DESeq2 comparison two cell types and two genotypes
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Yosapol • 0
@40f341c9
Last seen 3.2 years ago
Netherlands

Dear All,

I have the samples of two cell types (Embryo and Ovule) with two genotypes (WT and MT), which indicate as condition and genotype below.

I would like to compare:

  1. EmbryoWT vs EmbryoMT,
  2. OvuleWT vs OvuleMT.
  3. EmbryoWT vs OvuleWT
  4. EmbryoMT vs OvuleMT.

conditions and genotypes

I used this model to compare my samples and try to look for solutions. However, It seems to be the analysis will include the same condition or genotype together which is not our interest.

dds <- DESeqDataSetFromMatrix(countData = clean_data, colData = meta, design = ~ genotype + condition + genotype:condition)

I highly appreciate your help.

Thanks in advance,

Enter the body of text here

Code should be placed in three backticks as shown below


# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

sessionInfo( )
GeneExpressionWorkflow DESeq2 GeneExpression • 3.5k views
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Has been cross-posted: https://www.biostars.org/p/9467526/

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@mikelove
Last seen 6 days ago
United States

This is answered in the vignette section on interactions.
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Dear Micheal,

Thank you very much for your suggestions. This is exactly what i am looking for.

### Create DESeq2Dataset object

dds <- DESeqDataSetFromMatrix(countData = clean_data, colData = meta, design = ~ celltypes + genotype)

dds$group <- factor(paste0(dds$celltypes, dds$genotype))

design(dds) <- ~ group


### Plot DESeq2
dds <- DESeq(dds)

### Extract information
# 1. EWT VS EMT
results(dds, name = "group_EmbryoWT_vs_EmbryoMT")

# 2. OWT VS OMT
results(dds, contrast=c("group", "OvuleWT", "OvuleMT"))

Could you please suggest how to find EmbryoWT vs OvuleWT and EmbryoMT vs OvuleMT? The helps from others also highly appreciate.

Thank you very much,

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Use contrast with those group names? I don't follow the question.

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I apologized for the unclear massage. Here is my group.

EmbryoMT EmbryoMT EmbryoMT EmbryoWT EmbryoWT EmbryoWT OvuleMT OvuleMT OvuleMT OvuleWT OvuleWT OvuleWT

Levels: EmbryoMT EmbryoWT OvuleMT OvuleWT

Would it be possible to extract information from All WT (EmbryoWT+OvuleWT) vs All MT (EmbryoMT+OvuleMT)

Thanks in advance

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You can use contrast with a list, e.g. list(c("A","B"), c("C","D")) and then listValues=c(1/2,-1/2)

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Is this different or better than using a design of ~ celltypes + genotype, and contrasting MT vs WT?

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It is different, hard to say which is better. Above is the average effect -- simply the arithmetic average of the two LFC. Your design assumes the effect is the same in both cell types. So the dispersion would be different in these two designs, for example.

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Hi Michael,

Thank you very much.

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Hi Michael,

I am trying the same I believe at the moment. I have diagnosis (no cancer, previous cancer, cancer) and site(arm, hand). And I want to to the following Differential Expression:

"No cancer" "arm" against "Previous cancer" + "cancer" "arm"

I managed to do the the groups as above, but still struggling to combine "previous cancer" + "cancer"

res <-results(dds, contrast=c("group", "nocancerarm", ("previouscancerarm","cancerarm"))

Can you help me? Thank you very much!

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Use the list style of contrast. Read over ?results

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Thanks I read it, but it is a bit hard to understand without an example.

I tried this but is giving me errors:

list(c("previouscancerarm","cancerarm")) listValues=c(1,-1)

And then

res<- results(dds, contrast=c("group", "nocancerarm", "list"))

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Did you know that man pages have examples? There are a bunch of them at the bottom of ?results.

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I cannot see an example for my specific case with a list of two and grouping? Thank you for any hint.

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There are many examples of this if you search the support site:

https://www.google.com/search?q=site%3Asupport.bioconductor.org+contrast+list

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One more question to this - in the vignette it uses this formula: ~genotype + condition + genotype:condition

Wouldnt I need to write the following in above sample: design = ~ celltypes + genotype + celltype:genotype ?

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Please adapt the code to your own experiment. It would seem that, yes, you need celltypes and genotype

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Ok thanks because above it says: dds <- DESeqDataSetFromMatrix(countData = clean_data, colData = meta, design = ~ celltypes + genotype), missing celltype:genotype

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