BioNet - interpretation of node score (Eva Benito Garagorri)
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Dear Eva, The likelihood ratio score used in BioNet is a monotone transformation of the p-values. In your case the node scores measure the significance of differential expression of the genes. Thus the higher the node score the more significant the gene and vice versa. Negative scores denote nodes that are regarded as not significant given a certain FDR threshold. If the FDR threshold is changed positive scoring nodes can of course become negative. Analogously, an edge between negative scoring nodes represents not differentially expressed interacting genes. If you have further question please feel free to contact us. Best wishes, Marcus and Daniela -----Urspr?ngliche Nachricht----- Von: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] Im Auftrag von bioconductor-request at stat.math.ethz.ch Gesendet: Donnerstag, 16. September 2010 12:00 An: bioconductor at stat.math.ethz.ch Betreff: Bioconductor Digest, Vol 91, Issue 15 Send Bioconductor mailing list submissions to bioconductor at stat.math.ethz.ch To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/bioconductor or, via email, send a message with subject or body 'help' to bioconductor-request at stat.math.ethz.ch You can reach the person managing the list at bioconductor-owner at stat.math.ethz.ch When replying, please edit your Subject line so it is more specific than "Re: Contents of Bioconductor digest..." Today's Topics: 1. Re: Gating with an ellipsoidGate (Roger Leigh) 2. Re: Gating with an ellipsoidGate (Roger Leigh) 3. Re: probeAnno for tilingArray package. (Wolfgang Huber) 4. Comparing 2 GEO datasets (Yogesh) 5. Oligo package crlmm method (Zhu, Julie) 6. Re: Oligo package crlmm method (Benilton Carvalho) 7. Re: Oligo package crlmm method (Zhu, Julie) 8. Re: Comparing 2 GEO datasets (Sean Davis) 9. Re: Comparing 2 GEO datasets (Yogesh) 10. Re: Oligo package crlmm method (Zhu, Julie) 11. Re: Agi4x44PreProcess- RSQLite driver: (RS_SQLite_fetch: failed first step: disk I/O error) (Brown, Joseph) 12. Re: Comparing 2 GEO datasets (Sean Davis) 13. Re: Comparing 2 GEO datasets (Yogesh) 14. Re: probeAnno for tilingArray package. (sohkhan at notes.cc.sunysb.edu) 15. Querying clustering in heatmap.2 (Elizabeth Ashley) 16. BioNet - interpretation of node score (Eva Benito Garagorri) 17. Re: Gating with an ellipsoidGate (Josef Spidlen) 18. e-LICO multi-omics prediction challenge with background knowledge on Obstructive Nephropathy (Adam Woznica) 19. Re: Querying clustering in heatmap.2 (Steve Lianoglou) ---------------------------------------------------------------------- Message: 1 Date: Wed, 15 Sep 2010 12:24:06 +0100 From: Roger Leigh <rleigh@codelibre.net> To: Josef Spidlen <jspidlen at="" bccrc.ca=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Gating with an ellipsoidGate Message-ID: <4C90ACD6.4090600 at codelibre.net> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Dear Josef, Many thanks, the speadsheet was most helpful! I did try creating a covariance matrix from separate scaling and rotation matrices (below), but this didn't work, I suppose because they are too simple--operating in Euclidean space rather than Mahalanobis space. cov.matrix.simple <- function (a, b, angle) { theta <- angle * (pi/180) R <- matrix(c(cos(theta), -sin(theta), sin(theta), cos(theta)), byrow=TRUE, ncol=2) S <- matrix(c(a, 0, 0, b), byrow=TRUE, ncol=2) S%*%R } Using your spreadsheet, I've created a macro to create a covariance matrix in R from a, b and an angle, which I've verified to match the results shown in your spreadsheet: cov.matrix <- function (a, b, angle) { theta <- angle * (pi/180) c1 <- ((cos(theta)^2)/a^2) + ((sin(theta)^2)/b^2) c2 <- sin(theta) * cos(theta) * ((1/a^2) - (1/b^2)) c3 <- ((sin(theta)^2)/a^2) + ((cos(theta)^2)/b^2) m1 <- matrix(c(c1, c2, c2, c3), byrow=TRUE, ncol=2) m2 <- solve(m1) m2 } This appears to work well when rotation is not involved. However, as soon as I try to add rotation, it blows up: library(flowCore) library(flowViz) library(flowUtils) cov.matrix <- function (a, b, angle) { theta <- angle * (pi/180) c1 <- ((cos(theta)^2)/a^2) + ((sin(theta)^2)/b^2) c2 <- sin(theta) * cos(theta) * ((1/a^2) - (1/b^2)) c3 <- ((sin(theta)^2)/a^2) + ((cos(theta)^2)/b^2) m1 <- matrix(c(c1, c2, c2, c3), byrow=TRUE, ncol=2) m2 <- solve(m1) m2 } d <- read.FCS("0isotype.fcs", alter.names=TRUE) d <- transform(d, `SS.Log` = log10(`SS.Log`)) # No rotation cov <- cov.matrix(20000, 0.4, 0) colnames(cov) <- c("FS.Lin", "SS.Log") rownames(cov) <- c("FS.Lin", "SS.Log") mean <- c("FS.Lin"=40000, "SS.Log"=2.8) cells <- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) print(cov) pdf("test.pdf", width=8, height=8, pointsize=12) print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", ylab=expression(log[10]~(SS)))) dev.off() # Now repeat with a 20 degree rotation... cov <- cov.matrix(20000, 0.4, 20) colnames(cov) <- c("FS.Lin", "SS.Log") rownames(cov) <- c("FS.Lin", "SS.Log") mean <- c("FS.Lin"=40000, "SS.Log"=2.8) cells <- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) print(cov) pdf("test-rotate.pdf", width=8, height=8, pointsize=12) print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", ylab=expression(log[10]~(SS)))) dev.off() I've put the source data, script and results at: http://www-users.york.ac.uk/~rl522/flowcore-test/ http://www-users.york.ac.uk/~rl522/flowcore-test/test.pdf http://www-users.york.ac.uk/~rl522/flowcore-test/test-rotate.pdf You can see that while an ellipse is drawn correctly in the first instance, when we add a 20 degree rotation, it's completely screwed. I'm not sure if this is due to the extreme differences in the x and y dimensions, which alter the length of a and b, or for some other reason. Many thanks, Roger On 14/09/2010 18:00, Josef Spidlen wrote: > Hi Roger, > I am not the author of flowCore but I believe the ellipsoid gate > constructor takes covariance matrices since this is used in the > Gating-ML specification. Mathematically, this is a nice way to specify > ellipsoids in multidimensional space. > > I have posted a simple spreadsheet that allows you to convert the > representation of an ellipse by half-axes, rotation and centre point to > covariance matrix, mean (= centre point), and distance square (=1 for > this case). Please follow this link for download: > http://sourceforge.net/projects/flowcyt/files/Gating-ML/Gating- ML%201.5/Elli pseCalculations.xls/download > > I hope this addresses your issues; let me know if further help is needed. > > Unfortunately, I'll have to leave it up to the flowCore/flowViz authors > to get back to you with your other questions. > > Cheers, > Josef > >> Message: 6 >> Date: Mon, 13 Sep 2010 15:51:55 +0100 >> From: Roger Leigh<rleigh at="" codelibre.net=""> >> To: bioconductor at stat.math.ethz.ch >> Subject: [BioC] FlowCore/FlowViz issues >> Message-ID:<20100913145155.GW6128 at codelibre.net> >> Content-Type: text/plain; charset=utf-8 >> >> Hi, >> >> [Apologies if this is delivered twice; my initial mail didn't >> appear to be accepted for several hours, and I didn't see any >> failure message; does the list object to GPG signatures?] >> >> I've just started to use FlowCore/FlowViz to analyse some of my >> flow cytometry data, and ran into a few problems. I'm hoping >> that you might be able to point me in the right direction! >> >> I've been very pleased with it so far, and have got some nice >> plots and stats out of it, but I'm sure I'm doing some things >> very inefficiently and/or incorrectly! >> >> [I'm using R version 2.11.1 (2010-05-31) on x86_64-pc-linux-gnu >> (Debian GNU/Linux) with current Bioconductor packages)] >> >> >> 1) Gating with an ellipsoidGate >> >> cov<- matrix(c(400000000, 0, 0, 0.08), ncol=2, >> dimnames=list(c("FS.Lin", "SS.Log"), c("FS.Lin", "SS.Log"))) >> mean<- c("FS.Lin"=32000, "SS.Log"=2.8) >> cells<- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) >> >> I want to select my cells using an ellipsoid gate on forward- and side- >> scatter plots. In the above situation, they lie in a region where >> FS=32000?10000 and log??