Closed blue-moon22 closed 8 years ago
Thanks for the report. I will need some time to investigate - hopefully tonight.
You need to update your annotation parameters:
> p <- setAnnotationParams(inputs = c("Arabidopsis thaliana", "TAIR locus model"))
Using species Arabidopsis thaliana genes (TAIR10 (2010-09-TAIR10))
Using feature type TAIR locus model ID(s)
Connecting to Biomart...
> makeGoSet(dunkley2006, p)
MSnSet (storageMode: lockedEnvironment)
assayData: 689 features, 0 samples
element names: exprs
protocolData: none
phenoData: none
featureData
featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
fvarLabels: assigned evidence ... markers (8 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation:
- - - Processing information - - -
Constructed GO set using cellular_component namespace: Sun Mar 27 19:22:57 2016
MSnbase version: 1.19.16
And by the way, you don't need to set the parameters if you pass them to the function. Either do
makeGoSet(dunkley2006, p)
or
setAnnotationParams(p)
dunkleygoset <- makeGoSet(dunkley2006)
I'm closing this issue now - feel free to reopen it if you still have errors.
Thanks! Though I now get an error with knntlOptimization
> p <- setAnnotationParams(inputs = c("Arabidopsis thaliana", "TAIR locus model"))
Using species Arabidopsis thaliana genes (TAIR10 (2010-09-TAIR10))
Using feature type TAIR locus model ID(s)
Connecting to Biomart...
> dunkleygoset <- makeGoSet(dunkley2006, p)
> m <- unique(fData(dunkley2006)$markers)
> m <- m[m != "unknown"]
> th <- thetas(length(m), length.out=4)
Weigths:
(0, 0.333333333333333, 0.666666666666667, 1)
> set.seed(1)
> i <- sample(nrow(th), 12)
> topt <- knntlOptimisation(dunkley2006, dunkleygoset, th = th[i,], k = c(3,3), fcol = "markers", times = 5)
Note: vector will be ordered according to classes: ER lumen ER membrane Golgi Mitochondrion Plastid PM Ribosome TGN vacuole (as names are not explicitly defined)
**Error in unserialize(socklist[[n]]) : error reading from connection
Error: failed to stop ‘SOCKcluster’ cluster: error writing to connection**
> sessionInfo()
R version 3.2.4 (2016-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] pRolocdata_1.8.0 pRoloc_1.10.1 MLInterfaces_1.50.0 cluster_2.0.3 annotate_1.48.0
[6] XML_3.98-1.4 AnnotationDbi_1.32.3 IRanges_2.4.8 S4Vectors_0.8.11 MSnbase_1.18.1
[11] ProtGenerics_1.2.1 BiocParallel_1.4.3 mzR_2.4.1 Rcpp_0.12.3 Biobase_2.30.0
[16] BiocGenerics_0.16.1
loaded via a namespace (and not attached):
[1] nlme_3.1-126 pbkrtest_0.4-6 bitops_1.0-6 doParallel_1.0.10
[5] RColorBrewer_1.1-2 threejs_0.2.1 prabclus_2.2-6 ggvis_0.4.2
[9] tools_3.2.4 R6_2.1.2 affyio_1.40.0 rpart_4.1-10
[13] mgcv_1.8-12 DBI_0.3.1 colorspace_1.2-6 trimcluster_0.1-2
[17] nnet_7.3-12 gbm_2.1.1 preprocessCore_1.32.0 quantreg_5.21
[21] SparseM_1.7 diptest_0.75-7 scales_0.4.0 sfsmisc_1.1-0
[25] DEoptimR_1.0-4 mvtnorm_1.0-5 robustbase_0.92-5 randomForest_4.6-12
[29] genefilter_1.52.1 affy_1.48.0 proxy_0.4-15 stringr_1.0.0
[33] digest_0.6.9 minqa_1.2.4 base64enc_0.1-3 htmltools_0.3
[37] lme4_1.1-11 rda_1.0.2-2 limma_3.26.8 htmlwidgets_0.6
[41] RSQLite_1.0.0 impute_1.44.0 BiocInstaller_1.20.1 FNN_1.1
[45] shiny_0.13.1 hwriter_1.3.2 mzID_1.8.0 mclust_5.1
[49] gtools_3.5.0 car_2.1-1 dplyr_0.4.3 RCurl_1.95-4.8
[53] magrittr_1.5 modeltools_0.2-21 Matrix_1.2-4 futile.logger_1.4.1
[57] MALDIquant_1.14 munsell_0.4.3 vsn_3.38.0 stringi_1.0-1
[61] MASS_7.3-45 zlibbioc_1.16.0 flexmix_2.3-13 plyr_1.8.3
[65] grid_3.2.4 pls_2.5-0 gdata_2.17.0 lattice_0.20-33
[69] splines_3.2.4 knitr_1.12.3 fpc_2.1-10 lpSolve_5.6.13
[73] reshape2_1.4.1 codetools_0.2-14 biomaRt_2.26.1 futile.options_1.0.0
[77] pcaMethods_1.60.0 lambda.r_1.1.7 mlbench_2.1-1 nloptr_1.0.4
[81] httpuv_1.3.3 foreach_1.4.3 MatrixModels_0.4-1 gtable_0.2.0
[85] kernlab_0.9-23 assertthat_0.1 ggplot2_2.1.0 mime_0.4
[89] xtable_1.8-2 e1071_1.6-7 class_7.3-14 survival_2.38-3
[93] snow_0.4-1 iterators_1.0.8 rgl_0.95.1441 caret_6.0-64
[97] sampling_2.7
That's a problem with parallel processing, which is Windows-specific and I won't be able to debug right now. You can do the following to proceed serially:
p <- SerialParam()
knntlOptimisation(dunkley2006, dunkleygoset, th = th[i,], k = c(3,3), fcol = "markers", times = 5)
You could also use molerat
and parallelise over 16 cores by setting
p <- MulticoreParam(16L)
The parameters should actually be
> p <- setAnnotationParams(inputs = c("Arabidopsis thaliana", "TAIR locus ID"))
Using species Arabidopsis thaliana genes (TAIR10 (2010-09-TAIR10))
Using feature type TAIR locus ID(s)
Connecting to Biomart...
> p
Object of class "AnnotationParams"
Using the 'plants_mart' BioMart database
Using the 'athaliana_eg_gene' dataset
Using 'tair_locus' as filter
Created on Fri Apr 1 10:00:21 2016
> makeGoSet(dunkley2006[1:10, ], p)
MSnSet (storageMode: lockedEnvironment)
assayData: 10 features, 21 samples
element names: exprs
protocolData: none
phenoData: none
featureData
featureNames: AT1G09210 AT1G21750 ... AT1G07810 (10 total)
fvarLabels: assigned evidence ... markers (8 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation:
- - - Processing information - - -
Constructed GO set using cellular_component namespace: Fri Apr 1 10:00:25 2016
MSnbase version: 1.19.17