[Bioc-devel] The story of tracing a derfinder bug on OSX that sometimes popped up, sometimes it didn't. Related to IRanges/S4Vectors '$<-'
Hervé Pagès
hpages at fredhutch.org
Thu Mar 30 08:32:43 CEST 2017
On 03/27/2017 09:43 AM, Michael Lawrence wrote:
> I committed a fix into R trunk with a regression test.
Thanks Michael. Any chance you can port the fix to the 3.4 branch?
H.
>
> On Mon, Mar 27, 2017 at 8:41 AM, Michael Lawrence <michafla at gene.com> wrote:
>> My bad guys, I'll fix when I get to work.
>>
>> On Mon, Mar 27, 2017 at 3:59 AM, Martin Morgan
>> <martin.morgan at roswellpark.org> wrote:
>>> On 03/22/2017 01:12 PM, Hervé Pagès wrote:
>>>>
>>>> Hi Martin,
>>>>
>>>> On 03/22/2017 03:17 AM, Martin Maechler wrote:
>>>>>>>>>>
>>>>>>>>>> Andrzej Oleś <andrzej.oles at gmail.com>
>>>>>>>>>> on Wed, 22 Mar 2017 10:29:57 +0100 writes:
>>>>>
>>>>>
>>>>> > Just for the record, on R-3.3.2 Herve's code fails with the
>>>>> following error:
>>>>> > Error in x[TRUE] <- new("A") :
>>>>> > incompatible types (from S4 to logical) in subassignment type fix
>>>>>
>>>>> yes, (of course).... and I would be interested in a small
>>>>> reproducible example which uses _valid_ code.
>>>>
>>>>
>>>> Looks like before performing the subassignment itself, [<- first tries
>>>> to coerce the RHS to the "mode" of the LHS by calling as.vector() on the
>>>> former. So if we define an as.vector S3 method for A objects:
>>>>
>>>> setClass("A", representation(stuff="numeric"))
>>>> as.vector.A <- function (x, mode="any") x at stuff
>>>> a <- new("A", stuff=c(3.5, 0.1))
>>>> x <- numeric(10)
>>>> x[3:4] <- a
>>>
>>>
>>> The relevant stack trace is
>>>
>>> * frame #0: 0x000000010dded77a libR.dylib`R_has_methods(op=<unavailable>)
>>> + 74 at objects.c:1415
>>> frame #1: 0x000000010ddaabf4
>>> libR.dylib`Rf_DispatchOrEval(call=0x00007fcea36f68a8, op=0x00007fcea201a178,
>>> generic=0x000000010df0a185, args=<unavailable>, rho=0x00007fcea2053318,
>>> ans=0x00007fff51f60c48, dropmissing=<unavailable>, argsevald=1) + 404 at
>>> eval.c:3150
>>> frame #2: 0x000000010de4e658 libR.dylib`SubassignTypeFix [inlined]
>>> dispatch_asvector(x=<unavailable>, call=0x00007fcea36f68a8,
>>> rho=0x00007fcea2053318) + 295 at subassign.c:283
>>>
>>>
>>> The segfault is at objects.c:1415
>>>
>>> offset = PRIMOFFSET(op);
>>> if(offset > curMaxOffset || prim_methods[offset] == NO_METHODS
>>> || prim_methods[offset] == SUPPRESSED)
>>>
>>> where offset is negative and prim_methods[offset] fails.
>>>
>>> (lldb) p *op
>>> (SEXPREC) $8 = {
>>> sxpinfo = (type = 0, obj = 0, named = 2, gp = 0, mark = 1, debug = 0,
>>> trace = 0, spare = 0, gcgen = 1, gccls = 0)
>>> attrib = 0x00007fcea201a178
>>> gengc_next_node = 0x00007fcea21874e8
>>> gengc_prev_node = 0x00007fcea2019ff0
>>> u = {
>>> primsxp = (offset = -1576951432)
>>> symsxp = {
>>>
>>>
>>> 'op' is assigned from subassign.c:287, op = R_Primitive("as.vector")
>>>
>>> static Rboolean dispatch_asvector(SEXP *x, SEXP call, SEXP rho) {
>>> static SEXP op = NULL;
>>> SEXP args;
>>> Rboolean ans;
>>> if (op == NULL)
>>> op = R_Primitive("as.vector");
>>> PROTECT(args = list2(*x, mkString("any")));
>>> ans = DispatchOrEval(call, op, "as.vector", args, rho, x, 0, 1);
>>> UNPROTECT(1);
>>> return ans;
>>> }
>>>
>>> But as.vector is not a primitive, so gets R_NilValue. This is passed to
>>> DispatchOrEval, and then to R_has_methods.
>>>
>>> It seems like dispatch_asvector() was introduced by
>>>
>>> $ svn log -c69747
>>> ------------------------------------------------------------------------
>>> r69747 | lawrence | 2015-12-09 09:04:56 -0500 (Wed, 09 Dec 2015) | 3 lines
>>>
>>> subassignment of an S4 value into an atomic vector coerces the value
>>> with as.vector
>>>
>>> ------------------------------------------------------------------------
>>>
>>> So maybe Michael can tell us about his thinking here.
>>>
>>> Also, should R_has_methods be robust to R_NilValue? And R_NilValue
>>> explicitly zero it's data?
>>>
>>> Martin
>>>
>>>
>>>
>>>>
>>>> then the code is now valid and we still get the segfault on Mac.
>>>>
>>>> I didn't define as.vector.A in my original minimalist reproducible
>>>> code in order to keep it as simple as possible.
>>>>
>>>> H.
>>>>
>>>>
>>>>> We have seen such examples with something (more complicated
>>>>> than, but basically like)
>>>>>
>>>>> df <- data.frame(x=1:5, y=5:1, m=matrix(-pi*1:30, 5,6))
>>>>> M <- Matrix::Matrix(exp(0:3),2)
>>>>> df[1:2,1:2] <- M
>>>>>
>>>>> which actually calls `[<-`, and then `[<-.data.frame` and
>>>>> always works for me but does seg.fault (in the CRAN checks of
>>>>> package FastImputation (on 3 of the dozen platforms,
>>>>>
>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__cran.r-2Dproject.org_web_checks_check-5Fresults-5FFastImputation.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=zUahQYlBHRwNf6lPnSA1515Rm-iL5ffQI7hUcDW-JkE&e=
>>>>>
>>>>>
>>>>> one of them is
>>>>>
>>>>>
>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.r-2Dproject.org_nosvn_R.check_r-2Ddevel-2Dmacos-2Dx86-5F64-2Dclang_FastImputation-2D00check.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=Z7LkVlUzmdmhqxGNFl4LuMVxYwQQGHSV7KdpKCJu12k&e=
>>>>>
>>>>>
>>>>> I strongly suspect this is the same bug as yours, but for a case
>>>>> where the correct behavior is *not* giving an error.
>>>>>
>>>>> I have also written and shown Herve's example to the R-core team.
>>>>>
>>>>> Unfortunately, I have no platform where I can trigger the bug.
>>>>> Martin
>>>>>
>>>>>
>>>>>
>>>>> > Cheers,
>>>>> > Andrzej
>>>>>
>>>>>
>>>>>
>>>>> > On Wed, Mar 22, 2017 at 1:28 AM, Martin Morgan <
>>>>> > martin.morgan at roswellpark.org> wrote:
>>>>>
>>>>> >> On 03/21/2017 08:21 PM, Hervé Pagès wrote:
>>>>> >>
>>>>> >>> Hi Leonardo,
>>>>> >>>
>>>>> >>> Thanks for hunting down and isolating that bug! I tried to
>>>>> simplify
>>>>> >>> your code even more and was able to get a segfault with just:
>>>>> >>>
>>>>> >>> setClass("A", representation(stuff="numeric"))
>>>>> >>> x <- logical(10)
>>>>> >>> x[TRUE] <- new("A")
>>>>> >>>
>>>>> >>> I get the segfault about 50% of the time on a fresh R session
>>>>> on Mac.
>>>>> >>> I tried this with R 3.3.3 on Mavericks, and with R devel (r72372)
>>>>> >>> on El Capitan. I get the segfault on both.
>>>>> >>>
>>>>> >>> So it looks like a bug in the `[<-` primitive to me
>>>>> (subassignment).
>>>>> >>>
>>>>> >>
>>>>> >> Any insight from
>>>>> >>
>>>>> >> R -d valgrind -f herve.R
>>>>> >>
>>>>> >> where herve.R contains the code above?
>>>>> >>
>>>>> >> Martin
>>>>> >>
>>>>> >>
>>>>> >>
>>>>> >>> Cheers,
>>>>> >>> H.
>>>>> >>>
>>>>> >>> On 03/21/2017 03:06 PM, Leonardo Collado Torres wrote:
>>>>> >>>
>>>>> >>>> Hi bioc-devel,
>>>>> >>>>
>>>>> >>>> This is a story about a bug that took me a long time to
>>>>> trace. The
>>>>> >>>> behaviour was really weird, so I'm sharing the story in case
>>>>> this
>>>>> >>>> helps others in the future. I was originally writing it to
>>>>> request
>>>>> >>>> help, but then I was able to find the issue ^^. The story
>>>>> ends right
>>>>> >>>> now with code that will reproduce the problem with '$<-' from
>>>>> >>>> IRanges/S4Vectors.
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> During this Bioc cycle, frequently my package derfinder has
>>>>> failed to
>>>>> >>>> pass R CMD check in OSX. The error is always the same when it
>>>>> appears
>>>>> >>>> and sometimes it shows up in release, but not devel and
>>>>> viceversa.