(SS)=2.8?0.5. However, the values in the >> covariance matrix don't match the dimensions; is there any explanation >> regarding how to construct a covariance matrix from the actual >> dimension I want (I got the above by trial and error until it fitted >> nicely--I'm afraid I know little about these matrices). >> >> In some plots I'd also like to rotate the ellipse, but I'm not sure >> how to put this into the matrix, if that's the way to do things. >> Is this possible? >> >> Is there an alternative constructor to create a gate from real >> dimensions? >> >> >> >> Many thanks for all your help, >> Roger >> >> -- >> .''`. Roger Leigh >> : :' : Debian GNU/Linux http://people.debian.org/~rleigh/ >> `. `' Printing on GNU/Linux? http://gutenprint.sourceforge.net/ >> `- GPG Public Key: 0x25BFB848 Please GPG sign your mail. >> >> > ------------------------------ Message: 2 Date: Wed, 15 Sep 2010 12:37:40 +0100 From: Roger Leigh <rleigh@codelibre.net> To: bioconductor at stat.math.ethz.ch Cc: jspidlen at bccrc.ca Subject: Re: [BioC] Gating with an ellipsoidGate Message-ID: <4C90B004.1070405 at codelibre.net> Content-Type: text/plain; charset=ISO-8859-1; format=flowed On 15/09/2010 12:24, Roger Leigh wrote: > This appears to work well when rotation is not involved. However, as > soon as I try to add rotation, it blows up: > > library(flowCore) > library(flowViz) > library(flowUtils) > > cov.matrix <- function (a, b, angle) { > theta <- angle * (pi/180) > > c1 <- ((cos(theta)^2)/a^2) + ((sin(theta)^2)/b^2) > c2 <- sin(theta) * cos(theta) * ((1/a^2) - (1/b^2)) > c3 <- ((sin(theta)^2)/a^2) + ((cos(theta)^2)/b^2) > > m1 <- matrix(c(c1, c2, c2, c3), byrow=TRUE, ncol=2) > m2 <- solve(m1) > > m2 > } > > d <- read.FCS("0isotype.fcs", alter.names=TRUE) > d <- transform(d, `SS.Log` = log10(`SS.Log`)) > > # No rotation > cov <- cov.matrix(20000, 0.4, 0) > colnames(cov) <- c("FS.Lin", "SS.Log") > rownames(cov) <- c("FS.Lin", "SS.Log") > mean <- c("FS.Lin"=40000, "SS.Log"=2.8) > cells <- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) > print(cov) > pdf("test.pdf", width=8, height=8, pointsize=12) > print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", > ylab=expression(log[10]~(SS)))) > dev.off() > > # Now repeat with a 20 degree rotation... > cov <- cov.matrix(20000, 0.4, 20) > colnames(cov) <- c("FS.Lin", "SS.Log") > rownames(cov) <- c("FS.Lin", "SS.Log") > mean <- c("FS.Lin"=40000, "SS.Log"=2.8) > cells <- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) > print(cov) > > pdf("test-rotate.pdf", width=8, height=8, pointsize=12) > print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", > ylab=expression(log[10]~(SS)))) > dev.off() > > I've put the source data, script and results at: > http://www-users.york.ac.uk/~rl522/flowcore-test/ > > http://www-users.york.ac.uk/~rl522/flowcore-test/test.pdf > http://www-users.york.ac.uk/~rl522/flowcore-test/test-rotate.pdf > > You can see that while an ellipse is drawn correctly in the first > instance, when we add a 20 degree rotation, it's completely screwed. I'm > not sure if this is due to the extreme differences in the x and y > dimensions, which alter the length of a and b, or for some other reason. cov <- cov.matrix(20000, 0.4, 20) While this gives what looks like an almost entirely vertical line, using a much smaller angle cov <- cov.matrix(20000, .25, 0.0005) actually results in a sensible rotation, of about 20 degrees. So it looks like it works, but the scaling of the angle is wrong--it's far too sensitive. I'm correctly converting from degrees to radians, so I'm not sure why 8.7e-6 radians actually results in a rotation of ~0.35 radians! Regards, Roger ------------------------------ Message: 3 Date: Wed, 15 Sep 2010 15:25:50 +0200 From: Wolfgang Huber <whuber@embl.de> To: sohkhan at notes.cc.sunysb.edu Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] probeAnno for tilingArray package. Message-ID: <4C90C95E.6070209 at embl.de> Content-Type: text/plain; charset=ISO-8859-1; format=flowed On Sep/14/10 7:36 PM, sohkhan at notes.cc.sunysb.edu wrote: > Thank you Wolfgang. > I corrected the chr parameter as suggested by Noah. segChrom runs with > strand=NA. Now, I have question about the probeAnno creation by > bpmapToProbeAnno(). Does the function treat 0's as "-" and 1's "+" strand. > I have just one BPMAP file for S.pombe from Affymetrix which contains > 0's and 1's in the strand column. Why don't you just try and report problems, if any? Wolfgang > Thanks again. > -Sohail > > > > From: Wolfgang Huber <whuber at="" embl.de=""> > To: bioconductor at stat.math.ethz.ch > Date: 09/11/2010 08:15 AM > Subject: Re: [BioC] probeAnno for tilingArray package. > Sent by: bioconductor-bounces at stat.math.ethz.ch > -------------------------------------------------------------------- ---- > > > > Dear Sohail > > in R, the character string "NA" is different from the value NA. Try with > supplying the second one for the argument 'strands'. Also, please make > sure that the 'chr' argument is set to the right value, the default > (1:17) is likely not to work with S.pombe. You can see what the > chromosome names are in your organism by inspecting the probeAnno object. > > Best wishes > Wolfgang > > On Sep/8/10 10:33 PM, sohkhan at notes.cc.sunysb.edu wrote: > > Hi all, > > > > I am trying to use the "tilingArray" package to analyze Affymetrix > S.pombe > > tiling arrays. I downloaded the .bpmap file from Affymetrix's website, > > and created a probeAnno object by bpmapToProbeAnno(). > > However, I am getting the following error when I try to segment the data > > by segChrom(). > > > >> segdf<-segChrom(cels,probeAnno,strands="NA") > > Running 'segment' on chromosome 1.NAError in probeAnno[w] : > > No mapping '1.NA.start' in this 'probeAnno' object. > > > >> sessionInfo() > > R version 2.11.1 (2010-05-31) > > x86_64-apple-darwin9.8.0 > > > > locale: > > [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 > > > > attached base packages: > > [1] grid stats graphics grDevices utils datasets methods > > base > > > > other attached packages: > > [1] davidTiling_1.2.12 GO.db_2.4.1 RSQLite_0.9-2 DBI_0.2-5 > > AnnotationDbi_1.10.2 tilingArray_1.26.0 pixmap_0.4-10 Starr_1.4.4 > > > > [9] affy_1.26.1 Ringo_1.12.0 Matrix_0.999375-39 > > lattice_0.18-8 limma_3.4.4 RColorBrewer_1.0-2 Biobase_2.8.0 > > affxparser_1.20.0 > > > > loaded via a namespace (and not attached): > > [1] affyio_1.16.0 annotate_1.26.1 genefilter_1.30.0 > > MASS_7.3-6 preprocessCore_1.10.0 pspline_1.0-14 splines_2.11.1 > > strucchange_1.4-1 > > [9] survival_2.35-8 tools_2.11.1 vsn_3.16.0 xtable_1.5-6 > > > > > > I am wondering if I used the correct .bpmap file?? > > Thanks for any suggestion/advice. > > > > -Sohail > > SBU, NY > >> > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor at stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- > > > Wolfgang Huber > EMBL > http://www.embl.de/research/units/genome_biology/huber > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber ------------------------------ Message: 4 Date: Wed, 15 Sep 2010 10:48:11 -0400 From: Yogesh <yogeshp08@gmail.com> To: Bioconductor-R Mailing List <bioconductor at="" stat.math.ethz.ch=""> Subject: [BioC] Comparing 2 GEO datasets Message-ID: <aanlktimq2fyrdrvv=n2-eaw97-ap=gxfuuyjgigeo92r at="" mail.gmail.com=""> Content-Type: text/plain Hello, I want to compare two GEO datasets based on the time series data. I want to be able to draw some visual highlighting/showing the difference in the expression of the two sets. Can some one please guide me on how to go about it? Thanks a lot. -Yogesh [[alternative HTML version deleted]] ------------------------------ Message: 5 Date: Wed, 15 Sep 2010 11:27:55 -0400 From: "Zhu, Julie" <julie.zhu@umassmed.edu> To: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: [BioC] Oligo package crlmm method Message-ID: <c8b65e3b.3370%julie.zhu at="" umassmed.edu=""> Content-Type: text/plain; charset=us-ascii Hi, I am using the crlmm function in Oligo package to make SNP calls. I encountered the following segfault error. Could you please let me know what could be the cause and how to tackle the problem? Thanks so much for your help! Kind regards, Julie library("oligo") library("pd.mapping250k.sty") fullFilenames <- list.celfiles("Sty_CEL_CML", full.names = TRUE) outputDir <- file.path(getwd(), "crlmmResults_StyCML") if (!file.exists(outputDir)) crlmm(fullFilenames, outputDir) *** caught segfault *** address 0x12de80000, cause 'memory not mapped' Traceback: 1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") 2: readCelHeader(x) 3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem- 250ksty.CEL", "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem- 250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL- 250ksty.CEL", "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[16L]], ...) 4: lapply(X, FUN, ...) 5: sapply(filenames, function(x) readCelHeader(x)$chiptype) 6: justCRLMMv3(filenames, outdir, batch_size = batch_size, recalibrate = recalibrate, minLLRforCalls = minLLRforCalls, balance = balance, verbose = verbose) 7: crlmm(fullFilenames, outputDir) Possible actions: 1: abort (with core dump, if enabled) 2: normal R exit 3: exit R without saving workspace 4: exit R saving workspace Selection: sessionInfo() R version 2.11.1 (2010-05-31) x86_64-apple-darwin9.8.0 locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] pd.mapping250k.sty_1.0.0 RSQLite_0.9-2 DBI_0.2-5 [4] oligo_1.12.2 oligoClasses_1.10.0 Biobase_2.8.0 loaded via a namespace (and not attached): [1] affxparser_1.20.0 affyio_1.16.0 Biostrings_2.16.9 IRanges_1.6.11 [5] preprocessCore_1.10.0 splines_2.11.1 ------------------------------ Message: 6 Date: Wed, 15 Sep 2010 16:43:18 +0100 From: Benilton Carvalho <beniltoncarvalho@gmail.com> To: "Zhu, Julie" <julie.zhu at="" umassmed.edu=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Oligo package crlmm method Message-ID: <aanlktim7vw2yk+ze_xjsgkgp5v32dyo67uxy774rknah at="" mail.gmail.com=""> Content-Type: text/plain; charset=ISO-8859-1 Start a new session of R and try the following: cels = list.files("Sty_CEL_CML", pattern="CEL$", full=TRUE) library(affxparser) table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) and let me know what you get. b On 15 September 2010 16:27, Zhu, Julie <julie.zhu at="" umassmed.edu=""> wrote: > Hi, > > I am using the crlmm function in Oligo package to make SNP calls. I > encountered the following segfault error. Could you please let me know what > could be the cause and how to tackle the problem? > > Thanks so much for your help! > > Kind regards, > > Julie > > library("oligo") > library("pd.mapping250k.sty") > fullFilenames <- list.celfiles("Sty_CEL_CML", full.names = TRUE) > outputDir <- file.path(getwd(), "crlmmResults_StyCML") > if (!file.exists(outputDir)) crlmm(fullFilenames, outputDir) > > ?*** caught segfault *** > address 0x12de80000, cause 'memory not mapped' > > Traceback: > ?1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") > ?2: readCelHeader(x) > ?3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP- 250ksty.CEL", > "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem-250ksty.CEL", > "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP- 250ksty.CEL", > "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC- 250ksty.CEL", > "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem- 250ksty.CEL", > "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC- 250ksty.CEL", > "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[16L]], ...) > ?4: lapply(X, FUN, ...) > ?5: sapply(filenames, function(x) readCelHeader(x)$chiptype) > ?6: justCRLMMv3(filenames, outdir, batch_size = batch_size, recalibrate = > recalibrate, ? ? minLLRforCalls = minLLRforCalls, balance = balance, verbose > = verbose) > ?7: crlmm(fullFilenames, outputDir) > > Possible actions: > 1: abort (with core dump, if enabled) > 2: normal R exit > 3: exit R without saving workspace > 4: exit R saving workspace > Selection: > > sessionInfo() > R version 2.11.1 (2010-05-31) > x86_64-apple-darwin9.8.0 > > locale: > [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base > > other attached packages: > [1] pd.mapping250k.sty_1.0.0 RSQLite_0.9-2 ? ? ? ? ? ?DBI_0.2-5 > [4] oligo_1.12.2 ? ? ? ? ? ? oligoClasses_1.10.0 ? ? ?Biobase_2.8.0 > > loaded via a namespace (and not attached): > [1] affxparser_1.20.0 ? ? affyio_1.16.0 ? ? ? ? Biostrings_2.16.9 > IRanges_1.6.11 > [5] preprocessCore_1.10.0 splines_2.11.1 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > ------------------------------ Message: 7 Date: Wed, 15 Sep 2010 11:55:01 -0400 From: "Zhu, Julie" <julie.zhu@umassmed.edu> To: "Benilton Carvalho" <beniltoncarvalho at="" gmail.com=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Oligo package crlmm method Message-ID: <c8b66495.3376%julie.zhu at="" umassmed.edu=""> Content-Type: text/plain; charset=iso-8859-1 Benilton, Thank you so much for such a quick response! I tried the following code and got the following error. Best regards, Julie table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) *** caught segfault *** address 0x1abb9000, cause 'memory not mapped' Traceback: 1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") 2: readCelHeader(x) 3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem- 250ksty.CEL", "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP- 250ksty.CEL", "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem- 250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL- 250ksty.CEL", "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC- 250ksty.CEL", "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP- 250ksty.CEL", "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[12L]], ...) 4: lapply(X, FUN, ...) 5: sapply(cels, function(x) readCelHeader(x)[["chiptype"]]) 6: table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) Possible actions: 1: abort (with core dump, if enabled) 2: normal R exit 3: exit R without saving workspace 4: exit R saving workspace Selection: On 9/15/10 11:43 AM, "Benilton Carvalho" <beniltoncarvalho at="" gmail.com=""> wrote: > Start a new session of R and try the following: > > cels = list.files("Sty_CEL_CML", pattern="CEL$", full=TRUE) > library(affxparser) > table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) > > and let me know what you get. > > b > > On 15 September 2010 16:27, Zhu, Julie <julie.zhu at="" umassmed.edu=""> wrote: >> Hi, >> >> I am using the crlmm function in Oligo package to make SNP calls. I >> encountered the following segfault error. Could you please let me know what >> could be the cause and how to tackle the problem? >> >> Thanks so much for your help! >> >> Kind regards, >> >> Julie >> >> library("oligo") >> library("pd.mapping250k.sty") >> fullFilenames <- list.celfiles("Sty_CEL_CML", full.names = TRUE) >> outputDir <- file.path(getwd(), "crlmmResults_StyCML") >> if (!file.exists(outputDir)) crlmm(fullFilenames, outputDir) >> >> ?