptome, microarray
> **Comments:**
> **username_1**: Which microarray? Some have negative controls that can be used to assess the probability that a gene/transcript is expressed.  
> **username_0**: I look at theese datasets: GSE73374, GSE73685, GSE9984, GSE122214, GSE22490, GSE35574, GSE37901. The microarray platforms are Affymetix and Illumina: affy_hugene_2_0_st_v1, affy_hugene_1_0_st_v1, affy_hg_u133_plus_2, illumina_humanwg_6_v2. Unfortunately I do not have access to illumina raw data, only to the preprocessed one. Can I still get negative controls from it?  
> **username_1**: At least some of those have the .CEL files included, so you have all of the data.  
> **username_0**: @DevonRyan I've looked .CELL files' description and indeed there are control probes. Could you give me a hint, how do I move from these raw data to the lists of up- and down-regulated genes?  
> **username_1**: Finding up and down regulated genes requires using R packages like affy and limma. Please search for tutorials.  
> **username_0**: Thank you @DevonRyan I will look them up more closely. But aren't these methods require having case and control groups of samples and not just one group like in my case?  
> **username_1**: Your previous comment asked for up and down-regulated genes. That requires some sort of treatment groups, yes.  
> **username_0**: Oh, I see now. I said up- and down-regulated, but I meant expressed and unexpressed relative to control probes. My bad.  

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## 1 answers

### Answer by username_2 on 2021-01-21. Score: 3 | Accepted answer

You ask about which "genes are expressed" and then you mention "if a gene is up or down regulated". These are different, and given your application I think what you actually want to know if these marker genes are expressed. This is not a question of up/down-regulation relative to a control, so you do not need control samples for this.

It seems most of your datasets are Affymetrix, and there are R/Bioconductor packages that will give you the probability a gene is expressed. One such I found is frma (<https://bioconductor.org/packages/release/bioc/html/frma.html>), which returns an object "weight" that is "a vector of weights which roughly correspond to the probability of expression for each gene." I haven't worked with microarrays in a while, but I recall many other R/Bioconductor packages offering this functionality.

A detection p-value is calculated for Illumina array probes in Illumina's native BeadStudio software.


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