>>>>> >>>> Right now (3/21/2017) it's visible in both
>>>>> >>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>>> >>>> ctor.org_checkResults_release_bioc-2DLATEST_derfinder_
>>>>> >>>> morelia-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfh
>>>>> >>>> Q&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>>> >>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=RS-lsygPtDdgWKAhjA2BcSLk
>>>>> >>>> Vy9RxxshXWAJaBZa_Yc&e=
>>>>> >>>>
>>>>> >>>> and
>>>>> >>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>>> >>>> ctor.org_checkResults_devel_bioc-2DLATEST_derfinder_toluca
>>>>> >>>> 2-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3X
>>>>> >>>> eAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>>> >>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=a_K-yK7w2LEV72lpHrpp0UoK
>>>>> >>>> Rru_7Aad74T5Uk0R-Fo&e=
>>>>> >>>> .
>>>>> >>>> The end of "test-all.Rout.fail" looks like this:
>>>>> >>>>
>>>>> >>>> Loading required package: foreach
>>>>> >>>> Loading required package: iterators
>>>>> >>>> Loading required package: locfit
>>>>> >>>> locfit 1.5-9.1 2013-03-22
>>>>> >>>> getSegments: segmenting
>>>>> >>>> getSegments: splitting
>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying potential segments
>>>>> >>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>>> >>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with cutoff(s)
>>>>> >>>> 16.3681899295041
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying candidate regions
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying potential segments
>>>>> >>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>>> >>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with cutoff(s)
>>>>> >>>> 19.7936614060235
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying candidate regions
>>>>> >>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>>> >>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>> >>>>
>>>>> >>>> *** caught segfault ***
>>>>> >>>> address 0x7f87d2f917e0, cause 'memory not mapped'
>>>>> >>>>
>>>>> >>>> Traceback:
>>>>> >>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>>> >>>> hostPackage <- environmentName(environment(smoothFun))
>>>>> >>>> requireNamespace(hostPackage) smoothed <-
>>>>> .runFunFormal(smoothFun,
>>>>> >>>> y = y, x = x, cluster = cluster, weights = weights,
>>>>> ...) if
>>>>> >>>> (any(!smoothed$smoothed)) {
>>>>> smoothed$fitted[!smoothed$smoothed]
>>>>> >>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>>> >>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]],
>>>>> dots[[3L]][[1L]],
>>>>> >>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL,
>>>>> cluster,
>>>>> >>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan
>>>>> = 0,
>>>>> >>>> verbose = TRUE) { if (is.null(dim(y)))
>>>>> y <-
>>>>> >>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>>> >>>> is.null(dim(weights))) weights <- matrix(weights,
>>>>> ncol =
>>>>> >>>> 1) if (is.null(x)) x <- seq(along =
>>>>> y) if
>>>>> >>>> (is.null(weights)) weights <- matrix(1, nrow =
>>>>> nrow(y),
>>>>> >>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster),
>>>>> cluster)
>>>>> >>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>>> >>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>>> >>>> if (verbose) if (i%%10000 == 0)
>>>>> >>>> cat(".") Index <- Indexes[[i]] if
>>>>> (clusterL[i]
>>>>> >>>>
>>>>> >>>>> = minNum & sum(rowSums(is.na(y[Index, , drop =
>>>>> >>>>>
>>>>> >>>> FALSE])) == 0) >= minNum) { nn <-
>>>>> >>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>>> >>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>>> >>>> j], weights = weights[Index, j]) fit <-
>>>>> >>>> locfit(y ˜ lp(pos, nn = nn, h = bpSpan),
>>>>> data =
>>>>> >>>> sdata, weights = weights, family = "gaussian",
>>>>> >>>> maxk = 10000) pp <- preplot(fit, where =
>>>>> "data", band
>>>>> >>>> = "local", newdata = data.frame(pos =
>>>>> x[Index]))
>>>>> >>>> y[Index, j] <- pp$trans(pp$fit) }
>>>>> >>>> } else { y[Index, ] <- NA
>>>>> >>>> smoothed[Index] <- FALSE } }
>>>>> >>>> return(list(fitted = y, smoothed = smoothed, smoother =
>>>>> "locfit"))
>>>>> >>>> }, verbose = TRUE, minNum = 1435)
>>>>> >>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>>> >>>> 3: FUN(...)
>>>>> >>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>>> >>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>>> >>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>>> >>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>>> >>>> 8: withCallingHandlers({ tryCatch({ FUN(...) },
>>>>> error =
>>>>> >>>> handle_error)}, warning = handle_warning)
>>>>> >>>> 9: FUN(X[[i]], ...)
>>>>> >>>> 10: lapply(X, FUN, ...)
>>>>> >>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>> = ddd,
>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>> >>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>> = ddd,
>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>> >>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>> clusterChunks,
>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>> >>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>> clusterChunks,
>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>> >>>> 15: .smootherFstats(fstats = fstats, position = position,
>>>>> weights =
>>>>> >>>> weights, smoothFunction = smoothFunction, ...)