*** caught segfault *** >> address 0x12de80000, cause 'memory not mapped' >> >> Traceback: >> ?1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") >> ?2: readCelHeader(x) >> ?3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP-250ksty.CEL", >> "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP-250ksty.CEL", >> "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP-250ksty.CEL", >> "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", >> "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", >> "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem-250ksty.CEL", >> "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP-250ksty.CEL", >> "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", >> "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", >> "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP-250ksty.CEL", >> "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", >> "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP-250ksty.CEL", >> "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP-250ksty.CEL", >> "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP-250ksty.CEL", >> "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", >> "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", >> "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", >> "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP-250ksty.CEL", >> "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC- 250ksty.CEL", >> "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem-250ksty.CEL", >> "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL-250ksty.CEL", >> "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP-250ksty.CEL", >> "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP- 250ksty.CEL", >> "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP- 250ksty.CEL", >> "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", >> "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[16L]], ...) >> ?4: lapply(X, FUN, ...) >> ?5: sapply(filenames, function(x) readCelHeader(x)$chiptype) >> ?6: justCRLMMv3(filenames, outdir, batch_size = batch_size, recalibrate = >> recalibrate, ? ? minLLRforCalls = minLLRforCalls, balance = balance, verbose >> = verbose) >> ?7: crlmm(fullFilenames, outputDir) >> >> Possible actions: >> 1: abort (with core dump, if enabled) >> 2: normal R exit >> 3: exit R without saving workspace >> 4: exit R saving workspace >> Selection: >> >> sessionInfo() >> R version 2.11.1 (2010-05-31) >> x86_64-apple-darwin9.8.0 >> >> locale: >> [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 >> >> attached base packages: >> [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base >> >> other attached packages: >> [1] pd.mapping250k.sty_1.0.0 RSQLite_0.9-2 ? ? ? ? ? ?DBI_0.2-5 >> [4] oligo_1.12.2 ? ? ? ? ? ? oligoClasses_1.10.0 ? ? ?Biobase_2.8.0 >> >> loaded via a namespace (and not attached): >> [1] affxparser_1.20.0 ? ? affyio_1.16.0 ? ? ? ? Biostrings_2.16.9 >> IRanges_1.6.11 >> [5] preprocessCore_1.10.0 splines_2.11.1 >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > ------------------------------ Message: 8 Date: Wed, 15 Sep 2010 12:03:48 -0400 From: Sean Davis <sdavis2@mail.nih.gov> To: Yogesh <yogeshp08 at="" gmail.com=""> Cc: Bioconductor-R Mailing List <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Comparing 2 GEO datasets Message-ID: <aanlktik8p=zn094qddey+hqzfwveqwmyusbf2g5kbjdp at="" mail.gmail.com=""> Content-Type: text/plain On Wed, Sep 15, 2010 at 10:48 AM, Yogesh <yogeshp08 at="" gmail.com=""> wrote: > Hello, > > I want to compare two GEO datasets based on the time series data. I want to > be able to draw some visual highlighting/showing the difference in the > expression of the two sets. > > Can some one please guide me on how to go about it? > > Hi, Yogesh. It is not really possible to answer this question as your problem is much too vague. You will need to be much more detailed about what you are planning and what you have already done. Sean [[alternative HTML version deleted]] ------------------------------ Message: 9 Date: Wed, 15 Sep 2010 12:36:01 -0400 From: Yogesh <yogeshp08@gmail.com> To: Sean Davis <sdavis2 at="" mail.nih.gov=""> Cc: Bioconductor-R Mailing List <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Comparing 2 GEO datasets Message-ID: <aanlktikyf0nikv_y4on_iphgsh04vg3szruz3szdk=ga at="" mail.gmail.com=""> Content-Type: text/plain Hi Sean, I have two GEO sets to compare (from two different experiments). I tried to use limma package. But most of the packages use groups within a single dataset for the t-test. I have 2 datasets with 24 time-points each. The basic experiment in both cases is the same. I want to compare, say time-point 0min vs. time-point 0min from both sets. I do not know what the expression is. I want to check if the expression changes at a particular time-point (from 0-24 time-points). I believe a line-chart or a volcano-plot (single time-point or whole set) will help understand this better. I thought I will ask for some guidance on how to go about it so that I can try something in that direction. Thanks, -Yogesh On 15 September 2010 12:03, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > On Wed, Sep 15, 2010 at 10:48 AM, Yogesh <yogeshp08 at="" gmail.com=""> wrote: > >> Hello, >> >> I want to compare two GEO datasets based on the time series data. I want >> to >> be able to draw some visual highlighting/showing the difference in the >> expression of the two sets. >> >> Can some one please guide me on how to go about it? >> >> > Hi, Yogesh. > > It is not really possible to answer this question as your problem is much > too vague. You will need to be much more detailed about what you are > planning and what you have already done. > > Sean > > [[alternative HTML version deleted]] ------------------------------ Message: 10 Date: Wed, 15 Sep 2010 12:46:20 -0400 From: "Zhu, Julie" <julie.zhu@umassmed.edu> To: "Benilton Carvalho" <beniltoncarvalho at="" gmail.com=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Oligo package crlmm method Message-ID: <c8b6709c.337f%julie.zhu at="" umassmed.edu=""> Content-Type: text/plain; charset=iso-8859-1 Benilton, Thanks for the debugging code! It helped me to get down to two files with unreadable header. Best regards, Julie On 9/15/10 11:55 AM, "Julie Zhu" <julie.zhu at="" umassmed.edu=""> wrote: > Benilton, > > Thank you so much for such a quick response! > > I tried the following code and got the following error. > > Best regards, > > Julie > > > table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) > > *** caught segfault *** > address 0x1abb9000, cause 'memory not mapped' > > Traceback: > 1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") > 2: readCelHeader(x) > 3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP- 250ksty.CEL", > "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem-250ksty.CEL", > "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP-250ksty.CEL", > "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", > "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP- 250ksty.CEL", > "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC- 250ksty.CEL", > "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem- 250ksty.CEL", > "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL-250ksty.CEL", > "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC- 