[BioC] edgeR estimateGLMCommonDisp Error on RNA analysis
Gordon K Smyth
smyth at wehi.EDU.AU
Wed Mar 26 00:31:45 CET 2014
Dear Justin,
Not sure why you ask this question --- the correct matrix to use is always
the one containing correct information.
Matrix1 incorrectly identifies two different individuals with Disease2 as
the same individual, whereas Matrix2 gives correct information.
Best wishes
Gordon
On Tue, 25 Mar 2014, Justin Jeyakani (GIS) wrote:
> Dear Gordon,
>
> Thanks you very much for your support. I got my results now.
> I have one more query about my another matrix... plz see the below is the matrix...
> which one of the matrix is right to use? I have 4 Healthy individuals (each individual have 2 stages npc and ne) and Disease1 one individual, Disease2 two individual and Disease3 one individual.
>
> Matrix1:
> Disease Patient Treatment
> 1 Healthy H1 npc
> 2 Healthy H1 ne
> 3 Healthy H2 npc
> 4 Healthy H2 ne
> 5 Healthy H3 npc
> 6 Healthy H3 ne
> 7 Healthy H4 npc
> 8 Healthy H4 ne
> 9 Disease1 D5 npc
> 10 Disease1 D5 ne
> 11 Disease2 D6 npc
> 12 Disease2 D6 ne
> 13 Disease2 D6 npc
> 14 Disease2 D6 ne
> 15 Disease3 D7 npc
> 16 Disease3 D7 ne
>
> OR
>
> Matrix2:
> Disease Patient Treatment
> 1 Healthy H1 npc
> 2 Healthy H1 ne
> 3 Healthy H2 npc
> 4 Healthy H2 ne
> 5 Healthy H3 npc
> 6 Healthy H3 ne
> 7 Healthy H4 npc
> 8 Healthy H4 ne
> 9 Disease1 D5 npc
> 10 Disease1 D5 ne
> 11 Disease2 D6 npc
> 12 Disease2 D6 ne
> 13 Disease2 D7 npc
> 14 Disease2 D7 ne
> 15 Disease3 D8 npc
> 16 Disease3 D8 ne
>
> Thank you very much.
>
> Justin
>
> -----Original Message-----
> From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
> Sent: Tuesday, March 25, 2014 9:34 AM
> To: Justin Jeyakani (GIS)
> Cc: Bioconductor mailing list
> Subject: RE: edgeR estimateGLMCommonDisp Error on RNA analysis
>
> I assumed that "Healthy" is set as the first level of the factor 'Disease'. I assumed that because that is how you set it up in the original code you posted.
>
> You can make this so by typing
>
> samples$Disease <- relevel(samples$Disease, ref="Healthy")
>
> The coefficient "Treatmentnpc" gives the effect of npc in healthy patients.
>
> Gordon
>
> On Mon, 24 Mar 2014, Justin Jeyakani (GIS) wrote:
>
>> Dear Gordon,
>>
>> Thanks for the code and the clarrification. I'm new to the edgeR your reply helps a lot. I have an issue to execute your code.
>>
>> I'm getting slightly different matrix compare to what you have sent
>> using the same code...
>
>> I'm getting the "DiseaseHealthy:Treatmentnpc" instead what you get
>> "DiseaseDisease1:Treatmentnpc" also can I know that coefficients of
>> "Treatmentnpc" is genes responding to the npc in healthy patients only?
>> Which one gives the genes respond to the npc in healthy?
>>
>>> samples <-read.table("matrix1.txt",header=TRUE)
>>> samples
>> Disease Patient Treatment
>> 1 Healthy H1 npc
>> 2 Healthy H1 ne
>> 3 Healthy H2 npc
>> 4 Healthy H2 ne
>> 5 Healthy H3 npc
>> 6 Healthy H3 ne
>> 7 Disease1 D4 npc
>> 8 Disease1 D4 ne
>> 9 Disease2 D5 npc
>> 10 Disease2 D5 ne
>> 11 Disease3 D6 npc
>> 12 Disease3 D6 ne
>>> design1 <- model.matrix(~Patient,data=samples)
>>> design2 <- model.matrix(~Disease*Treatment,data=samples)
>>> design <- cbind(design1,design2[,5:8])
>>> colnames(design)
>> [1] "(Intercept)" "PatientD5"
>> [3] "PatientD6" "PatientH1"
>> [5] "PatientH2" "PatientH3"
>> [7] "Treatmentnpc" "DiseaseDisease2:Treatmentnpc"
>> [9] "DiseaseDisease3:Treatmentnpc" "DiseaseHealthy:Treatmentnpc"
>>
>> Thank you very much.