>>>>> >>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose
>>>>> = TRUE,
>>>>> >>>> smooth = TRUE, minNum = 1435)
>>>>> >>>> 17: eval(exprs, env)
>>>>> >>>> 18: eval(exprs, env)
>>>>> >>>> 19: source_file(path, new.env(parent = env), chdir = TRUE)
>>>>> >>>> 20: force(code)
>>>>> >>>> 21: with_reporter(reporter = reporter, start_end_reporter =
>>>>> >>>> start_end_reporter, {
>>>>> lister$start_file(basename(path))
>>>>> >>>> source_file(path, new.env(parent = env), chdir = TRUE)
>>>>> >>>> end_context() })
>>>>> >>>> 22: FUN(X[[i]], ...)
>>>>> >>>> 23: lapply(paths, test_file, env = env, reporter =
>>>>> current_reporter,
>>>>> >>>> start_end_reporter = FALSE, load_helpers = FALSE)
>>>>> >>>> 24: force(code)
>>>>> >>>> 25: with_reporter(reporter = current_reporter, results <-
>>>>> >>>> lapply(paths, test_file, env = env, reporter =
>>>>> current_reporter,
>>>>> >>>> start_end_reporter = FALSE, load_helpers = FALSE))
>>>>> >>>> 26: test_files(paths, reporter = reporter, env = env, ...)
>>>>> >>>> 27: test_dir(test_path, reporter = reporter, env = env, filter =
>>>>> >>>> filter, ...)
>>>>> >>>> 28: with_top_env(env, { test_dir(test_path, reporter =
>>>>> reporter,
>>>>> >>>> env = env, filter = filter, ...)})
>>>>> >>>> 29: run_tests(package, test_path, filter, reporter, ...)
>>>>> >>>> 30: test_check("derfinder")
>>>>> >>>> An irrecoverable exception occurred. R is aborting now ...
>>>>> >>>>
>>>>> >>>> I was finally able to reproduce this error on my Mac OSX
>>>>> laptop after
>>>>> >>>> running R CMD build and R CMD check (same options as in Bioc)
>>>>> several
>>>>> >>>> times. It took me a while, but I figured out what's the exact
>>>>> code
>>>>> >>>> that's failing. It can be reproduced (noting that it won't
>>>>> always
>>>>> >>>> fail...) in OSX by running:
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> Here is the output from my laptop one time it actually failed:
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>>>
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>>
>>>>> >>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>>> chunksize=1e3,
>>>>> >>>>>
>>>>> >>>> + colsubset=NULL)
>>>>> >>>>
>>>>> >>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>> >>>>>
>>>>> >>>> 2017-03-21 16:37:39 findRegions: smoothing
>>>>> >>>>
>>>>> >>>> *** caught segfault ***
>>>>> >>>> address 0x7f958dbf2be0, cause 'memory not mapped'
>>>>> >>>>
>>>>> >>>> Traceback:
>>>>> >>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>>> >>>> hostPackage <- environmentName(environment(smoothFun))
>>>>> >>>> requireNamespace(hostPackage) smoothed <-
>>>>> .runFunFormal(smoothFun,
>>>>> >>>> y = y, x = x, cluster = cluster, weights = weights,
>>>>> ...) if
>>>>> >>>> (any(!smoothed$smoothed)) {
>>>>> smoothed$fitted[!smoothed$smoothed]
>>>>> >>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>>> >>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]],
>>>>> dots[[3L]][[1L]],
>>>>> >>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL,
>>>>> cluster,
>>>>> >>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan
>>>>> = 0,
>>>>> >>>> verbose = TRUE) { if (is.null(dim(y)))
>>>>> y <-
>>>>> >>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>>> >>>> is.null(dim(weights))) weights <- matrix(weights,
>>>>> ncol =
>>>>> >>>> 1) if (is.null(x)) x <- seq(along =
>>>>> y) if
>>>>> >>>> (is.null(weights)) weights <- matrix(1, nrow =
>>>>> nrow(y),
>>>>> >>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster),
>>>>> cluster)
>>>>> >>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>>> >>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>>> >>>> if (verbose) if (i%%10000 == 0)
>>>>> >>>> cat(".") Index <- Indexes[[i]] if
>>>>> (clusterL[i]
>>>>> >>>>
>>>>> >>>>> = minNum & sum(rowSums(is.na(y[Index, , drop =
>>>>> >>>>>
>>>>> >>>> FALSE])) == 0) >= minNum) { nn <-
>>>>> >>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>>> >>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>>> >>>> j], weights = weights[Index, j]) fit <-
>>>>> >>>> locfit(y ~ lp(pos, nn = nn, h = bpSpan),
>>>>> data =
>>>>> >>>> sdata, weights = weights, family = "gaussian",
>>>>> >>>> maxk = 10000) pp <- preplot(fit, where =
>>>>> "data", band
>>>>> >>>> = "local", newdata = data.frame(pos =
>>>>> x[Index]))
>>>>> >>>> y[Index, j] <- pp$trans(pp$fit) }
>>>>> >>>> } else { y[Index, ] <- NA
>>>>> >>>> smoothed[Index] <- FALSE } }
>>>>> >>>> return(list(fitted = y, smoothed = smoothed, smoother =
>>>>> "locfit"))
>>>>> >>>> }, verbose = TRUE, minNum = 1435)
>>>>> >>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>>> >>>> 3: FUN(...)
