Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-04-22T08:07:10.633Z
Comments.Text: 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.
Comments.UserDisplayName: Adamm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-04-23T15:59:10.083Z
Comments.Text: What about Bayesian approaches like mc-stan.org? Those generally provide good approaches for working with missing data.
Comments.UserDisplayName: winni2k
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-04-24T02:41:35.173Z
Comments.Text: @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?
Comments.UserDisplayName: b14108
Tags: <r><bioconductor><gene-expression><imputation>
Title: Missing value imputation method for gene expression data
ThreadText: # 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.
  
  [![enter image description here](https://i.sstatic.net/3enT2.png)](https://i.sstatic.net/3enT2.png)
  
  ```
  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**: @username_1  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?  
  
  ---
  
  ## 0 answers
  
  