Question: Correcting the batch effect in Edgeseq data
0
gravatar for jivarajivaraj
9 months ago by
jivarajivaraj10 wrote:

Hi,

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
>


neu.dds.LRT <- DESeq(dds,betaPrior=FALSE, test="LRT",
                     full=~ batch + condition, reduced=~batch)
dpsc.res.LRT <- results(neu.dds.LRT)


> resultsNames(dpsc.res.LRT)
character(0)

Please correct me if I am wrong

Thanks a lot

deseq2 batch edgeseq • 296 views
ADD COMMENTlink modified 9 months ago by Michael Love25k • written 9 months ago by jivarajivaraj10
Answer: Correcting the batch effect in Edgeseq data
2
gravatar for Michael Love
9 months ago by
Michael Love25k
United States
Michael Love25k wrote:

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

ADD COMMENTlink written 9 months ago by Michael Love25k

Sorry, I have obtained this

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

By

> 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 typeNOF.co culturedYES 
Wald test p-value: cell typeNOF.co 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 9 months ago • written 9 months ago by jivarajivaraj10
1

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 9 months ago by Michael Love25k
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