<p>Based on the info you provide, <a href="https://cran.r-project.org/web/packages/ArrayBin/ArrayBin.pdf" rel="nofollow noreferrer">ArrayBin</a> R package provides you the necessary tools:</p>

<ul>
<li><code>binarize.array()</code> from <a href="https://cran.r-project.org/web/packages/ArrayBin/ArrayBin.pdf" rel="nofollow noreferrer">ArrayBin</a>, allowing:</li>
</ul>

<blockquote>
  <p>Implementation of an adaptive method for binarizing gene expression data on a per-probe basis and demonstrate the superior effectiveness of our method when compared with other, commonly used approaches. This adaptive binarization method can be applied to DNA methylation microarray data,
  which has implications for cross-platform integration, and can reduce batch effects in the data.</p>
</blockquote>

<p>In complement, I'd advise you go through some literature and choose the approach which fits you best. Some suggestions:</p>

<ul>
<li><a href="https://www.nature.com/articles/35076576" rel="nofollow noreferrer">Computational analysis of microarray data</a></li>
<li><a href="https://link.springer.com/article/10.1007/s10618-009-0145-2" rel="nofollow noreferrer">Binary matrix factorization for analyzing gene expression data</a></li>
<li>and probably most relevant for you given your application: <a href="http://sci-hub.tw/10.1109/tcbb.2011.62" rel="nofollow noreferrer">Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks</a></li>
</ul>

<p>Maybe you also want to look into more general concepts of transformation prior to microarray data analysis:</p>

<ul>
<li><a href="http://www.cs.cmu.edu/~zivbj/class05/reading/norm.pdf" rel="nofollow noreferrer">Microarray data normalization and transformation</a></li>
<li><a href="http://www.ub.edu/stat/docencia/bioinformatica/microarrays/ADM/slides/A_Tutorial_Review_of_Microarray_data_Analysis_17-06-08.pdf" rel="nofollow noreferrer">A Tutorial Review of Microarray Data Analysis</a></li>
</ul>

CC BY-SA 3.0
2018-04-10T06:20:44.623Z
2018-04-10T06:20:44.623Z
aechchiki
<p>I am using gene expression data from a microarray dataset for the purpose of learning the network structure of genes in the dataset. </p>

<p>Now to do this I need to binarize my data, i.e convert the data for each gene to 0s and 1s. I am new to bioinformatics and am not sure what approach is considered standard for microarray data. </p>

<p>I appreciate your inputs regarding this and I am implementing this in R. So if you know any package that does this please let me know.</p>

<r><microarray><data-preprocessing>
What is a standard approach to binarize microarray gene expression data?
# What is a standard approach to binarize microarray gene expression data?

(Asked by: username_0 on 2018-04-10)

I am using gene expression data from a microarray dataset for the purpose of learning the network structure of genes in the dataset.

Now to do this I need to binarize my data, i.e convert the data for each gene to 0s and 1s. I am new to bioinformatics and am not sure what approach is considered standard for microarray data.

I appreciate your inputs regarding this and I am implementing this in R. So if you know any package that does this please let me know.

**Tags:** r, microarray, data-preprocessing

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## 1 answers

### Answer by username_1 on 2018-04-10. Score: 2

Based on the info you provide, [ArrayBin](https://cran.r-project.org/web/packages/ArrayBin/ArrayBin.pdf) R package provides you the necessary tools:

* `binarize.array()` from [ArrayBin](https://cran.r-project.org/web/packages/ArrayBin/ArrayBin.pdf), allowing:

> Implementation of an adaptive method for binarizing gene expression data on a per-probe basis and demonstrate the superior effectiveness of our method when compared with other, commonly used approaches. This adaptive binarization method can be applied to DNA methylation microarray data,
> which has implications for cross-platform integration, and can reduce batch effects in the data.

In complement, I'd advise you go through some literature and choose the approach which fits you best. Some suggestions:

* [Computational analysis of microarray data](https://www.nature.com/articles/35076576)
* [Binary matrix factorization for analyzing gene expression data](https://link.springer.com/article/10.1007/s10618-009-0145-2)
* and probably most relevant for you given your application: [Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks](http://sci-hub.tw/10.1109/tcbb.2011.62)

Maybe you also want to look into more general concepts of transformation prior to microarray data analysis:

* [Microarray data normalization and transformation](http://www.cs.cmu.edu/~zivbj/class05/reading/norm.pdf)
* [A Tutorial Review of Microarray Data Analysis](http://www.ub.edu/stat/docencia/bioinformatica/microarrays/ADM/slides/A_Tutorial_Review_of_Microarray_data_Analysis_17-06-08.pdf)


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