250ksty.CEL", > "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP-250ksty.CEL", > "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP- 250ksty.CEL", > "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", > "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[12L]], ...) > 4: lapply(X, FUN, ...) > 5: sapply(cels, function(x) readCelHeader(x)[["chiptype"]]) > 6: table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) > > Possible actions: > 1: abort (with core dump, if enabled) > 2: normal R exit > 3: exit R without saving workspace > 4: exit R saving workspace > Selection: > > > On 9/15/10 11:43 AM, "Benilton Carvalho" <beniltoncarvalho at="" gmail.com=""> wrote: > >> Start a new session of R and try the following: >> >> cels = list.files("Sty_CEL_CML", pattern="CEL$", full=TRUE) >> library(affxparser) >> table(sapply(cels, function(x) readCelHeader(x)[["chiptype"]])) >> >> and let me know what you get. >> >> b >> >> On 15 September 2010 16:27, Zhu, Julie <julie.zhu at="" umassmed.edu=""> wrote: >>> Hi, >>> >>> I am using the crlmm function in Oligo package to make SNP calls. I >>> encountered the following segfault error. Could you please let me know what >>> could be the cause and how to tackle the problem? >>> >>> Thanks so much for your help! >>> >>> Kind regards, >>> >>> Julie >>> >>> library("oligo") >>> library("pd.mapping250k.sty") >>> fullFilenames <- list.celfiles("Sty_CEL_CML", full.names = TRUE) >>> outputDir <- file.path(getwd(), "crlmmResults_StyCML") >>> if (!file.exists(outputDir)) crlmm(fullFilenames, outputDir) >>> >>> ?*** caught segfault *** >>> address 0x12de80000, cause 'memory not mapped' >>> >>> Traceback: >>> ?1: .Call("R_affx_get_cel_file_header", filename, PACKAGE = "affxparser") >>> ?2: readCelHeader(x) >>> ?3: FUN(c("Sty_CEL_CML/CML-#1-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#1-CP-250ksty.CEL", "Sty_CEL_CML/CML-#10-AP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#10-CP-250ksty.CEL", "Sty_CEL_CML/CML-#11-AP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#11-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-AP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#12-CP-250ksty.CEL", "Sty_CEL_CML/CML-#12-CP2-250ksty.CEL", >>> "Sty_CEL_CML/CML-#13-CP-250ksty.CEL", "Sty_CEL_CML/CML-#13-CP2-250ksty.CEL", >>> "Sty_CEL_CML/CML-#14-BC-250ksty.CEL", "Sty_CEL_CML/CML-#14-Rem-250ksty.CEL", >>> "Sty_CEL_CML/CML-#15-BC-250ksty.CEL", "Sty_CEL_CML/CML-#15-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#15-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#16-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#16-BC-GL-250ksty.CEL", >>> "Sty_CEL_CML/CML-#16-CP-250ksty.CEL", "Sty_CEL_CML/CML-#16-CP2-250ksty.CEL", >>> "Sty_CEL_CML/CML-#17-AP-250ksty.CEL", "Sty_CEL_CML/CML-#17-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#18-BC-250ksty.CEL", "Sty_CEL_CML/CML-#19-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#19-BC-GL-250ksty.CEL", >>> "Sty_CEL_CML/CML-#19-CP-250ksty.CEL", "Sty_CEL_CML/CML-#2-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#2-CP2-250ksty.CEL", "Sty_CEL_CML/CML-#20-AP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#20-BC-250ksty.CEL", "Sty_CEL_CML/CML-#20-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#21-CP-250ksty.CEL", "Sty_CEL_CML/CML-#21-CP2-250ksty.CEL", >>> "Sty_CEL_CML/CML-#22-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#22-BC-GL-250ksty.CEL", >>> "Sty_CEL_CML/CML-#22-CP-250ksty.CEL", "Sty_CEL_CML/CML-#23-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#23-BC-GL-250ksty.CEL", >>> "Sty_CEL_CML/CML-#23-CP-250ksty.CEL", "Sty_CEL_CML/CML-#3-AP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#3-BC-250ksty.CEL", "Sty_CEL_CML/CML-#4-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#4-CP-250ksty.CEL", "Sty_CEL_CML/CML-#4-Rem-250ksty.CEL", >>> "Sty_CEL_CML/CML-#5-BC-250ksty.CEL", "Sty_CEL_CML/CML-#5-BC-GL-250ksty.CEL", >>> "Sty_CEL_CML/CML-#5-Rem-250ksty.CEL", "Sty_CEL_CML/CML-#6-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#6-BC-GL-250ksty.CEL", "Sty_CEL_CML/CML-#6-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#7-BC-250ksty.CEL", "Sty_CEL_CML/CML-#7-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#8-AP-250ksty.CEL", "Sty_CEL_CML/CML-#8-CP-250ksty.CEL", >>> "Sty_CEL_CML/CML-#9-BC-250ksty.CEL", >>> "Sty_CEL_CML/CML-#9-BC-GL-250ksty.CEL")[[16L]], ...) >>> ?4: lapply(X, FUN, ...) >>> ?5: sapply(filenames, function(x) readCelHeader(x)$chiptype) >>> ?6: justCRLMMv3(filenames, outdir, batch_size = batch_size, recalibrate = >>> recalibrate, ? ? minLLRforCalls = minLLRforCalls, balance = balance, verbose >>> = verbose) >>> ?7: crlmm(fullFilenames, outputDir) >>> >>> Possible actions: >>> 1: abort (with core dump, if enabled) >>> 2: normal R exit >>> 3: exit R without saving workspace >>> 4: exit R saving workspace >>> Selection: >>> >>> sessionInfo() >>> R version 2.11.1 (2010-05-31) >>> x86_64-apple-darwin9.8.0 >>> >>> locale: >>> [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 >>> >>> attached base packages: >>> [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base >>> >>> other attached packages: >>> [1] pd.mapping250k.sty_1.0.0 RSQLite_0.9-2 ? ? ? ? ? ?DBI_0.2-5 >>> [4] oligo_1.12.2 ? ? ? ? ? ? oligoClasses_1.10.0 ? ? ?Biobase_2.8.0 >>> >>> loaded via a namespace (and not attached): >>> [1] affxparser_1.20.0 ? ? affyio_1.16.0 ? ? ? ? Biostrings_2.16.9 >>> IRanges_1.6.11 >>> [5] preprocessCore_1.10.0 splines_2.11.1 >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > ------------------------------ Message: 11 Date: Wed, 15 Sep 2010 10:48:33 -0600 From: "Brown, Joseph" <brownjo@njhealth.org> To: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] Agi4x44PreProcess- RSQLite driver: (RS_SQLite_fetch: failed first step: disk I/O error) Message-ID: <24B2F1E784B3A8498804477290B70C4003718B87 at ePostoffice.NJRC.ORG> Content-Type: text/plain Regarding: RSQLite driver: (RS_SQLite_fetch: failed first step: disk I/O error) We successfully diagnosed and solved this problem. We tried the suggestions of installing the R packages on a local disk and ran RSQLite-specific commands which indicated the RSQLite-package was installed and working but the error persisted. The problem manifested in a call to the lookUp() function of the 'annotate' package which uses RSQLite. It appeared that for the version installed on the server, there was some hard-coded limit in the underlying (non-R) SQLite that caused calls to lookUp() with > 24814 items to throw the error. In particular, sqlite 3.3.6-2 was installed on our server that demonstrated the error, while on a separate server running 3.3.6-5, the function ran properly. Thus, we installed sqlite with the newest date on the src tree under 3.3.6 from http://www.sqlite.org/src/info/715cecb8c7 on our server and we no longer see this error. For future reference, this snippet of code was enough to pinpoint the problem (we noticed that could use lookUp for subsets of all PROBE_ID but not for the full vector of PROBE_ID): ----------------------- library(annotate) library(mgug4112a.db) PROBE_ID = <list of="" probe="" ids=""> # This returns ACCNUM without error for first PROBE_ID GENE_ID= lookUp(PROBE_ID[1], 'mgug4112a.db', "ACCNUM") # This gets RSQLite error and any subsequent calls to lookUp show # error, have to restart R session to reset -> dislikes whole vector as input GENE_ID= lookUp(PROBE_ID, 'mgug4112a.db', "ACCNUM") # has RSQLite error # This displays ACCNUM for each PROBE_ID individually, without error # so problem isn't a specific PROBE_ID[i] for (i in 0:length(PROBE_ID)) { GENE_ID= lookUp(PROBE_ID[i], 