Update on mike's tip on plotMA, it worked ! i used ;
DESeq:: plotMA(res)
or DESeq2:: plotMA(your requirements)
I have another question. I am working with 4 animals as my samples with absolutely NO replicates !!! It is downright expensive to have replicate conditions for the animals and also during the process of library preparation , replicates were not made due to cost of running NGS on all of them and so on....but anyways, right now what I have is what I have My problem is this: I have used DESeq2 to perform the DE analysis as indicated in part one below and DESeq to perform DE analysis as indicated in part 2. For DESeq I am using per sample across its two conditions for evaluation.
For my first part I have used all four samples (treated as replicates) for both untreated and treated condition> basically I have sample1,2,3,4 fed across both untreated and treated into DESeq2. I generated heatmaps of highly expressed genes. However I have only 3 genes differentially expressed with a Padj value of less than 0.05.
Part 2: I am using DESeq per sample using its own untreated vs treated. To indicate I have no replicates I have used
cds<-estimateDispersions(cds,method="blind",sharingMode="fit-only",fitType="local"). I get a result fo rbinom test with a lot of NA !!!!!downstream
however when I do a :
> cdsBlind = estimateDispersions( cds, method="blind" )
> vsd = varianceStabilizingTransformation( cdsBlind )
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘normalizationFactors’ for signature ‘"CountDataSet"’
I get the above error...........why is that? I had normalized my counts and used that to perform this step . However I still get this error: plz see below;
> a<-counts(cds,normalized=T)
cds3<-estimateDispersions(a,method="blind",sharingMode="fit-only",fitType="local")
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘estimateDispersions’ for signature ‘"matrix"’
> cdsBlind = estimateDispersions( a, method="blind" )
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘estimateDispersions’ for signature ‘"matrix"’
Please could u suggest how I can get to get a top highly expressed gene list per sample WITHOUT REPLICATES in DESeq ? Why am I getting this error? I poked a little bit into this googling and I am confused. My "a" is normalized counts.
Also can I compare fold2change values obtained from results of nbinom using DESeq to the fold2change I get from results dseq<-DESeq(dds) in DESeq2?
I appreciate any input for such a condition where one is working without any replicates !!! Thanks very much in advance !
DESeq:: plotMA(res)
or DESeq2:: plotMA(your requirements)
I have another question. I am working with 4 animals as my samples with absolutely NO replicates !!! It is downright expensive to have replicate conditions for the animals and also during the process of library preparation , replicates were not made due to cost of running NGS on all of them and so on....but anyways, right now what I have is what I have My problem is this: I have used DESeq2 to perform the DE analysis as indicated in part one below and DESeq to perform DE analysis as indicated in part 2. For DESeq I am using per sample across its two conditions for evaluation.
For my first part I have used all four samples (treated as replicates) for both untreated and treated condition> basically I have sample1,2,3,4 fed across both untreated and treated into DESeq2. I generated heatmaps of highly expressed genes. However I have only 3 genes differentially expressed with a Padj value of less than 0.05.
Part 2: I am using DESeq per sample using its own untreated vs treated. To indicate I have no replicates I have used
cds<-estimateDispersions(cds,method="blind",sharingMode="fit-only",fitType="local"). I get a result fo rbinom test with a lot of NA !!!!!downstream
however when I do a :
> cdsBlind = estimateDispersions( cds, method="blind" )
> vsd = varianceStabilizingTransformation( cdsBlind )
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘normalizationFactors’ for signature ‘"CountDataSet"’
I get the above error...........why is that? I had normalized my counts and used that to perform this step . However I still get this error: plz see below;
> a<-counts(cds,normalized=T)
cds3<-estimateDispersions(a,method="blind",sharingMode="fit-only",fitType="local")
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘estimateDispersions’ for signature ‘"matrix"’
> cdsBlind = estimateDispersions( a, method="blind" )
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘estimateDispersions’ for signature ‘"matrix"’
Please could u suggest how I can get to get a top highly expressed gene list per sample WITHOUT REPLICATES in DESeq ? Why am I getting this error? I poked a little bit into this googling and I am confused. My "a" is normalized counts.
Also can I compare fold2change values obtained from results of nbinom using DESeq to the fold2change I get from results dseq<-DESeq(dds) in DESeq2?
I appreciate any input for such a condition where one is working without any replicates !!! Thanks very much in advance !
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