>>>>> >>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>>> >>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>>> >>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>>> >>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>>> >>>> 8: withCallingHandlers({ tryCatch({ FUN(...) },
>>>>> error =
>>>>> >>>> handle_error)}, warning = handle_warning)
>>>>> >>>> 9: FUN(X[[i]], ...)
>>>>> >>>> 10: lapply(X, FUN, ...)
>>>>> >>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>> = ddd,
>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>> >>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd
>>>>> = ddd,
>>>>> >>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>>> >>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>> clusterChunks,
>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>> >>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks,
>>>>> clusterChunks,
>>>>> >>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>>> >>>> ...), BPPARAM = BPPARAM)
>>>>> >>>> 15: .smootherFstats(fstats = fstats, position = position,
>>>>> weights =
>>>>> >>>> weights, smoothFunction = smoothFunction, ...)
>>>>> >>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose
>>>>> = TRUE,
>>>>> >>>> smooth = TRUE, minNum = 1435)
>>>>> >>>>
>>>>> >>>> Possible actions:
>>>>> >>>> 1: abort (with core dump, if enabled)
>>>>> >>>> 2: normal R exit
>>>>> >>>> 3: exit R without saving workspace
>>>>> >>>> 4: exit R saving workspace
>>>>> >>>>
>>>>> >>>> The traceback information ends at's
>>>>> bumphunter::loessByCluster().
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> I have successfully used the following code other times (see
>>>>> below)
>>>>> >>>> where I test the culprit line 100 times. By successfully, I
>>>>> mean that
>>>>> >>>> the code ran without problems... so it was unsuccessful at
>>>>> reproducing
>>>>> >>>> the problem.
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>>
>>>>> >>>> for(i in 1:100) {
>>>>> >>>> print(i)
>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>> >>>> }
>>>>> >>>> options(width = 120)
>>>>> >>>> devtools::session_info()
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> I had several R processes open the one time it did fail, but
>>>>> well,
>>>>> >>>> I've had multiple of them open the times that the code didn't
>>>>> fail. So
>>>>> >>>> having multiple R processes doesn't seem to be an issue.
>>>>> >>>>
>>>>> >>>> The line that triggers the segfault is used simply to test that
>>>>> >>>> passing the argument 'minNum' to bumphunter::loessByCluster()
>>>>> via
>>>>> >>>> '...' works. It's not a relevant test for derfinder and I was
>>>>> tempted
>>>>> >>>> to remove it, although before tracing the bug I talked with
>>>>> Valerie
>>>>> >>>> about not removing it. With the upcoming Bioconductor release I
>>>>> >>>> decided to finally trace the line that triggers the segfault.
>>>>> At this
>>>>> >>>> point I was feeling lost...
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> Running the following code seems to trigger the segfault more
>>>>> often (I
>>>>> >>>> got it like 4 times in a row):
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE)
>>>>> >>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>>> >>>> bumphunter::runmedByCluster)
>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>> >>>>
>>>>> >>>> But then I can still run the same code without problems on a
>>>>> for loop
>>>>> >>>> for 100 times:
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>>
>>>>> >>>> for(i in 1:100) {
>>>>> >>>> print(i)
>>>>> >>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE)
>>>>> >>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>>> >>>> bumphunter::runmedByCluster)
>>>>> >>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> >>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>> >>>> }
>>>>> >>>> options(width = 120)
>>>>> >>>> devtools::session_info()
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> I next thought of going through findRegions() to produce simple
>>>>> >>>> objects that could reproduce the error. I had in mine sharing
>>>>> these
>>>>> >>>> objects so it would be easier for others to help me figure
>>>>> out what
>>>>> >>>> was failing. It turns out that this code segfaulted reliably
>>>>> (all the
>>>>> >>>> times I tested it at least):
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> library('derfinder')
>>>>> >>>> library('BiocParallel')
>>>>> >>>> library('IRanges')
>>>>> >>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> >>>> chunksize=1e3,
>>>>> >>>> colsubset=NULL)
>>>>> >>>> fstats <- genomeFstats
>>>>> >>>> position <- prep$position
>>>>> >>>> weights <- NULL
>>>>> >>>> cluster <- derfinder:::.clusterMakerRle(position, 300L)
>>>>> >>>> cluster
>>>>> >>>> BPPARAM <- SerialParam()
>>>>> >>>> iChunks <- rep(1, length(cluster))
>>>>> >>>>
>>>>> >>>> fstatsChunks <- split(fstats, iChunks)
>>>>> >>>> posChunks <- split(which(position), iChunks)
>>>>> >>>> clusterChunks <- split(cluster, iChunks)
>>>>> >>>> weightChunks <- vector('list', length = length(unique(iChunks)))
>>>>> >>>>
>>>>> >>>> res <- bpmapply(bumphunter::loessByCluster, fstatsChunks,
>>>>> posChunks,
>>>>> >>>> clusterChunks, weightChunks, MoreArgs = list(minNum = 1435),
>>>>> >>>> BPPARAM = BPPARAM, SIMPLIFY = FALSE)
>>>>> >>>>
>>>>> >>>> y <- fstatsChunks[[1]]
>>>>> >>>> smoothed <- res[[1]]
>>>>> >>>>
>>>>> >>>> ## This segfaults:
>>>>> >>>> if(any(!smoothed$smoothed)) {
>>>>> >>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>> >>>> }
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> The objects on the line that fail are a list and an Rle:
>>>>> >>>>
>>>>> >>>> y
>>>>> >>>>>
>>>>> >>>> numeric-Rle of length 1434 with 358 runs
>>>>> >>>> Lengths: 1 5
>>>>> >>>> ... 1
>>>>> >>>> Values : 5.109484425367 3.85228949953674 ...