'mgug4112a.db', "ACCNUM"); cat(i, " ", GENE_ID,"\n") } # This displays i-th ACCNUM when lookUp 1:i but get error for # i=24815, reproducibly and any subsequent call to lookUp displays # RSQLite error - seems to be hard-coded size limit in SQLite for (i in 0:length(PROBE_ID)) { GENE_ID= lookUp(PROBE_ID[1:i], 'mgug4112a.db', "ACCNUM"); cat(i, " ", GENE_ID[i],"\n") } --------- Previous Posts ----------------------------- From: Benilton Carvalho [mailto:beniltoncarvalho@gmail.com] Sent: Wednesday, August 25, 2010 2:44 PM To: Brown, Joseph Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] Agi4x44PreProcess- RSQLite driver: (RS_SQLite_fetch: failed first step: disk I/O error) I have seen something similar (with RSQLite alone)... and, back in the day, it appeared to have an association with the fact that the library directory was on a network drive (and the connection of the machine with the storage system was not that great)... Installing the RSQLite-dependent package on a local disk always solved the problem for me... b On 25 August 2010 21:10, Brown, Joseph <brownjo at="" njhealth.org=""> wrote: > Issue: > > When trying to execute filter.probes the following error occurs: > >>ddFILT=filter.probes(ddNORM,control,wellaboveBG,isfound,wellaboveNEG ,s a > t,PopnOL,NonUnifOL,nas,limWellAbove,limISF,limNEG,limSAT,limPopnOL,lim No > nUnifOL,limNAS,makePLOT=FALSE, annotation.package,flag.counts,targets) > > FILTERING PROBES BY FLAGS > > > > > > FILTERING BY ControlType FLAG > > Error in sqliteFetch(rs, n = -1, ...) : > > RSQLite driver: (RS_SQLite_fetch: failed first step: disk I/O error) > > > > > > This issue is reproducible on this particular Linux installation only > (craps out on all data sets, including sets known to work), so I've > tried uninstalling and reinstalling R, as well as the packages, several > times. Utilizing the same data, I have been able to get results using > Windows installations and other Linux installs. > > > > Has anyone seen this error before and/or have ideas to remedy the > problem? > > > > My session info: > >> sessionInfo() > > R version 2.11.1 (2010-05-31) > > x86_64-unknown-linux-gnu > > > > locale: > > [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C > > [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 > > [5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8 > > [7] LC_PAPER=en_US.UTF-8 LC_NAME=C > > [9] LC_ADDRESS=C LC_TELEPHONE=C > > [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C > > > > attached base packages: > > [1] stats graphics grDevices utils datasets methods base > > > > other attached packages: > > [1] vsn_3.16.0 mgug4122a.db_2.4.1 org.Mm.eg.db_2.4.1 > > [4] RSQLite_0.9-2 DBI_0.2-5 > Agi4x44PreProcess_1.8.0 > > [7] genefilter_1.30.0 annotate_1.26.1 > AnnotationDbi_1.10.2 > > [10] limma_3.4.4 Biobase_2.8.0 > > > > loaded via a namespace (and not attached): > > [1] affy_1.26.1 affyio_1.16.0 grid_2.11.1 > > [4] lattice_0.18-8 preprocessCore_1.10.0 splines_2.11.1 > > [7] survival_2.35-8 xtable_1.5-6 > NOTICE: This email message is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. [[alternative HTML version deleted]] ------------------------------ Message: 12 Date: Wed, 15 Sep 2010 12:49:29 -0400 From: Sean Davis <sdavis2@mail.nih.gov> To: Yogesh <yogeshp08 at="" gmail.com=""> Cc: Bioconductor-R Mailing List <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Comparing 2 GEO datasets Message-ID: <aanlkti=r7mk8ag2rb+hqssmrhwj4k2gkms8_6q8-_vp_ at="" mail.gmail.com=""> Content-Type: text/plain On Wed, Sep 15, 2010 at 12:36 PM, Yogesh <yogeshp08 at="" gmail.com=""> wrote: > Hi Sean, > > I have two GEO sets to compare (from two different experiments). I tried to > use limma package. But most of the packages use groups within a single > dataset for the t-test. I have 2 datasets with 24 time-points each. The > basic experiment in both cases is the same. I want to compare, say > time-point 0min vs. time-point 0min from both sets. I do not know what the > expression is. I want to check if the expression changes at a particular > time-point (from 0-24 time-points). I believe a line-chart or a volcano-plot > (single time-point or whole set) will help understand this better. > > You will not likely be able to compare the two datasets to each other using a t-test or any other test, for that matter, given the perfect confounding of experimental effect and lab/technical variation between the two datasets. Perhaps I am misunderstanding what you are trying to do, though. Sean > I thought I will ask for some guidance on how to go about it so that I can > try something in that direction. > > Thanks, > > -Yogesh > > > > On 15 September 2010 12:03, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > >> >> On Wed, Sep 15, 2010 at 10:48 AM, Yogesh <yogeshp08 at="" gmail.com=""> wrote: >> >>> Hello, >>> >>> I want to compare two GEO datasets based on the time series data. I want >>> to >>> be able to draw some visual highlighting/showing the difference in the >>> expression of the two sets. >>> >>> Can some one please guide me on how to go about it? >>> >>> >> Hi, Yogesh. >> >> It is not really possible to answer this question as your problem is much >> too vague. You will need to be much more detailed about what you are >> planning and what you have already done. >> >> Sean >> >> > > [[alternative HTML version deleted]] ------------------------------ Message: 13 Date: Wed, 15 Sep 2010 13:12:19 -0400 From: Yogesh <yogeshp08@gmail.com> To: Sean Davis <sdavis2 at="" mail.nih.gov=""> Cc: Bioconductor-R Mailing List <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Comparing 2 GEO datasets Message-ID: <aanlktinmo-6ask_atukvpxwsrshw2qxua8bkw9rgfpjz at="" mail.gmail.com=""> Content-Type: text/plain Hi Sean, You are right. I agree. Biologically, it may not be possible to compare because of the experimental conditions. But can it be done just statistically. -Yogesh On 15 September 2010 12:49, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > On Wed, Sep 15, 2010 at 12:36 PM, Yogesh <yogeshp08 at="" gmail.com=""> wrote: > >> Hi Sean, >> >> I have two GEO sets to compare (from two different experiments). I tried >> to use limma package. But most of the packages use groups within a single >> dataset for the t-test. I have 2 datasets with 24 time-points each. The >> basic experiment in both cases is the same. I want to compare, say >> time-point 0min vs. time-point 0min from both sets. I do not know what >> the expression is. I want to check if the expression changes at a particular >> time-point (from 0-24 time-points). I believe a line-chart or a volcano-plot >> (single time-point or whole set) will help understand this better. >> >> > You will not likely be able to compare the two datasets to each other using > a t-test or any other test, for that matter, given the perfect confounding > of experimental effect and lab/technical variation between the two datasets. > Perhaps I am misunderstanding what you are trying to do, though. > > Sean > [[alternative HTML version deleted]] ------------------------------ Message: 14 Date: Tue, 14 Sep 2010 13:36:51 -0400 From: sohkhan@notes.cc.sunysb.edu To: Wolfgang Huber <whuber at="" embl.de=""> Cc: bioconductor-bounces at stat.math.ethz.ch, bioconductor at stat.math.ethz.ch Subject: Re: [BioC] probeAnno for tilingArray package. Message-ID: <of58c99901.2a658da7-on8525779e.005fd5d3-8525779e.0060c3c2 at="" notes.cc.sunysb.e="" du=""> Content-Type: text/plain Thank you Wolfgang. I corrected the chr parameter as suggested by Noah. segChrom runs with strand=NA. Now, I have question about the probeAnno creation by bpmapToProbeAnno(). Does