I like the idea of having multiple, domain-specific cores. Those could also
serve as a vehicle for high-level documentation, including the workflows
but also more "cheat-sheet" and/or cookbook-style documentation. Rafa has
brought this up on the phone calls.
On Tue, May 12, 2015 at 4:10 PM, Herv? Pag?s <hpages at fredhutch.org> wrote:
SummarizedExperiment was just an example. I agree it can be a
little challenging for end users to know where to find a particular
functionality but I'm not sure about using "meta" packages to address
that. At least I feel we should probably avoid creating new "meta"
packages out of the blue, with arbitrary limits and possibly endless
discussions about what exactly goes in them. Also I don't think there
is a single "core" but rather several domain-specific cores.
What about using the existing workflow packages instead?
A workflow package (like the variants package here
http://bioconductor.org/help/workflows/variants/)
covers a specific domain and loading it should load the "core"
for that domain. Plus the user gets a great vignette as a bonus
to get started so it's not just an empty shell.
There are probably some shortcomings with workflow packages
that would need to be addressed before they can serve as
convenient "meta" packages though e.g. they're treated too
differently from other BioC packages (e.g. they're not available
via biocLite() and don't show up under the biocViews tree here
http://bioconductor.org/packages/release/BiocViews.html).
Nothing that seems impossible to address though...
H.
On 05/12/2015 03:22 PM, Michael Lawrence wrote:
It's more general than SummarizedExperiment. I think people would
appreciate a simple way to load the core, without having to remember,
for example, that VCF reading is in VariantAnnotation.
On Mon, May 11, 2015 at 9:51 PM, Herv? Pag?s <hpages at fredhutch.org
<mailto:hpages at fredhutch.org>> wrote:
Hi Michael,
On 05/11/2015 05:35 PM, Michael Lawrence wrote:
Splitting stuff into different packages is good for modularity,
but
tough on the mind of the user. What about having some sort of
"meta"
package that simply loads the core infrastructure packages?
something simple like "Genomics" or "GenomicsCore".
Don't know if we need this. For example, for all the
SummarizedExperiment use cases I ran into, the end-user generally
only needs to load the corresponding high-level package (DESeq2,
VariantAnnotation, minfi, GenomicAlignments, etc...) and that takes
care of loading all the low-level infrastructure packages.
H.
On Mon, May 11, 2015 at 5:10 PM, Herv? Pag?s
<hpages at fredhutch.org <mailto:hpages at fredhutch.org>
<mailto:hpages at fredhutch.org <mailto:hpages at fredhutch.org>>>
wrote:
Hi Tim,
The SummarizedExperiment class is being replaced with the
RangedSummarizedExperiment class from the new
SummarizedExperiment
package. This is a work-in-progress and the name and
representation of the RangedSummarizedExperiment class are
not
finalized yet. The main goal for now is to move all the
SummarizedExperiment stuff from GenomicRanges to its own
package.
Anyway, metadata() is the replacement for exptData() on
RangedSummarizedExperiment objects. It's on my list to add
an exptData method for backward compatibility.
Cheers,
H.
On 05/11/2015 04:37 PM, Tim Triche, Jr. wrote:
who determined that breaking this would be a good
R> ?SummarizedExperiment
Help on topic 'SummarizedExperiment' was found in the
following
packages:
Package Library
GenomicRanges
/home/tim/R/x86_64-pc-linux-gnu-library/3.2
SummarizedExperiment
/home/tim/R/x86_64-pc-linux-gnu-library/3.2
R> nrows <- 200; ncols <- 6
R> counts <- matrix(runif(nrows * ncols, 1,
1e4), nrows)
R> rowRanges <- GRanges(rep(c("chr1", "chr2"),
c(50, 150)),
+ IRanges(floor(runif(200,
1e5, 1e6)),
width=100),
+ strand=sample(c("+", "-"),
200, TRUE))
R> colData <- DataFrame(Treatment=rep(c("ChIP",
"Input"), 3),
+ row.names=LETTERS[1:6])
R> sset <-
SummarizedExperiment(assays=SimpleList(counts=counts),
+ rowRanges=rowRanges,
colData=colData)
R> sset
class: RangedSummarizedExperiment
dim: 200 6
metadata(0):
assays(1): counts
rownames: NULL
rowRanges metadata column names(0):
colnames(6): A B ... E F
colData names(1): Treatment
R> assayNames(sset)
[1] "counts"
R> assays(sset) <- endoapply(assays(sset),
R> head(assay(sset))
A B C D E F
[1,] 6.89 8.81 9.46 9.20 8.88 9.44
[2,] 5.07 9.70 4.08 7.47 8.91 5.64
[3,] 9.88 9.84 8.95 9.07 9.86 9.06
[4,] 9.89 8.88 8.92 8.05 8.46 9.51
[5,] 9.75 8.48 4.73 9.86 8.43 9.86
[6,] 9.29 9.13 9.80 9.77 9.50 8.40
R> exptData(sset)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function
'exptData'
for signature
'"RangedSummarizedExperiment"'
It's one of those things that's a handy place to put
data when
you need to
carry it around for the same set of people/subjects but
don't
have a handy
multidimensional container for it. So it's a bit of a
drag that
it now
breaks...
