Chris, MakeVennDiagram does show both overlapping and non-overlapping peaks (overlapping is shared regions and non-overlapping is non-shared regions). Regarding using findOverlappingPeaks function to get non-overlapping peaks, here is an old post that address your questions. You could annotate the non-overlapping regions with annotatePeakInBatch. http://permalink.gmane.org/gmane.science.biology.informatics.conductor /32880 Best regards, Julie On 1/25/11 4:00 PM, "Christopher Ricupero" <ricupero at="" eden.rutgers.edu=""> wrote: > Julie, > > Is there a simple way to view both the overlapping and nonoverlapping peaks > of the makeVennDiagram function? > > I realize there is a findOverlappingPeaks, but I am also very interested in > finding both the overlapping peaks but also list the unique or > nonoverlappingpeaks for each sample. The ensemble gene names would be > perfect. > > Please let me know if this is an easy step > Thanks, Chris > > > > > > -----Original Message----- > From: Zhu, Lihua (Julie) [mailto:Julie.Zhu at umassmed.edu] > Sent: Monday, January 24, 2011 6:24 PM > To: ricupero at eden.rutgers.edu > Subject: Re: ChIPpeak Anno RangedData question > > Chris, > >> On 1/24/11 1:43 AM, "ricupero at eden.rutgers.edu" <ricupero at="" eden.rutgers.edu=""> >> wrote: >> >> Julie, >> >> Thank you for the help, I have created some nice code to get 2500 bp >> upstream and 500 downstream of each TSS. >> >> I have 2 more questions if you don't mind: >> >> 1. The function getEnrichedGo. After this runs, is it possible to dig in >> and actually see the geneIDs that were enriched for each Go term. This >> would be very interesting for discovery purposes to see how many and what >> genes for each class. > > It is a great idea! Someone else had the same question and I have been > thinking of this for a while now. I will try to add this to the future > release. >> >> 2. Have you automated any of your steps? I am having a tough time creating >> some automated loops becuase I have 12 samples to annotate ad check the go >> terms on. >> What I would like to automate is the annotatePeak, followed by the >> enriched Gofunctions. In fact, I have already manually run all 12 samples >> and have 12 annotatePeak objects. I would just need to loop them into the >> getEnrichedGO function > > Loop should work. Did you try while, for or lapply (help(lapply)) > >> >> Have you had any success batching your functions? >> > Not yet. If you would like to contribute the code, that would be great >> CHris >> >> Thanks, Chris > > Best regards, > > Julie > > >>> Chris, >>> >>> Please type helpis.na) in a R session to access the menu about is.na >>> function. Please use & for and, | for or inside the [ ] for combining >>> selection criteria. The online book at >>> http://cran.r-project.org/doc/manuals/R-intro.pdf is a very useful >>> resource, it helped me a lot when I started. >>> >>> FeatureIDs = annotatedPeak[!is.na(annotatedPeak$distancetoFeature) & >>> abs(annotatedPeak$distancetoFeature<5000000) & annotatedPeak$ >>> insideFeature %in% c("upstream" , "inside" , "overlapStart"),]$feature >>> >>> Best regards, >>> >>> Julie >>> >>> On 1/20/11 4:52 PM, "Christopher Ricupero" <ricupero at="" eden.rutgers.edu=""> >>> wrote: >>> >>> Hi Julie >>> >>> Thanks for the quick reply. Could I potentially bother you one more time. >>> If you could send me some info on how to manipulate the ranged data that >>> would be great. >>> >>> This function did work, but I wanted to take it 1 step further: To take >>> only (5000 upstream, inside, overlap) and limit to only (500 bp >>> downstream) >>> >>> My questions are: >>> 1. What is the [!is.na mean? >>> >>> 2. I would like to combine my arguments. Instead of taking just the >>> absolute value of 50000000 in distance to feature. I would like to take >>> 50000 distance to feature plus insideFeature : upstream, inside, >>> overlapStart, etc.. but then only 500 bp downsteam. I am sure there is a >>> way to do this, I am just unsure of the syntax and how to combine the >>> arguments. >>> >>> Could I say something like : >>> >>> abs(annotatedPeak$distancetoFeature<5000000) and annotatedPeak$ >>> insideFeature=?upstream?, ?inside?, ?overlapStart?)]? >>> >>> Thanks, Chris >>> >>> >>> >>> >>> >>> From: Zhu, Lihua (Julie) [mailto:Julie.Zhu at umassmed.edu] >>> Sent: Thursday, January 20, 2011 3:36 PM >>> To: Christopher Ricupero; Ou, Jianhong >>> Cc: bioconductor at stat.math.ethz.ch >>> Subject: Re: ChIPpeak Anno RangedData question >>> >>> Chris, >>> >>> Thanks for your kind comment! >>> >>> The following code snippets should do. >>> >>> FeatureIDs = annotatedPeak[!is.na(annotatedPeak$distancetoFeature) & >>> abs(annotatedPeak$distancetoFeature<5000000),]$feature >>> library(org.Hs.eg.db) >>> EnrichedGO = GetEnrichedGO(featureIDs, orgAnn="org.Hs.eg.db", maxP=0.01, >>> multiAdj=FALSE, minGOterm=10, multiAdjMethod="") >>> >>> Best regards, >>> >>> Julie >>> >>> On 1/20/11 3:13 PM, "Christopher Ricupero" <ricupero at="" eden.rutgers.edu=""> >>> wrote: >>> Hello Dr. Zhu, >>> >>> I have recently been introduced to your ChIPpeakAnno package and think it >>> is great. >>> >>> However, I am having some difficulties when working with the RangedData >>> dataset once it is convereted and then annotated. >>> >>> I followed your manuscript and exported the annotated Peak file into >>> excel, but I would like to do something different. >>> >>> What I am trying to accomplish is to filter the annotated peak Ranged > Data >>> by either the ?distancetoFeature? or ?shortestDistance? variables. I am >>> only interested in peaks that are very close to the promoter regions and >>> TSS so I wanted to limit these distances by approx 5000 kb. After this , >>> I would then run the GO analysis. >>> >>> Is this possible to select on this variable to get a subset of my >>> annotated peaks before I run the enrichedGo function? >>> >>> Thank you, >>> >>> Chris >>> >>> Christopher Ricupero >>> Graduate Fellow >>> Rutgers Univeristy >>> Piscataway, NJ 08854 >>> >>> >>> >>> >>> >>> >> >> > > > >