the function treat 0's as "-" and 1's "+" strand. I have just one BPMAP file for S.pombe from Affymetrix which contains 0's and 1's in the strand column. Thanks again. -Sohail From: Wolfgang Huber <whuber@embl.de> To: bioconductor at stat.math.ethz.ch Date: 09/11/2010 08:15 AM Subject: Re: [BioC] probeAnno for tilingArray package. Sent by: bioconductor-bounces at stat.math.ethz.ch Dear Sohail in R, the character string "NA" is different from the value NA. Try with supplying the second one for the argument 'strands'. Also, please make sure that the 'chr' argument is set to the right value, the default (1:17) is likely not to work with S.pombe. You can see what the chromosome names are in your organism by inspecting the probeAnno object. Best wishes Wolfgang On Sep/8/10 10:33 PM, sohkhan at notes.cc.sunysb.edu wrote: > Hi all, > > I am trying to use the "tilingArray" package to analyze Affymetrix S.pombe > tiling arrays. I downloaded the .bpmap file from Affymetrix's website, > and created a probeAnno object by bpmapToProbeAnno(). > However, I am getting the following error when I try to segment the data > by segChrom(). > >> segdf<-segChrom(cels,probeAnno,strands="NA") > Running 'segment' on chromosome 1.NAError in probeAnno[w] : > No mapping '1.NA.start' in this 'probeAnno' object. > >> sessionInfo() > R version 2.11.1 (2010-05-31) > x86_64-apple-darwin9.8.0 > > locale: > [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] grid stats graphics grDevices utils datasets methods > base > > other attached packages: > [1] davidTiling_1.2.12 GO.db_2.4.1 RSQLite_0.9-2 DBI_0.2-5 > AnnotationDbi_1.10.2 tilingArray_1.26.0 pixmap_0.4-10 Starr_1.4.4 > > [9] affy_1.26.1 Ringo_1.12.0 Matrix_0.999375-39 > lattice_0.18-8 limma_3.4.4 RColorBrewer_1.0-2 Biobase_2.8.0 > affxparser_1.20.0 > > loaded via a namespace (and not attached): > [1] affyio_1.16.0 annotate_1.26.1 genefilter_1.30.0 > MASS_7.3-6 preprocessCore_1.10.0 pspline_1.0-14 splines_2.11.1 > strucchange_1.4-1 > [9] survival_2.35-8 tools_2.11.1 vsn_3.16.0 xtable_1.5-6 > > > I am wondering if I used the correct .bpmap file?? > Thanks for any suggestion/advice. > > -Sohail > SBU, NY >> > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]] ------------------------------ Message: 15 Date: Wed, 15 Sep 2010 12:59:19 +0100 From: Elizabeth Ashley <elizabeth.ashley@linacre.ox.ac.uk> To: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: [BioC] Querying clustering in heatmap.2 Message-ID: <01B4B1C924D5FC44B57B269548B562763C553B8634 at EXMBX05.ad.oak.ox.ac.uk> Content-Type: text/plain; charset="us-ascii" Hi I have used heatmap.2 to create a heatmap of my data. It automatically clusters the data and I can't identify which part of the text does this. However I am quite happy to have the clustering (of GO categories) but would like to be able to query the groupings to find insights into how it is clustered. Are these clusters named, and if so can you find out which ones bring back significant data? Below is the script I am using. GO<-read.delim("file.txt",header=TRUE) GO GO[,-c(1)] row.names(GO)<-GO$NAMES source("http://www.bioconductor.org/biocLite.R") biocLite("ALL") library("Ringo") GO_matrix<-data.matrix(GO[,-c(1)]) source("http://www.bioconductor.org/biocLite.R") biocLite("limma") library(limma) library(gplots) heatmap.2(GO_matrix,col=redgreen(256), margins=c(5,10),xlab="DATASET",ylab="GO TERM", key=TRUE,keysize=1,symkey=FALSE,density.info="none", trace="none",cexCol=0.5,cexRow=0.1) Thanks for any help you can provide with this matter Lizzy ------------------------------ Message: 16 Date: Wed, 15 Sep 2010 19:35:02 +0200 From: Eva Benito Garagorri <ebenito@umh.es> To: bioconductor at stat.math.ethz.ch Subject: [BioC] BioNet - interpretation of node score Message-ID: <2EE8CB0B-170D-42B1-9625-6BB9F128D66F at umh.es> Content-Type: text/plain Dear List, I have recently started exploring the Package BioNet for network analysis of microarray data. I have a fundamental question about the interpretation of the results. From my understanding, BioNet first takes a series of p-values, fits them to a model, and then uses this information to score the nodes of a given network (the package is based on a human protein- protein interaction network) and output the best scoring subnetwork. In the 2D visualization of this subnetwork, squares represent nodes with a negative score and circles represent nodes with a positive score. What I do not understand is what the sign of the scores represents, i.e., what is the interpretation of an interaction between two positive nodes vs the interaction of two negative nodes? I am attaching the code from the BioNet Tutorial for the introductory example, only I am not aggregating the pvalues, but rather just taking the pvalues for differential expression (because I am only using this information for my own data). I also changed the FDR threshold so that the resulting network would be smaller. Thanks a lot in advance. Best, Eva ---------- Eva Benito Garagorri PhD program in Neurosciences Institute for Neurosciences in Alicante UMH-CSIC San Juan de Alicante 03550 Spain ebenito at umh.es (34) 965 91 92 33 ---------------- library(BioNet) library(DLBCL) data(dataLym) data(interactome) pvals=dataLym$t.pval names(pvals)=dataLym$label subnet=subNetwork(dataLym$label,interactome) subnet=rmSelfLoops(subnet) fb=fitBumModel(pvals,plot=FALSE) scores=scoreNodes(subnet,fb,fdr=0.000001) module=runFastHeinz(subnet,scores) logFC=dataLym$diff names(logFC)=dataLym$label plotModule(module,scores=scores,diff.expr=logFC) ---------- > sessionInfo() R version 2.11.1 (2010-05-31) x86_64-apple-darwin9.8.0 locale: [1] es_ES.UTF-8/es_ES.UTF-8/C/C/es_ES.UTF-8/es_ES.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] DLBCL_1.2 BioNet_1.6.5 RBGL_1.24.0 graph_1.26.0 Biobase_2.8.0 loaded via a namespace (and not attached): [1] AnnotationDbi_1.10.2 DBI_0.2-5 igraph_0.5.4 [4] RSQLite_0.9-2 tools_2.11.1 XML_3.1-0 > [[alternative HTML version deleted]] ------------------------------ Message: 17 Date: Wed, 15 Sep 2010 11:52:10 -0700 From: Josef Spidlen <jspidlen@bccrc.ca> To: Roger Leigh <rleigh at="" codelibre.net=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Gating with an ellipsoidGate Message-ID: <4C9115DA.2080506 at bccrc.ca> Content-Type: text/plain; charset="ISO-8859-1"; format=flowed Hi Roger, your calculation of the covariance matrix seems fine to me. All seems working fine if your data is on linear axis. I tried the following: library(flowCore) library(flowViz) x <- read.FCS('A01.fcs') cov <- cov.matrix(150, 50, 45) mean <- c(200, 100) names(mean) <- colnames(x)[1:2] colnames(cov) <- colnames(x)[1:2] rownames(cov) <- colnames(x)[1:2] cells <- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) xyplot(`FSC-H` ~ `SSC-H`, x, filter=cells) where the data file can be downloaded from http://flowcyt.sf.net/temp/A01.fcs and cov.matrix is from your code, namely: cov.matrix <- function (a, b, angle) { theta <- angle * (pi/180) c1 <- ((cos(theta)^2)/a^2) + ((sin(theta)^2)/b^2) c2 <- sin(theta) * cos(theta) * ((1/a^2) - (1/b^2)) c3 <- ((sin(theta)^2)/a^2) + ((cos(theta)^2)/b^2) m1 <- matrix(c(c1, c2, c2, c3), byrow=TRUE, ncol=2) m2 <- solve(m1) m2 } This is working well for different angles and - by-eye - the rotation of the ellipse corresponds to the one requested. I am thinking whether your issue is a bug in flowCore/flowViz or just an artifact of having completely different scales for x and y. What I mean is that your ellipse is essentially 50,000x longer in the x dimension than in y dimension and flowViz "adjusts" for that in the display - scaling