Bonus:
R> ?"exptData,SummarizedExperiment-method"
SummarizedExperiment-class package:GenomicRanges
Documentation
SummarizedExperiment instances
Description:
The SummarizedExperiment class is a matrix-like
container
where
rows represent ranges of interest (as a 'GRanges
or
GRangesList-class') and columns represent
samples (with
sample
data summarized as a 'DataFrame-class'). A
'SummarizedExperiment'
contains one or more assays, each represented
matrix-like
object of numeric or other mode.
R> sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 15.04
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8
[5] LC_MONETARY=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] grid stats4 parallel stats graphics
grDevices datasets
[8] utils methods base
other attached packages:
[1] disintegrator_0.4.8 vegan_2.2-1
[3] permute_0.8-3 CCAGFA_1.0.4
[5] FEM_2.3.0 org.Hs.eg.db_3.1.2
[7] igraph_0.7.1 corrplot_0.73
[9] marray_1.47.0 AnnotationDbi_1.31.6
[11] MotifDb_1.10.0 PWMEnrich_4.5.0
[13] SCAN.UPC_2.10.0 sva_3.15.0
[15] genefilter_1.51.0 mgcv_1.8-6
[17] nlme_3.1-120 affyio_1.37.0
[19] affy_1.47.0 oligo_1.33.0
[21] oligoClasses_1.31.0 SRAdb_1.23.0
[23] RCurl_1.95-4.6 bitops_1.0-6
[25] graph_1.47.0 quadprog_1.5-5
[27] mclust_5.0.1
ConsensusClusterPlus_1.23.0
[29] simulatorZ_1.5.1 CoxBoost_1.4
[31] prodlim_1.5.1 rsig_1.0
[33] survival_2.38-1 DMRcate_1.5.42
[35] matrixStats_0.14.0 rtracklayer_1.29.5
[37] Matrix_1.2-0 qvalue_2.1.0
[39] impute_1.43.0 DMRcatedata_1.5.0
[41] minfi_1.15.3 bumphunter_1.8.0
[43] locfit_1.5-9.1 iterators_1.0.7
[45] foreach_1.4.2 Biostrings_2.37.2
[47] XVector_0.9.1
SummarizedExperiment_0.1.1
[49] GenomicRanges_1.21.9 GenomeInfoDb_1.5.2
[51] IRanges_2.3.8 S4Vectors_0.7.2
[53] lattice_0.20-31 limma_3.25.3
[55] ks_1.9.4 rgl_0.95.1247
[57] mvtnorm_1.0-2 misc3d_0.8-4
[59] KernSmooth_2.23-14 dplyr_0.4.1
[61] GEOmetadb_1.29.0 RSQLite_1.0.0
[63] DBI_0.3.1 GEOquery_2.35.4
[65] Biobase_2.29.1 BiocGenerics_0.15.0
[67] bigrquery_0.1.0.9000 BiocInstaller_1.19.5
[69] magrittr_1.5 gtools_3.4.2
loaded via a namespace (and not attached):
[1] Hmisc_3.16-0 plyr_1.8.2
splines_3.2.0
[4] BiocParallel_1.3.9 ggplot2_1.0.1
digest_0.6.8
[7] SuppDists_1.1-9.1 gdata_2.16.1
GMD_0.3.3
[10] checkmate_1.5.2 BBmisc_1.9
cluster_2.0.1
[13] annotate_1.47.0 siggenes_1.43.0
colorspace_1.2-6
[16] tcltk_3.2.0 registry_0.2
gtable_0.1.2
[19] zlibbioc_1.15.0 RGCCA_2.0
evd_2.3-0
[22] scales_0.2.4 futile.options_1.0.0
pheatmap_1.0.2
[25] rngtools_1.2.4 Rcpp_0.11.6
xtable_1.7-4
[28] foreign_0.8-63 bit_1.1-12
preprocessCore_1.31.0
[31] Formula_1.2-1 lava_1.4.0
glmnet_2.0-2
[34] httr_0.6.1 gplots_2.17.0
RColorBrewer_1.1-2
[37] acepack_1.3-3.3 ff_2.2-13
reshape_0.8.5
[40] XML_3.98-1.1 nnet_7.3-9
reshape2_1.4.1
[43] munsell_0.4.2 tools_3.2.0
stringr_1.0.0
[46] bootstrap_2015.2 beanplot_1.2
caTools_1.17.1
[49] doRNG_1.6 nor1mix_1.2-0
biomaRt_2.25.1
[52] stringi_0.4-1 superpc_1.09
futile.logger_1.4.1
[55] GenomicFeatures_1.21.2 survcomp_1.19.0
gbm_2.1.1
[58] survivalROC_1.0.3 multtest_2.25.0
R6_2.0.1
[61] latticeExtra_0.6-26 gridExtra_0.9.1
affxparser_1.41.2
[64] codetools_0.2-11 lambda.r_1.1.7
seqLogo_1.35.0
[67] MASS_7.3-40 assertthat_0.1
proto_0.3-10
[70] pkgmaker_0.22 GenomicAlignments_1.5.8
Rsamtools_1.21.4
[73] mixOmics_5.0-4 rpart_4.1-9
base64_1.1
[76] illuminaio_0.11.0 rmeta_2.16
Statistics is the grammar of science.
Karl Pearson
<http://en.wikipedia.org/wiki/The_Grammar_of_Science>
[[alternative HTML version deleted]]