Question: DESEQ2 normalize data of population data
0
gravatar for vrehaman
11 months ago by
vrehaman0
vrehaman0 wrote:

Dear Team,

I want to perform differential expression on counts generated from featureCounts. Each family has different disease. Can we perfrom differential expression across all samples? or since disease is different from family to family, should we perform separately family by family?

Example sample information is like below

SampleID condition Family
Sample1 Normal Fam1
Sample2 Diseased Fam1
Sample3 Normal Fam2
Sample4 Diseased Fam2
Sample5 Normal Fam3
Sample6 Diseased Fam3
Sample7 Normal Fam4
Sample8 Diseased Fam4
Sample9 Normal Fam5
Sample10 Diseased Fam5
 

I followed the below link for grouping condition and family and get multiple comparisons. Is it ok t proceed like this?

DESEq2 comparison with mulitple cell types under 2 conditions

dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata , design = ~ Family + condition)

dds$group <- factor(paste0(dds$Family, dds$condition))

design(dds) <- ~ group
dds <- DESeq(dds)
resultsNames(dds)

Could you please let me know your suggestions.

Thanks In Advance

Fazulur Rehaman

ADD COMMENTlink modified 11 months ago by Michael Love25k • written 11 months ago by vrehaman0
Answer: DESEQ2 normalize data of population data
1
gravatar for Michael Love
11 months ago by
Michael Love25k
United States
Michael Love25k wrote:

What kind of genes are you looking for? Genes commonly DE in diseased relative to normal?

You cannot perform DE within family here, because there are no "replicates" (I don't know if these are human donors, or what exactly a sample refers to...).

ADD COMMENTlink written 11 months ago by Michael Love25k

Dear Michael,

Thanks a lot for your quick response.

Please find below the details

What kind of genes are you looking for? Genes commonly DE in diseased relative to normal?

We are looking for more diabetic and obesity genes commonly DE in diseased relative to normal.

You cannot perform DE within family here, because there are no "replicates" (I don't know if these are human donors, or what exactly a sample refers to...).

yes, these are human donors having diabetic or obseity.  Each family has one control and one diseases sample (more than 6 families). Disease might be either diabetic or obesity. 

Please suggest me how can I proceed with DE.

Thanks In Advance

Fazulur Rehaman

ADD REPLYlink written 11 months ago by vrehaman0
1

You can use ~family + condition, which will control for family baseline, while finding genes where the diseased samples show DE relative to normal.

ADD REPLYlink written 11 months ago by Michael Love25k

Dear Michael,

Thanks a lot for your suggestions.

At first, I used the same model ~family + condition 

Here are the details:

which will control for family baseline, while finding genes where the diseased samples show DE relative to normal.

It means only one comparison where we can find genes diseased vs Normal, irrespective of which disease, right?

> resultsNames(dds)

[1] "Intercept"                    "family_Fam139_vs_Fam10"       

[3] "family_Fam193_vs_Fam10"        "family_Fam43_vs_Fam10"        

[5] "family_Fam52_vs_Fam10"         "family_Fam53_vs_Fam10"        

[7] "family_Fam55_vs_Fam10"         "family_Fam8_vs_Fam10"         

[9] "condition_Normal_vs_Diseased"

Building Results table for diseased vs Normal condition.

res1 <- results(dds, contrast=c("condition","Diseased","Normal"))

In resultsNames, it was mentioned as "condition_Normal_vs_Diseased". Since we have to get genes DE in diseased relative to normal, in results() function I have given "Diseased" followed by the "Normal". Please confirm if it is ok?

And also resultsNames() function giving other possible contrasts which are "family_Fam139_vs_Fam10", `"family_Fam43_vs_Fam10" etc. How can I use them.

Please let me know your suggestions.

Thanks In Advance

Fazulur Rehaman

ADD REPLYlink written 11 months ago by vrehaman0
1

Take a look at the DESeq2 vignette note on factor levels, where this is discussed.

But in short, what you have above is fine, you can always specify the LFC you want using contrast. The object res1 is your results table of interest.

You do not use the other coefficients listed in resultsNames, they are nuisance coefficients for your purposes. Those coefficients control for the family baselines.

ADD REPLYlink written 11 months ago by Michael Love25k

Dear Michael,

Thanks a lot for your confirmation and suggestions on factor levels.

Thanks & Regards

Fazulur Rehaman

ADD REPLYlink written 11 months ago by vrehaman0

Dear Michael,

I have one more question. Since, we have only one comparsion which is diseased relative to Normal, Is there any possibility, I might know the family belongs to upregulated or down regulated genes. 

Please let me know.

Thanks & Regards

Fazulur Rehaman

ADD REPLYlink written 11 months ago by vrehaman0

You can look at plotCounts()

ADD REPLYlink written 11 months ago by Michael Love25k
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