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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?

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  • $\begingroup$ Which microarray? Some have negative controls that can be used to assess the probability that a gene/transcript is expressed. $\endgroup$
    – Devon Ryan
    Jan 14 at 11:05
  • $\begingroup$ 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? $\endgroup$ Jan 14 at 21:45
  • $\begingroup$ At least some of those have the .CEL files included, so you have all of the data. $\endgroup$
    – Devon Ryan
    Jan 16 at 14:23
  • $\begingroup$ @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? $\endgroup$ Jan 16 at 16:04
  • $\begingroup$ Finding up and down regulated genes requires using R packages like affy and limma. Please search for tutorials. $\endgroup$
    – Devon Ryan
    Jan 16 at 16:48
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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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