>>>>> >>>> 3.99765511645983
>>>>> >>>>
>>>>> >>>>> lapply(smoothed, head)
>>>>> >>>>>
>>>>> >>>> $fitted
>>>>> >>>> [,1]
>>>>> >>>> [1,] NA
>>>>> >>>> [2,] NA
>>>>> >>>> [3,] NA
>>>>> >>>> [4,] NA
>>>>> >>>> [5,] NA
>>>>> >>>> [6,] NA
>>>>> >>>>
>>>>> >>>> $smoothed
>>>>> >>>> [1] FALSE FALSE FALSE FALSE FALSE FALSE
>>>>> >>>>
>>>>> >>>> $smoother
>>>>> >>>> [1] "loess"
>>>>> >>>>
>>>>> >>>>> table(!smoothed$smoothed)
>>>>> >>>>>
>>>>> >>>>
>>>>> >>>> TRUE
>>>>> >>>> 1434
>>>>> >>>>
>>>>> >>>>> y[!smoothed$smoothed]
>>>>> >>>>>
>>>>> >>>> numeric-Rle of length 1434 with 358 runs
>>>>> >>>> Lengths: 1 5
>>>>> >>>> ... 1
>>>>> >>>> Values : 5.109484425367 3.85228949953674 ...
>>>>> >>>> 3.99765511645983
>>>>> >>>>
>>>>> >>>> So in my derfinder code I was assigning an Rle to a matrix,
>>>>> and that
>>>>> >>>> was the segfault. I have no idea why this doesn't always fail
>>>>> on OSX
>>>>> >>>> and why it never failed on Linux or Windows.
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> This is the super simplified IRanges code that fails:
>>>>> >>>>
>>>>> >>>> library('IRanges')
>>>>> >>>> y <- Rle(runif(10, 1, 1))
>>>>> >>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>> >>>> 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>> >>>> sessionInfo()
>>>>> >>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>> >>>>
>>>>> >>>> ## Segfault on OSX
>>>>> >>>>
>>>>> >>>> library('IRanges')
>>>>> >>>>> y <- Rle(runif(10, 1, 1))
>>>>> >>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>> >>>>>
>>>>> >>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>> >>>>
>>>>> >>>>>
>>>>> >>>>> sessionInfo()
>>>>> >>>>>
>>>>> >>>> R Under development (unstable) (2016-10-26 r71594)
>>>>> >>>> Platform: x86_64-apple-darwin13.4.0 (64-bit)
>>>>> >>>> Running under: macOS Sierra 10.12.3
>>>>> >>>>
>>>>> >>>> locale:
>>>>> >>>> [1]
>>>>> en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>>>>> >>>>
>>>>> >>>> attached base packages:
>>>>> >>>> [1] stats4 parallel stats graphics grDevices utils
>>>>> >>>> datasets methods base
>>>>> >>>>
>>>>> >>>> other attached packages:
>>>>> >>>> [1] IRanges_2.9.19 S4Vectors_0.13.15 BiocGenerics_0.21.3
>>>>> >>>>
>>>>> >>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>> >>>>>
>>>>> >>>>
>>>>> >>>> *** caught segfault ***
>>>>> >>>> address 0x7fcdc31dffe0, cause 'memory not mapped'
>>>>> >>>>
>>>>> >>>> Possible actions:
>>>>> >>>> 1: abort (with core dump, if enabled)
>>>>> >>>> 2: normal R exit
>>>>> >>>> 3: exit R without saving workspace
>>>>> >>>> 4: exit R saving workspace
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> ## No problems on Linux
>>>>> >>>>
>>>>> >>>> library('IRanges')
>>>>> >>>>> y <- Rle(runif(10, 1, 1))
>>>>> >>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>> >>>>>
>>>>> >>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>> >>>>
>>>>> >>>>>
>>>>> >>>>> sessionInfo()
>>>>> >>>>>
>>>>> >>>> R version 3.3.1 Patched (2016-09-30 r71426)
>>>>> >>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>>> >>>> Running under: Red Hat Enterprise Linux Server release 6.6
>>>>> (Santiago)
>>>>> >>>>
>>>>> >>>> locale:
>>>>> >>>> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
>>>>> >>>> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
>>>>> >>>> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
>>>>> >>>> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