>>
>> Justin
>>
>> -----Original Message-----
>> From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
>> Sent: Monday, March 24, 2014 12:00 PM
>> To: Justin Jeyakani (GIS)
>> Cc: Bioconductor mailing list
>> Subject: edgeR estimateGLMCommonDisp Error on RNA analysis
>>
>> Dear Justin,
>>
>> You are creating design matrices that have more columns than you have samples. So it's not surprising that edgeR tells you there are no residual df available to estimate the dispersion.
>>
>> You want to test the treatment effect for each individual diseased patient vs the average of the treatment effect for healthy patients. Making design matrices for hypotheses like this isn't automatic in R. Here is one way to do it:
>>
>> > samples
>> Disease Patient Treatment
>> 1 Healthy H1 npc
>> 2 Healthy H1 ne
>> 3 Healthy H2 npc
>> 4 Healthy H2 ne
>> 5 Healthy H3 npc
>> 6 Healthy H3 ne
>> 7 Disease1 D4 npc
>> 8 Disease1 D4 ne
>> 9 Disease2 D5 npc
>> 10 Disease2 D5 ne
>> 11 Disease3 D6 npc
>> 12 Disease3 D6 ne
>> > design1 <- model.matrix(~Patient,data=samples)
>> > design2 <- model.matrix(~Disease*Treatment,data=samples)
>> > design <- cbind(design1,design2[,5:8]) > colnames(design)
>> [1] "(Intercept)" "PatientD5"
>> [3] "PatientD6" "PatientH1"
>> [5] "PatientH2" "PatientH3"
>> [7] "Treatmentnpc" "DiseaseDisease1:Treatmentnpc"
>> [9] "DiseaseDisease2:Treatmentnpc" "DiseaseDisease3:Treatmentnpc"
>>
>> Then you can test for coefficients 8, 9 and 10.
>>
>> Best wishes
>> Gordon
>>
>>
>>> Date: Fri, 21 Mar 2014 17:09:37 +0800
>>> From: "Justin Jeyakani (GIS)" <gnanakkan at gis.a-star.edu.sg>
>>> To: "bioconductor at r-project.org" <bioconductor at r-project.org>
>>> Subject: [BioC] edgeR estimateGLMCommonDisp Error on RNA analysis
>>>
>>> Hello Sir,
>>>
>>> I'm doing differential gene exp. by the edgeR package. My data seems
>>> to suitable to analyse by glm method (As per edgeR userguide section
>>> 3.5 Comparisons Both Between and Within Subjects).
>>>
>>> I have two different matrix to design.
>>> Matrix One:
>>
>>> I have 12 dataset 3 samples from different healthy individuals
>>> (Healthy) of two different stages (npc,ne) and 3 samples from
>>> different individuals (Disease1) of two different stages (npc,ne), so
>>> 6 data from each healthy(6) and Disese1 (6) below is my code and
>>> works well. But for my another design matrix it's not!
>>>
>>> Matrix Two:
>>
>>> But I want to comapre the All the Healthy (6data from 3 indiauals)
>>> with each of the Disease1 individual (npc,ne). B'ze I'm getting 125
>>> diff.exp genes btn healthyVsDisease1 and I'm expecting more, If I do
>>> individual test of 3 disease1 with Healthy but the I'm getting error
>>> in the "estimateGLMCommonDisp" steps. I couldn't solve the problem. I
>>> have the same problem with my another matrix also. Can guide me to
>>> solve this issue highly appreciated ... below is my code for the
>>> above mentiond two matrix...do I need to define anthing in the generate factor Level "gl"
>>> further? Since Healthy has 3 sets and Disease 1, 2, 3 has one set
>>> causing dispersion error.
