Answers.Body: <p>These values represent the expression of a particular gene in each sample. While expression of an immune system gene like <code>CXLC12</code> might be associated with the aggressiveness of a cancer, that's not going to be the case for the majority of genes.</p>
  
  <p>Looking at the numbers, these look like they are probably log$_2$-transformed values. In order to know for sure, plotting a density distribution of the dataset is a good idea:</p>
  
  <pre><code>plot(density(exprs(TCGA_eset)));
  </code></pre>
  
  <p>If the values are in the range of around 0..15, then it'll be log$_2$-transformed values, whereas if they're in the range of 0..100000, it'll be "raw" expression values. The log-transformed values are preferred for data analysis because gene expression tends to be close to a normal distribution in log-space.</p>
  
  <p>These numbers are best treated as relative counts (i.e. the most appropriate <em>unit</em> is "expression"), as variation in the preparation of the sample (or the sequencer, or the microarray) can influence the results that come out the other end. Converting them to something more meaningful (e.g. "transcripts per cell") requires normalising against a known reference sample.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-02-20T05:29:25.307Z
Answers.LastActivityDate: 2018-02-20T20:04:29.387Z
Answers.LastEditDate: 2018-02-20T20:04:29.387Z
Answers.UserDisplayName: gringer
Answers.Body: <p>The values are <code>rma</code> normalized expression values from affy microarrays. They are indeed log2 transformed (as @gringer already expected). You can find more info about the data <a href="https://bioconductor.org/packages/release/data/experiment/manuals/curatedOvarianData/man/curatedOvarianData.pdf" rel="nofollow noreferrer">here</a>, under heading <code>TCGA_eset</code>.</p>
  
  <p>To get more info about <code>rma</code>, check <a href="https://www.rdocumentation.org/packages/affy/versions/1.50.0/topics/rma" rel="nofollow noreferrer">this</a> out.</p>
  
  <p>Larger values are not necessarily greater cancer possibility, I wish biology was that simple. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-02-20T20:22:05.553Z
Answers.LastActivityDate: 2018-02-20T20:22:05.553Z
Answers.UserDisplayName: benn
Body: <p>I am wondering how to interpret values inside the gene expression levels contained within Bioconductor. For example, if we have the following commands via <code>R</code>, we get:</p>
  
  <pre><code>source("https://bioconductor.org/biocLite.R") 
  biocLite("curatedOvarianData")
  library(curatedOvarianData)
  data(TCGA_eset)
  head(exprs(TCGA_eset)["CXCL12",])
  
  TCGA.20.0987 TCGA.23.1031 TCGA.24.0979 TCGA.23.1117 TCGA.23.1021 TCGA.04.1337 
      5.437923     6.307250     4.193748     9.071293     5.101532     6.697770 
  </code></pre>
  
  <p>The above calls out gene expression levels for the gene <code>CXCL12</code>. However, I am wondering how to interpret the values <code>5.437923</code>, <code>6.307250</code>, etc. </p>
  
  <p>I know they are expression levels, but do they come with units? Do larger values mean greater cancer possibility?</p>
  
Tags: <r><bioconductor><gene>
Title: When working with Bioconductor and the curatedOvarianData dataset, what are the unit values of the expression data?
ThreadText: # When working with Bioconductor and the curatedOvarianData dataset, what are the unit values of the expression data?
  
  (Asked by: username_0 on 2018-02-20)
  
  I am wondering how to interpret values inside the gene expression levels contained within Bioconductor. For example, if we have the following commands via `R`, we get:
  
  ```
  source("https://bioconductor.org/biocLite.R") 
  biocLite("curatedOvarianData")
  library(curatedOvarianData)
  data(TCGA_eset)
  head(exprs(TCGA_eset)["CXCL12",])
  
  TCGA.20.0987 TCGA.23.1031 TCGA.24.0979 TCGA.23.1117 TCGA.23.1021 TCGA.04.1337 
      5.437923     6.307250     4.193748     9.071293     5.101532     6.697770 
  
  ```
  
  The above calls out gene expression levels for the gene `CXCL12`. However, I am wondering how to interpret the values `5.437923`, `6.307250`, etc.
  
  I know they are expression levels, but do they come with units? Do larger values mean greater cancer possibility?
  
  **Tags:** r, bioconductor, gene
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2018-02-20. Score: 2
  
  These values represent the expression of a particular gene in each sample. While expression of an immune system gene like `CXLC12` might be associated with the aggressiveness of a cancer, that's not going to be the case for the majority of genes.
  
  Looking at the numbers, these look like they are probably log$\_2$-transformed values. In order to know for sure, plotting a density distribution of the dataset is a good idea:
  
  ```
  plot(density(exprs(TCGA_eset)));
  
  ```
  
  If the values are in the range of around 0..15, then it'll be log$\_2$-transformed values, whereas if they're in the range of 0..100000, it'll be "raw" expression values. The log-transformed values are preferred for data analysis because gene expression tends to be close to a normal distribution in log-space.
  
  These numbers are best treated as relative counts (i.e. the most appropriate *unit* is "expression"), as variation in the preparation of the sample (or the sequencer, or the microarray) can influence the results that come out the other end. Converting them to something more meaningful (e.g. "transcripts per cell") requires normalising against a known reference sample.
  
  
  ---
  
  ### Answer by username_2 on 2018-02-20. Score: 2
  
  The values are `rma` normalized expression values from affy microarrays. They are indeed log2 transformed (as @username_1 already expected). You can find more info about the data [here](https://bioconductor.org/packages/release/data/experiment/manuals/curatedOvarianData/man/curatedOvarianData.pdf), under heading `TCGA_eset`.
  
  To get more info about `rma`, check [this](https://www.rdocumentation.org/packages/affy/versions/1.50.0/topics/rma) out.
  
  Larger values are not necessarily greater cancer possibility, I wish biology was that simple.
  
  
  ---
  
  