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how do I find the genes which are common to and those which differ between multiple lists of DE genes

I have multiple dataframes, each one represents a different experimental condition.

What I want is a list of:

a) the names of the genes which differ between the dataframes and b) the names of the genes which are common to the dataframes.

I know I can do this using the syntax listed here "http://www.cookbook-r.com/Manipulating_data/Comparing_data_frames/" but I am already using the VennDiagram package in R to illustrate visually how many genes are

a) common to and b) differ between dataframes.

It occurred to me that what I want is probably calculated by the VennDiagram package in order to be able to draw the venn diagram.

Does anybody know if it is and can it be extracted?

Thanks

package r venndiagram
• 7,758 views • link
updated by svlachavas svlachavas • written by peter.berry5 • peter.berry5

Hello peter.berry5!

We believe that this post does not fit the main topic of this site.

This is a pure R question that has nothing to do with bioinformatics. Please search/ask StackOverflow

For this reason we have closed your question. This allows us to keep the site focused on the topics that the community can help with.

If you disagree please tell us why in a reply below, we'll be happy to talk about it.

Cheers!

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written by Ram Ram

Hi I would suggest that the analysis of differences in protein and gene expression, which is what my dataset involves, is the very definition of bioinformatics. R is the tool I am using to achieve that.

Key to this is identifying the genes/proteins which differ between conditions which is what my question asked albeit with reference to a specific R package.

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written by peter.berry5 • peter.berry5

Perhaps the question could have made it clear there was a dataset and real life data behind the question and it wasn't just theoretical but I feel that's a moot point. Surely the point of the community is to help not censor.

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written by peter.berry5 • peter.berry5

Indeed - if the question had made it clear, it would not have been closed. It's not too late. Please edit the question and add some detail on how this is related to our domain, and I'll re-open the question and clean up these comments.

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written by Ram Ram

There are a ton of ways to do this in base R, but the dplyr sntax for this is a bit more simple. You can use dplyr::anti_join to return rows in one data.frame that don't have matches in a column in another data.frame. If you want to get rows in a data.frame that have matches to a column in another data.frame, you can use dplyr::semi_join.

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written by rpolicastro rpolicastro

@rpolicastro. Thanks for the reply. That's exactly what i ended up doing and worked perfectly. However, unless I am missing something anti join only allows me compare two dataframes. When I started analysing the full dataset I ended up comparing 4 dataframes to each other which I didn't anticipate and involved quite a bit of code and is messy.

Hence the question regarding the VennDiagram package as it must be performing these types of caomparisions this to draw the Venn diagram.

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written by peter.berry5 • peter.berry5

This is a visualization tool that comes after OP understands the basics of their own dataset. As such, I don't think this qualifies as an answer.

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written by Ram Ram

1 answer

You can simply use the set operation from the base package (also exist in BioGenerics and some other packages) .

see ?sets 

union(x, y)
intersect(x, y)
setdiff(x, y)

Provide your genes as vectors of gene names from your data.frames. So for genes that are different between x and y use setdiff(x,y) (genes in x but not in y) or setdiff (y,x), etc. For the common genes use intersect(x,y).

> x <- c('a','b','c')
> y <- c('b','d','c')
> setdiff (x,y)
[1] "a"
> setdiff (y,x)
[1] "d"
> union (y,x)
[1] "b" "d" "c" "a"
> intersect (y,x)
[1] "b" "c"
> setdiff(union(x,y), intersect(x,y))
[1] "a" "d"

For Venn diagrams also check out the vennr package (in addition to VennDiagram), that will do most of the work for you.

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written by Michael Dondrup Michael Dondrup

These are functions in base R and BioGenerics is unnecessary. If the lists are character vectors and the spelling is matched, they work beautifully. If the user has lists of mismatched IDs, gene-protein, or factor level, they won't work, and some conversion is needed specific to the situation. I think it's a good solution in the long run and may need further biinf work for the user to adapt to his or her needs.

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written by karl.stamm karl.stamm

I agree there can be some problems with curation and mismatched identifies, and I assumed the user has curated the input data properly. This process depends on the processing pipeline. Better to stick with unique ids like Ensembl gene or transcript IDs and not to mix different types of names and IDs, or transfer data back and forward from R to Excel sheets. Always good to raise awareness.

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written by Michael Dondrup Michael Dondrup

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