>>>
>>> Matrix1.txt: my input (works fine)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Disease1 4 npc
>>> 8 Disease1 4 ne
>>> 9 Disease1 5 npc
>>> 10 Disease1 5 ne
>>> 11 Disease1 6 npc
>>> 12 Disease1 6 ne
>>>
>>> SUPT1<-read.table("matrix1.txt",header=TRUE)
>>> summary(SUPT1)
>>> Patient<-gl(3,2,length=12)
>>> Disease <- factor(SUPT1$Disease, levels=c("Healthy","Disease1"))
>>> Treatment <- factor(SUPT1$Treatment, levels=c("npc","ne"))
>>>
>>> data.frame(Disease,Patient,Treatment)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Disease1 1 npc
>>> 8 Disease1 1 ne
>>> 9 Disease1 2 npc
>>> 10 Disease1 2 ne
>>> 11 Disease1 3 npc
>>> 12 Disease1 3 ne
>>>
>>> design <- model.matrix(~Disease+Disease:Patient+Disease:Treatment)
>>> colnames(design)
>>> [1] "(Intercept)" "DiseaseDisease1"
>>> [3] "DiseaseHealthy:Patient2" "DiseaseDisease1:Patient2"
>>> [5] "DiseaseHealthy:Patient3" "DiseaseDisease1:Patient3"
>>> [7] "DiseaseHealthy:Treatmentne" "DiseaseDisease1:Treatmentne"
>>>
>>> library(edgeR)
>>> count<-read.table("RHN035-46_s1.txt",header=TRUE)
>>> head (count)
>>> colnames(count)
>>> samplename=colnames(count)
>>> cds01<-DGEList(count,group=samplename)
>>> head(cds01)
>>> cds01
>>> summary(cds01)
>>> dim(cds01)
>>>
>>>
>>> keep<-rowSums(cpm(cds01)>2)>=4
>>> cds01<-cds01[keep,]
>>> dim(cds01)
>>>
>>> cds01$sample$lib.size<-colSums(cds01$counts)
>>> y <- estimateGLMCommonDisp(cds01,design)
>>> y <- estimateGLMTrendedDisp(y,design) y <-
>>> estimateGLMTagwiseDisp(y,design) fit <- glmFit(y, design)
>>>
>>> lrt <- glmLRT(fit, coef="DiseaseHealthy:Treatmentne")
>>> topTags(lrt)
>>> detags <- rownames(topTags(lrt)$table) cpm(y)[detags,
>>> order(y$samples$group)] summary(de <- decideTestsDGE(lrt))
>>>
>>> lrt <- glmLRT(fit, coef="DiseaseDisease1:Treatmentne")
>>> topTags(lrt)
>>> detags <- rownames(topTags(lrt)$table) cpm(y)[detags,
>>> order(y$samples$group)] summary(de <- decideTestsDGE(lrt))
>>>
>>> lrt <- glmLRT(fit, contrast=c(0,0,0,0,0,0,-1,1))
>>> topTags(lrt)
>>> detags <- rownames(topTags(lrt)$table) cpm(y)[detags,
>>> order(y$samples$group)] summary(de <- decideTestsDGE(lrt))
>>> Matrix2.txt:my input (gives Error) ( I have split the Disease1 into 3 Disease sets and need to compare Healthy Vs Disease1/Disease2/Disese3 and genes respond to "ne" in Disease1/2/3)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Disease1 4 npc
>>> 8 Disease1 4 ne
>>> 9 Disease2 5 npc
>>> 10 Disease2 5 ne
>>> 11 Disease3 6 npc
>>> 12 Disease3 6 ne
>>>
>>> SUPT1<-read.table("matrix2.txt",header=TRUE)
>>> summary(SUPT1)
>>> Patient<-gl(3,2,length=12)
>>> Disease <- factor(SUPT1$Disease,
>>> levels=c("Healthy","Disease1","Disease2","Disease3"))
>>> Treatment <- factor(SUPT1$Treatment, levels=c("npc","ne"))
>>>
>>> data.frame(Disease,Patient,Treatment)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Disease1 1 npc
>>> 8 Disease1 1 ne
>>> 9 Disease2 2 npc
>>> 10 Disease2 2 ne
>>> 11 Disease3 3 npc
>>> 12 Disease3 3 ne
>>>
>>> design <- model.matrix(~Disease+Disease:Patient+Disease:Treatment)
>>> colnames(design)
>>> [1] "(Intercept)" "DiseaseDisease1"
>>> [3] "DiseaseDisease2" "DiseaseDisease3"
>>> [5] "DiseaseHealthy:Patient2" "DiseaseDisease1:Patient2"
>>> [7] "DiseaseDisease2:Patient2" "DiseaseDisease3:Patient2"