>>>>> >>>> [9] LC_ADDRESS=C LC_TELEPHONE=C
>>>>> >>>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>>>> >>>>
>>>>> >>>> attached base packages:
>>>>> >>>> [1] stats4 parallel stats graphics grDevices
>>>>> datasets utils
>>>>> >>>> [8] methods base
>>>>> >>>>
>>>>> >>>> other attached packages:
>>>>> >>>> [1] IRanges_2.8.2 S4Vectors_0.12.2 BiocGenerics_0.20.0
>>>>> >>>> [4] colorout_1.1-2
>>>>> >>>>
>>>>> >>>> loaded via a namespace (and not attached):
>>>>> >>>> [1] tools_3.3.1
>>>>> >>>>
>>>>> >>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>> >>>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> Best,
>>>>> >>>> Leo
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> The session information for my first tests is below:
>>>>> >>>>
>>>>> >>>> devtools::session_info()
>>>>> >>>>>
>>>>> >>>> Session info
>>>>> >>>> ------------------------------------------------------------
>>>>> >>>> -----------------------------------------------
>>>>> >>>>
>>>>> >>>> setting value
>>>>> >>>> version R Under development (unstable) (2016-10-26 r71594)
>>>>> >>>> system x86_64, darwin13.4.0
>>>>> >>>> ui X11
>>>>> >>>> language (EN)
>>>>> >>>> collate en_US.UTF-8
>>>>> >>>> tz America/New_York
>>>>> >>>> date 2017-03-21
>>>>> >>>>
>>>>> >>>> Packages
>>>>> >>>> ------------------------------------------------------------
>>>>> >>>> ---------------------------------------------------
>>>>> >>>>
>>>>> >>>> package * version date source
>>>>> >>>> acepack 1.4.1 2016-10-29 CRAN (R 3.4.0)
>>>>> >>>> AnnotationDbi 1.37.4 2017-03-10 Bioconductor
>>>>> >>>> assertthat 0.1 2013-12-06 CRAN (R 3.4.0)
>>>>> >>>> backports 1.0.5 2017-01-18 CRAN (R 3.4.0)
>>>>> >>>> base64enc 0.1-3 2015-07-28 CRAN (R 3.4.0)
>>>>> >>>> Biobase 2.35.1 2017-02-23 Bioconductor
>>>>> >>>> BiocGenerics * 0.21.3 2017-01-12 Bioconductor
>>>>> >>>> BiocParallel 1.9.5 2017-01-24 Bioconductor
>>>>> >>>> biomaRt 2.31.4 2017-01-13 Bioconductor
>>>>> >>>> Biostrings 2.43.5 2017-03-19 cran (@2.43.5)
>>>>> >>>> bitops 1.0-6 2013-08-17 CRAN (R 3.4.0)
>>>>> >>>> BSgenome 1.43.7 2017-02-24 Bioconductor
>>>>> >>>> bumphunter * 1.15.0 2016-10-23 Bioconductor
>>>>> >>>> checkmate 1.8.2 2016-11-02 CRAN (R 3.4.0)
>>>>> >>>> cluster 2.0.6 2017-03-16 CRAN (R 3.4.0)
>>>>> >>>> codetools 0.2-15 2016-10-05 CRAN (R 3.4.0)
>>>>> >>>> colorout * 1.1-2 2016-11-15 Github
>>>>> >>>> (jalvesaq/colorout at 6d84420)
>>>>> >>>> colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
>>>>> >>>> crayon 1.3.2 2016-06-28 CRAN (R 3.4.0)
>>>>> >>>> data.table 1.10.4 2017-02-01 CRAN (R 3.4.0)
>>>>> >>>> DBI 0.6 2017-03-09 CRAN (R 3.4.0)
>>>>> >>>> DelayedArray 0.1.7 2017-02-17 Bioconductor
>>>>> >>>> derfinder * 1.9.10 2017-03-17 cran (@1.9.10)
>>>>> >>>> derfinderHelper 1.9.4 2017-03-07 Bioconductor
>>>>> >>>> devtools 1.12.0 2016-12-05 CRAN (R 3.4.0)
>>>>> >>>> digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
>>>>> >>>> doRNG 1.6 2014-03-07 CRAN (R 3.4.0)
>>>>> >>>> foreach * 1.4.3 2015-10-13 CRAN (R 3.4.0)
>>>>> >>>> foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
>>>>> >>>> Formula 1.2-1 2015-04-07 CRAN (R 3.4.0)
>>>>> >>>> GenomeInfoDb * 1.11.9 2017-02-08 Bioconductor
>>>>> >>>> GenomeInfoDbData 0.99.0 2017-02-14 Bioconductor
>>>>> >>>> GenomicAlignments 1.11.12 2017-03-16 cran (@1.11.12)
>>>>> >>>> GenomicFeatures 1.27.10 2017-03-16 cran (@1.27.10)
