On 01/29/12 07:49, Tina Asante Boahene wrote: > Hi all, > > I am still having problems with bayseq > > Having followed the pdf document associated with it and also tailoring it to the Marioni et al data I am using, it seems that the code has been running for over two days without any results. > > I am wondering this code be down to my code. > > I have therefore attached my code to this email hoping that someone can help me solve this problem, thank you. > > > library(baySeq) > library(edgeR) > library(limma) > library(snow) > > cl<- makeCluster(4, "SOCK") > > > ##Calculating normalization factors## > D=MA.subsetA$M > head(D) > names(D) > dim(D) Please provide the output of these (i.e., head, names, dim). > > g<- gsub("R[1-2]L[1-8]", "", colnames(D)) > d<- DGEList(counts = D, group = substr(colnames(D), 5, 30)) > d$samples > names(d) > dim(d) These values would be helpful too. > > > CD<- new("countData", data = as.matrix(MA.subsetA$M), libsizes = > as.integer(d$samples$lib.size), replicates = g) > groups(CD)<- list(rep(1, ncol(CD)), g) > > CD at libsizes<- getLibsizes(CD) > > plotMA.CD(CD, samplesA = c(1,3,6,8,10), samplesB = c(2,4,5,7,9), col = c(rep("red", > 100), rep("black", 900))) Did this work? Your original question was about plotMA.CD not recognizing your groups. Does the plot work for you now? > > ## Optionally adding annotation details to the @annotation slot of the countData object. ## > CD at annotation<- data.frame(name = paste("gene", 1:1000, sep = "_")) > > > > > ### Poisson-Gamma Approach ### > > CDP.Poi<- getPriors.Pois(CD, samplesize = 2, takemean = TRUE, cl = cl) > > CDP.Poi at priors ## This takes time ### Is the call to getPriors.Pois() that has been running for over 2 days? If not, please specify which function call is taking so long. Did the rest of the code below work for you? Valerie > > CDPost.Poi<- getLikelihoods.Pois(CDP.Poi, pET = "BIC", cl = cl) > CDPost.Poi at estProps > > CDPost.Poi at posteriors[1:10, ] ## A list of the posterior likelihoods each model for the first 10 genes ## > CDPost.Poi at posteriors[101:110, ] ## A list of the posterior likelihoods each model for the genes from 101 to 110 ## > > > ### Negative-Binomial Approach ### > > CDP.NBML<- getPriors.NB(CD, samplesize = 1000, estimation = "QL", cl = cl) > > CDPost.NBML<- getLikelihoods.NB(CDP.NBML, pET = 'BIC', cl = cl) > > CDPost.NBML at estProps > > CDPost.NBML at posteriors[1:10,] > > CDPost.NBML at posteriors[101:110,] > > > Kind Regards > > Tina > ________________________________________ > From: Valerie Obenchain [vobencha at fhcrc.org] > Sent: 25 January 2012 18:14 > To: Tina Asante Boahene > Cc: bioconductor at stat.math.ethz.ch > Subject: Re: [BioC] Help With RNA-seq > > Hi Tina, > > It's difficult to help without knowing what your data look like or what > error message you are seeing. Both pieces of information would be helpful. > > For starters I think you need to provide 'replicate' and 'groups' > arguments when you create your new "countData" object. Depending on what > order your data are in you need something like, > > groups<- list(NDE = c(1,1,1,1,1,1,1,1,1,1), DE = > c(1,2,1,2,2,1,2,1,2,1)) > replicates<- c("Kidney", "Liver", "Kidney", "Liver", "Liver', > "Kidney", "Liver", "Kidney", "Liver", "Kidney") > > Then create your "countData" with these variables, > > CD<- new("countData", data = as.matrix(MA.subsetA$M), libsizes = > as.integer(d$samples$lib.size), > replicates = replicates, groups = groups) > > Now look at the CD object and make sure the columns are labeled as they > should be and the other slot values make sense. The MA plot call would > look something like, > > plotMA.CD(CD, samplesA = "Kidney", samplesB = "Liver") > > The author used the red and black colors for the vignette plot because > there was a known structure to the data; the first 100 counts showed > differential expression and the last 900 did not. You probably have a > different situation in your data so using the same color scheme may not > make sense. > > Valerie > > > On 01/23/2012 06:13 AM, Tina Asante Boahene wrote: >> Hi all, >> >> I am conducting some analysis using the Marioni et al data. >> >> However, I am having a bit of trouble using my data to conduct the analysis based on the baySeq package. >> >> And I was wondering if you could stir me in the right direction. >> >> I have already used edgeR to find the library sizes for the ten libraries I have for my data as well as for the groups (Liver and Kidney) as stated below. >> >> >> library(baySeq) >> library(edgeR) >> library(limma) >> library(snow) >> >> cl<- makeCluster(4, "SOCK") >> >> >> ##Calculating normalization factors## >> D=MA.subsetA$M >> head(D) >> names(D) >> dim(D) >> >> g<- gsub("R[1-2]L[1-8]", "", colnames(D)) >> d<- DGEList(counts = D, group = substr(colnames(D), 5, 30)) >> d$samples >> names(d) >> dim(d) >> >> >> I will like to know how to model my code in order to produce the MA plot for count data >> >> >> This is what I have, however it runs with the wrong response. >> >> Can someone help me fix this please. >> >> CD<- new("countData", data = as.matrix(MA.subsetA$M), libsizes = as.integer(d$samples$lib.size)) >> >> plotMA.CD(CD, samplesA = 1:5, samplesB = 6:10, col = c(rep("red", >> 100), rep("black", 900))) >> >> >> How can I get it to recognise the "groups" as "g" (Library and Kidney) >> >> This is the output for the groups [1] "Kidney" "Liver" "Kidney" "Liver" "Liver" "Kidney" "Liver" "Kidney" "Liver" "Kidney" >> >> thank you. >> >> >> >> >> >> >> Kind Regards >> >> Tina >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor