Question: Correcting the batch effect in Edgeseq data
gravatar for jivarajivaraj
12 days ago by
jivarajivaraj0 wrote:


I have raw read counts of 57 patients ranked with Mandard score as TRG12 and TRG45.

Mandard score : A histological assessment of the response of oesophageal cancers to neoadjuvant treatment (cisplatin chemotherapy and radiotherapy in the original paper). The tumour regression grades were defined as follows: TRG 1 Complete regression - absence of residual cancer and fibrosis extending through the different layers of the oesophageal wall TRG 2 Presence of rare residual cancer TRG 3 An increase in the number of residual cancer cells, but predominantly fibrosis TRG 4 Residual cancer outgrowing fibrosis TRG 5 Absence of regressive changes

Some of these patients have recieved chemotrapy but some of patients recieved chemotrapy + radiotrapy so I considering this as batch but I am not sure if my design is right;

 ***`My question is: is there any difference TRG12 and TRG45 in terms of gene expression?`***

> head(mycols)
    condition batch
A10     TRG12     A
A11     TRG12     B
A7      TRG12     A
A8      TRG12     A
A9      TRG12     B
B10     TRG12     B
> <- DESeq(dds,betaPrior=FALSE, test="LRT",
                     full=~ batch + condition, reduced=~batch)
dpsc.res.LRT <- results(

> resultsNames(dpsc.res.LRT)

Please correct me if I am wrong

Thanks a lot

deseq2 edgeseq batch • 72 views
ADD COMMENTlink modified 12 days ago by Michael Love21k • written 12 days ago by jivarajivaraj0
Answer: Correcting the batch effect in Edgeseq data
gravatar for Michael Love
12 days ago by
Michael Love21k
United States
Michael Love21k wrote:

This design will test the condition effect while controlling for differences due to A vs B.

ADD COMMENTlink written 12 days ago by Michael Love21k

Sorry, I have obtained this

> resultsNames(dds)
[1] "Intercept"                   "cell_type_NOF_vs_CAF"        "co_cultured_YES_vs_NO"      
[4] "cell_typeNOF.co_culturedYES"


> mycols
   cell_type co_cultured
G2       NOF          NO
G3       NOF         YES
G4       CAF          NO
G5       CAF         YES
G6       CAF         YES

dds=DESeqDataSetFromMatrix(countData = NOFCAF,colData = mycols, design        =~ cell_type*co_cultured)

But I could not figure out how to extract differentially expressed genes behind "cell_type_NOF_vs_CAF"

I run like below

> res=results(dds)
> res
log2 fold change (MLE): cell culturedYES 
Wald test p-value: cell culturedYES 
DataFrame with 2545 rows and 6 columns

But nothing in differentially expressed genes

Could you please help me in extracting DEGs for cell_type_NOF_vs_CAF?

Thanks a lot

ADD REPLYlink modified 12 days ago • written 12 days ago by jivarajivaraj0

Take a look at the help functions and the vignette. This is answered by looking at ?results, how to extract different results from a dds object.

ADD REPLYlink written 11 days ago by Michael Love21k
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 253 users visited in the last hour