>>>>> >>>> GenomicFiles 1.11.4 2017-03-10 Bioconductor
>>>>> >>>> GenomicRanges * 1.27.23 2017-02-25 Bioconductor
>>>>> >>>> ggplot2 2.2.1 2016-12-30 CRAN (R 3.4.0)
>>>>> >>>> gridExtra 2.2.1 2016-02-29 CRAN (R 3.4.0)
>>>>> >>>> gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
>>>>> >>>> Hmisc 4.0-2 2016-12-31 CRAN (R 3.4.0)
>>>>> >>>> htmlTable 1.9 2017-01-26 CRAN (R 3.4.0)
>>>>> >>>> htmltools 0.3.5 2016-03-21 CRAN (R 3.4.0)
>>>>> >>>> htmlwidgets 0.8 2016-11-09 CRAN (R 3.4.0)
>>>>> >>>> IRanges * 2.9.19 2017-03-15 cran (@2.9.19)
>>>>> >>>> iterators * 1.0.8 2015-10-13 CRAN (R 3.4.0)
>>>>> >>>> knitr 1.15.1 2016-11-22 CRAN (R 3.4.0)
>>>>> >>>> lattice 0.20-34 2016-09-06 CRAN (R 3.4.0)
>>>>> >>>> latticeExtra 0.6-28 2016-02-09 CRAN (R 3.4.0)
>>>>> >>>> lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
>>>>> >>>> locfit * 1.5-9.1 2013-04-20 CRAN (R 3.4.0)
>>>>> >>>> magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
>>>>> >>>> Matrix 1.2-8 2017-01-20 CRAN (R 3.4.0)
>>>>> >>>> matrixStats 0.51.0 2016-10-09 CRAN (R 3.4.0)
>>>>> >>>> memoise 1.0.0 2016-01-29 CRAN (R 3.4.0)
>>>>> >>>> munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
>>>>> >>>> nnet 7.3-12 2016-02-02 CRAN (R 3.4.0)
>>>>> >>>> pkgmaker 0.22 2014-05-14 CRAN (R 3.4.0)
>>>>> >>>> plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
>>>>> >>>> qvalue 2.7.0 2016-10-23 Bioconductor
>>>>> >>>> R6 2.2.0 2016-10-05 CRAN (R 3.4.0)
>>>>> >>>> RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.4.0)
>>>>> >>>> Rcpp 0.12.10 2017-03-19 CRAN (R 3.4.0)
>>>>> >>>> RCurl 1.95-4.8 2016-03-01 CRAN (R 3.4.0)
>>>>> >>>> registry 0.3 2015-07-08 CRAN (R 3.4.0)
>>>>> >>>> reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
>>>>> >>>> rngtools 1.2.4 2014-03-06 CRAN (R 3.4.0)
>>>>> >>>> rpart 4.1-10 2015-06-29 CRAN (R 3.4.0)
>>>>> >>>> Rsamtools 1.27.13 2017-03-14 cran (@1.27.13)
>>>>> >>>> RSQLite 1.1-2 2017-01-08 CRAN (R 3.4.0)
>>>>> >>>> rtracklayer 1.35.9 2017-03-19 cran (@1.35.9)
>>>>> >>>> S4Vectors * 0.13.15 2017-02-14 cran (@0.13.15)
>>>>> >>>> scales 0.4.1 2016-11-09 CRAN (R 3.4.0)
>>>>> >>>> stringi 1.1.2 2016-10-01 CRAN (R 3.4.0)
>>>>> >>>> stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
>>>>> >>>> SummarizedExperiment 1.5.7 2017-02-23 Bioconductor
>>>>> >>>> survival 2.41-2 2017-03-16 CRAN (R 3.4.0)
>>>>> >>>> testthat * 1.0.2 2016-04-23 CRAN (R 3.4.0)
>>>>> >>>> tibble 1.2 2016-08-26 CRAN (R 3.4.0)
>>>>> >>>> VariantAnnotation 1.21.17 2017-02-12 Bioconductor
>>>>> >>>> withr 1.0.2 2016-06-20 CRAN (R 3.4.0)
>>>>> >>>> XML 3.98-1.5 2016-11-10 CRAN (R 3.4.0)
>>>>> >>>> xtable 1.8-2 2016-02-05 CRAN (R 3.4.0)
>>>>> >>>> XVector 0.15.2 2017-02-02 Bioconductor
>>>>> >>>> zlibbioc 1.21.0 2016-10-23 Bioconductor
>>>>> >>>>
>>>>> >>>> _______________________________________________
>>>>> >>>> Bioc-devel at r-project.org mailing list
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>>>>> >>>> oN3XSBk6ppmeRZhdLoB8VseYM_Byk&e=
>>>>> >>>>
>>>>> >>>>
>>>>> >>>>
>>>>> >>>
>>>>> >>
>>>>> >> This email message may contain legally privileged
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>>>>>
>>>>> >>
>>>>>
>>>>> > [[alternative HTML version deleted]]
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--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
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