Answers.Body: <p>I can offer this as a way to scrape the data from the Abcam resource:</p>
  
  <pre><code>library(rvest)
  library(dplyr)
  
  antigens &lt;- read_html("http://www.abcam.com/primary-antibodies/human-cd-antigen-guide") %&gt;% 
    html_node("table") %&gt;% 
    html_table(header = TRUE)
  
  # what targets expressed in "Langerhans" ?
  antigens %&gt;% 
    filter(grepl("Langerhans", `Cellular Expression`)) %&gt;% 
    select(`Target Name`)
  
     Target Name
  1         CD1a
  2         CD1b
  3         CD1c
  4         CD1e
  5         CD14
  6         CD39
  7         CD50
  8         CD69
  9         CD74
  10        CD83
  11       CD101
  12       CD206
  13       CD207
  </code></pre>
  
  <p>The data could be further processed by, for example, splitting the <code>Cellular Expression</code> column on either <code>and</code> or <code>,</code> (comma) to try and make one row per cell type.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-10-18T03:12:21.230Z
Answers.LastActivityDate: 2017-10-18T06:33:57.653Z
Answers.LastEditDate: 2017-10-18T06:33:57.653Z
Answers.UserDisplayName: neilfws
Body: <p>There are 394 <a href="https://www.genenames.org/cgi-bin/genefamilies/set/471" rel="nofollow noreferrer">CD marker genes</a>. Various resources (<a href="https://www.bdbiosciences.com/documents/cd_marker_handbook.pdf" rel="nofollow noreferrer">BD handbook</a>, <a href="http://www.abcam.com/primary-antibodies/human-cd-antigen-guide" rel="nofollow noreferrer">Abcam guide</a>) contain information about which cell types express which CD markers. What is the best way to obtain or download this data so that it can be used with programs such as <code>R</code>?</p>
  
  <p>How can we download similar annotation data on expression for any gene or cell type?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-17T21:44:52.320Z
Comments.Text: Most of the work of biologist is to characterize the cells and populations, mostly because they reason why they express/use them is mostly unkown. I am not aware of a single database listing markers of a cell line, but it would be quite useful.
Comments.UserDisplayName: llrs
Tags: <r><sequence-annotation><gene-ontology><flow-cytometry>
Title: Obtain CD (cluster of differentiation) marker expression information
ThreadText: # Obtain CD (cluster of differentiation) marker expression information
  
  (Asked by: username_0 on 2017-10-17)
  
  There are 394 [CD marker genes](https://www.genenames.org/cgi-bin/genefamilies/set/471). Various resources ([BD handbook](https://www.bdbiosciences.com/documents/cd_marker_handbook.pdf), [Abcam guide](http://www.abcam.com/primary-antibodies/human-cd-antigen-guide)) contain information about which cell types express which CD markers. What is the best way to obtain or download this data so that it can be used with programs such as `R`?
  
  How can we download similar annotation data on expression for any gene or cell type?
  
  **Tags:** r, sequence-annotation, gene-ontology, flow-cytometry
  
  > **Comments:**
  > **username_1**: Most of the work of biologist is to characterize the cells and populations, mostly because they reason why they express/use them is mostly unkown. I am not aware of a single database listing markers of a cell line, but it would be quite useful.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-10-18. Score: 5 | Accepted answer
  
  I can offer this as a way to scrape the data from the Abcam resource:
  
  ```
  library(rvest)
  library(dplyr)
  
  antigens <- read_html("http://www.abcam.com/primary-antibodies/human-cd-antigen-guide") %>% 
    html_node("table") %>% 
    html_table(header = TRUE)
  
  # what targets expressed in "Langerhans" ?
  antigens %>% 
    filter(grepl("Langerhans", `Cellular Expression`)) %>% 
    select(`Target Name`)
  
     Target Name
  1         CD1a
  2         CD1b
  3         CD1c
  4         CD1e
  5         CD14
  6         CD39
  7         CD50
  8         CD69
  9         CD74
  10        CD83
  11       CD101
  12       CD206
  13       CD207
  
  ```
  
  The data could be further processed by, for example, splitting the `Cellular Expression` column on either `and` or `,` (comma) to try and make one row per cell type.
  
  
  ---
  
  