>>> [9] "DiseaseHealthy:Patient3" "DiseaseDisease1:Patient3"
>>> [11] "DiseaseDisease2:Patient3" "DiseaseDisease3:Patient3"
>>> [13] "DiseaseHealthy:Treatmentne" "DiseaseDisease1:Treatmentne"
>>> [15] "DiseaseDisease2:Treatmentne" "DiseaseDisease3:Treatmentne"
>>>
>>> library(edgeR)
>>> count<-read.table("RHN035-46_s1.txt",header=TRUE)
>>> head (count)
>>> colnames(count)
>>> samplename=colnames(count)
>>> cds01<-DGEList(count,group=samplename)
>>> head(cds01)
>>> cds01
>>> summary(cds01)
>>> dim(cds01)
>>>
>>>
>>> keep<-rowSums(cpm(cds01)>2)>=4
>>> cds01<-cds01[keep,]
>>> dim(cds01)
>>>
>>> cds01$sample$lib.size<-colSums(cds01$counts)
>>>
>>> y <- estimateGLMCommonDisp(cds01,design)
>>> Warning message:
>>> In estimateGLMCommonDisp.default(y = y$counts, design = design, :
>>> No residual df: setting dispersion to N
>>> Matri3.txt- my input (gives Error)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Healthy 4 npc
>>> 8 Healthy 4 ne
>>> 9 Disease1 5 npc
>>> 10 Disease1 5 ne
>>> 11 Disease2 6 npc
>>> 12 Disease2 6 ne
>>> 13 Disease2 7 npc
>>> 14 Disease2 7 ne
>>> 15 Disease3 8 npc
>>> 16 Disease3 8 ne
>>>
>>> SUPT1<-read.table("matrix2.txt",header=TRUE)
>>> summary(SUPT1)
>>> Patient<-gl(4,2,length=16)
>>> Disease <- factor(SUPT1$Disease,
>>> levels=c("Healthy","Disease1","Disease2","Disease3"))
>>> Treatment <- factor(SUPT1$Treatment, levels=c("npc","ne"))
>>>
>>> data.frame(Disease,Patient,Treatment)
>>> Disease Patient Treatment
>>> 1 Healthy 1 npc
>>> 2 Healthy 1 ne
>>> 3 Healthy 2 npc
>>> 4 Healthy 2 ne
>>> 5 Healthy 3 npc
>>> 6 Healthy 3 ne
>>> 7 Healthy 4 npc
>>> 8 Healthy 4 ne
>>> 9 Disease1 1 npc
>>> 10 Disease1 1 ne
>>> 11 Disease2 2 npc
>>> 12 Disease2 2 ne
>>> 13 Disease2 3 npc
>>> 14 Disease2 3 ne
>>> 15 Disease3 4 npc
>>> 16 Disease3 4 ne
>>>
>>> design <- model.matrix(~Disease+Disease:Patient+Disease:Treatment)
>>> colnames(design)
>>> [1] "(Intercept)" "DiseaseDisease1"
>>> [3] "DiseaseDisease2" "DiseaseDisease3"
>>> [5] "DiseaseHealthy:Patient2" "DiseaseDisease1:Patient2"
>>> [7] "DiseaseDisease2:Patient2" "DiseaseDisease3:Patient2"
>>> [9] "DiseaseHealthy:Patient3" "DiseaseDisease1:Patient3"
>>> [11] "DiseaseDisease2:Patient3" "DiseaseDisease3:Patient3"
>>> [13] "DiseaseHealthy:Patient4" "DiseaseDisease1:Patient4"
>>> [15] "DiseaseDisease2:Patient4" "DiseaseDisease3:Patient4"
>>> [17] "DiseaseHealthy:Treatmentne" "DiseaseDisease1:Treatmentne"
>>> [19] "DiseaseDisease2:Treatmentne" "DiseaseDisease3:Treatmentne"
>>>
>>> library(edgeR)
>>> count<-read.table("RHN035-46-47-53_51-57_s1-s2gtf_tophatuniq.count.tx
>>> t
>>> ",header=TRUE)
>>> head (count)
>>> colnames(count)
>>> samplename=colnames(count)
>>> cds01<-DGEList(count,group=samplename)
>>> head(cds01)
>>> cds01
>>> summary(cds01)
>>> dim(cds01)
>>>
>>> keep<-rowSums(cpm(cds01)>1)>=4
>>> cds01<-cds01[keep,]
>>> dim(cds01)
>>>
>>> cds01$sample$lib.size<-colSums(cds01$counts)
>>> y <- estimateGLMCommonDisp(cds01,design)
>>> Error in return(NA, ntags) : multi-argument returns are not permitted
>>> In addition: Warning message:
>>> In estimateGLMTrendedDisp.default(y = y$counts, design = design, :
>>> No residual df: cannot estimate dispersion
>>>
>>> Thanks and regards,
>>> Justin
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