<p>I am new to working with gene expression data sets and am wondering what is the most standard or best way to impute missing values in a gene expression data? I got mine from the GEO database and the accession number is GSE70947. This is a breast cancer data set with a total of 296 samples (148 normal/148 cancer) and there are 62976 features (genes).</p>
<p>The original data is only log2 normalized by the experimenter.</p>
<p>When I first queried the data from GEO database using GEOquery from Bioconductor, the data set had NAs in it and a lot of them -- I found that there are 729494 of them in the data set.</p>
<p><a href="https://i.sstatic.net/3enT2.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/3enT2.png" alt="enter image description here" /></a></p>
<pre><code>sum(is.na(gene.expr))
[1] 729494
</code></pre>
<p>Now, I am looking online to see what are the most common imputation methods for gene expression data, but there are so many (local, global, hybrid, knowledge assisted methods) and I don't really know which is more suitable for my data set. I am just wondering what is the best way to impute the NAs. Should I just impute the NAs to 0s?</p>

CC BY-SA 4.0
2022-04-22T08:07:10.633Z
Once I imputed NAs to 0s and that was a mistake. NA is 'no available' record and it can be anything. So you can work with logs, do na.omit of your z-scores or download raw data and process it by yourself with bioconductor. It's Agilent-028004 SurePrint G3 Human GE 8x60K Microarray, so it should be a package to deal with it. Then you can do anything with normalized signals.
Adamm
CC BY-SA 4.0
2022-04-23T15:59:10.083Z
What about Bayesian approaches like mc-stan.org? Those generally provide good approaches for working with missing data.
winni2k
CC BY-SA 4.0
2022-04-24T02:41:35.173Z
@Adamm  You said, "It's Agilent-028004 SurePrint G3 Human GE 8x60K Microarray, so it should be a package to deal with it". How so? Where can I find it?
b14108
<r><bioconductor><gene-expression><imputation>
Missing value imputation method for gene expression data
# Missing value imputation method for gene expression data

(Asked by: username_0 on 2022-04-21)

I am new to working with gene expression data sets and am wondering what is the most standard or best way to impute missing values in a gene expression data? I got mine from the GEO database and the accession number is GSE70947. This is a breast cancer data set with a total of 296 samples (148 normal/148 cancer) and there are 62976 features (genes).
The original data is only log2 normalized by the experimenter.
When I first queried the data from GEO database using GEOquery from Bioconductor, the data set had NAs in it and a lot of them -- I found that there are 729494 of them in the data set.

sum(is.na(gene.expr))
[1] 729494

Now, I am looking online to see what are the most common imputation methods for gene expression data, but there are so many (local, global, hybrid, knowledge assisted methods) and I don't really know which is more suitable for my data set. I am just wondering what is the best way to impute the NAs. Should I just impute the NAs to 0s?

**Tags:** r, bioconductor, gene-expression, imputation
> **Comments:**
> **username_1**: Once I imputed NAs to 0s and that was a mistake. NA is 'no available' record and it can be anything. So you can work with logs, do na.omit of your z-scores or download raw data and process it by yourself with bioconductor. It's Agilent-028004 SurePrint G3 Human GE 8x60K Microarray, so it should be a package to deal with it. Then you can do anything with normalized signals.  
> **username_2**: What about Bayesian approaches like mc-stan.org? Those generally provide good approaches for working with missing data.  
> **username_0**: @Adamm  You said, "It's Agilent-028004 SurePrint G3 Human GE 8x60K Microarray, so it should be a package to deal with it". How so? Where can I find it?  

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## 0 answers