x and y completely different. If you rotate it even only a tiny bit, everything changes drastically and the new ellipse now needs a completely different scales to show up reasonably. My feeling is that rotating an ellipse in a space that consist of one linear and one log dimension may not be very meaningful and may create weird artifacts. Could you maybe scale the data somehow so that x/y get on similar value range and try if that would work Cheers, Josef On 10-09-15 04:37 AM, Roger Leigh wrote: > On 15/09/2010 12:24, Roger Leigh wrote: > >> This appears to work well when rotation is not involved. However, as >> soon as I try to add rotation, it blows up: >> >> library(flowCore) >> library(flowViz) >> library(flowUtils) >> >> cov.matrix<- function (a, b, angle) { >> theta<- angle * (pi/180) >> >> c1<- ((cos(theta)^2)/a^2) + ((sin(theta)^2)/b^2) >> c2<- sin(theta) * cos(theta) * ((1/a^2) - (1/b^2)) >> c3<- ((sin(theta)^2)/a^2) + ((cos(theta)^2)/b^2) >> >> m1<- matrix(c(c1, c2, c2, c3), byrow=TRUE, ncol=2) >> m2<- solve(m1) >> >> m2 >> } >> >> d<- read.FCS("0isotype.fcs", alter.names=TRUE) >> d<- transform(d, `SS.Log` = log10(`SS.Log`)) >> >> # No rotation >> cov<- cov.matrix(20000, 0.4, 0) >> colnames(cov)<- c("FS.Lin", "SS.Log") >> rownames(cov)<- c("FS.Lin", "SS.Log") >> mean<- c("FS.Lin"=40000, "SS.Log"=2.8) >> cells<- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) >> print(cov) >> pdf("test.pdf", width=8, height=8, pointsize=12) >> print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", >> ylab=expression(log[10]~(SS)))) >> dev.off() >> >> # Now repeat with a 20 degree rotation... >> cov<- cov.matrix(20000, 0.4, 20) >> colnames(cov)<- c("FS.Lin", "SS.Log") >> rownames(cov)<- c("FS.Lin", "SS.Log") >> mean<- c("FS.Lin"=40000, "SS.Log"=2.8) >> cells<- ellipsoidGate(filterId="CellGate", .gate=cov, mean=mean) >> print(cov) >> >> pdf("test-rotate.pdf", width=8, height=8, pointsize=12) >> print(xyplot(`SS.Log` ~ `FS.Lin`, d, filter=cells, xlab="FS", >> ylab=expression(log[10]~(SS)))) >> dev.off() >> >> I've put the source data, script and results at: >> http://www-users.york.ac.uk/~rl522/flowcore-test/ >> >> http://www-users.york.ac.uk/~rl522/flowcore-test/test.pdf >> http://www-users.york.ac.uk/~rl522/flowcore-test/test-rotate.pdf >> >> You can see that while an ellipse is drawn correctly in the first >> instance, when we add a 20 degree rotation, it's completely screwed. I'm >> not sure if this is due to the extreme differences in the x and y >> dimensions, which alter the length of a and b, or for some other reason. >> > cov<- cov.matrix(20000, 0.4, 20) > > While this gives what looks like an almost entirely vertical line, using > a much smaller angle > > cov<- cov.matrix(20000, .25, 0.0005) > > actually results in a sensible rotation, of about 20 degrees. So it > looks like it works, but the scaling of the angle is wrong--it's far too > sensitive. I'm correctly converting from degrees to radians, so I'm not > sure why 8.7e-6 radians actually results in a rotation of ~0.35 radians! > > > Regards, > Roger > > -- Josef Spidlen, Ph.D. Terry Fox Laboratory, BC Cancer Agency 675 West 10th Avenue, V5Z 1L3 Vancouver, BC, Canada Tel: +1 (604) 675-8000 x 7755 http://www.terryfoxlab.ca/people/rbrinkman/josef.aspx ------------------------------ Message: 18 Date: Wed, 15 Sep 2010 22:07:10 +0200 From: Adam Woznica <adam.woznica@unige.ch> To: bioconductor at stat.math.ethz.ch Subject: [BioC] e-LICO multi-omics prediction challenge with background knowledge on Obstructive Nephropathy Message-ID: <4C91276E.4030607 at unige.ch> Content-Type: text/plain; CHARSET=US-ASCII; format=flowed Dear all, We present a biological data-mining problem that poses a number of significant challenges; the available data: (i) are of high dimensionality but of extremely small sample size, (ii) come from different sources which correspond to different biological levels, (iii) exhibit a high degree of feature dependencies and interactions within and between the different sources; some of the interactions between the different sources are known and available as background knowledge, and (iv) are incomplete. This data was obtained from patients with Obstructive Nephropathy (ON) which is the most frequent nephropathy observed among newborns and children, and the first cause of end stage renal disease usually treated by dialysis or transplantation. The goal is to construct diagnostic models that accurately connect the biological levels to the severity of the pathology. We particularly welcome data mining approaches and learning methods that are able to accommodate the available background information in order to address the formidable challenge of high dimensionality small sample size of our setting and deliver better models. A prize is envisaged for the top performing approaches (2500EU in total). The price is sponsored by Rapid-I the company that supports RapidMiner, probably the most popular open-source data mining environment, and the European Commission through the e-Lico EU project. Participants are expected to prepare a paper, maximum 8 pages, describing their approach. We plan to have a number of selected papers considered for publication in a special issue of a journal (to be announced soon). Challenge web page: http://tunedit.org/challenge/ON . Started: Sep 15, 2010 Ends: Dec 19, 2010 Organizing Committee: - Alexandros Kalousis, University of Geneva, Switzerland - Julie Klein, Inserm U858, Toulouse, France - Joost Schanstra, Inserm U858, Toulouse, France - Adam Woznica, University of Geneva, Switzerland Regards, Adam Woznica ------------------------------ Message: 19 Date: Wed, 15 Sep 2010 16:17:25 -0400 From: Steve Lianoglou <mailinglist.honeypot@gmail.com> To: Elizabeth Ashley <elizabeth.ashley at="" linacre.ox.ac.uk=""> Cc: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Subject: Re: [BioC] Querying clustering in heatmap.2 Message-ID: <aanlktinnlzafvgaqpwktqp1uaq_ek5hg-f0p1afz==eq at="" mail.gmail.com=""> Content-Type: text/plain; charset=ISO-8859-1 Hi Lizzy, On Wed, Sep 15, 2010 at 7:59 AM, Elizabeth Ashley <elizabeth.ashley at="" linacre.ox.ac.uk=""> wrote: > Hi > > I have used heatmap.2 to create a heatmap of my data. It automatically clusters the data and I can't identify which part of the text does this. However I am quite happy to have the clustering (of GO categories) but would like to be able to query the groupings to find insights into how it is clustered. Are these clusters named, and if so can you find out which ones bring back significant data? Below is the script I am using. I don't really know what type of data is in your GO matrix, nor do I understand how you intend to find out "which ones bring back significant data" but an answer to your more general question is to look at the help from the heatmap.2 function: "?heatmap.2" Scroll down to the "Value" section, and you'll see that there is a list/structure that is returned "invisibly" (see ?invisible). There are two elements in the list named "rowInd" and "colInd" as well as "rowDendogram" and "colDendrogram" which you can use to start investigating which rows + cols are grouping together. Hope that helps, -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology ?| Memorial Sloan-Kettering Cancer Center ?| Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ------------------------------ _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor End of Bioconductor Digest, Vol 91, Issue 15
SNP Microarray GO Network Clustering Cancer Organism mgug4122a probe limma convert SNP • 1.5k views
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