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This is the git repository matching the Bioconductor package xcms: LC/MS and GC/MS Data Analysis
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Getting errors while running retention time correction #345

Open raagbtitl opened 5 years ago

raagbtitl commented 5 years ago

I am working with mzXML files generated frpm the the ACQUITY UPLC (XEVO-G2SQTOF) .

Getting following errors:

s <- adjustRtime(res, param = ObiwarpParam()) Sample number 3 used as center sample.

Aligning Mouse_feces C18_NEG_sa1.mzXML against Mouse_feces_C18_NEG_sa1_inj2.mzXML ... Error in names(res) <- nms : 'names' attribute [5] must be the same length as the vector [4] In addition: Warning message: stop worker failed: 'clear_cluster' receive data failed: reached elapsed time limit

pgp <- PeakGroupsParam(minFraction = 1, extraPeaks = 1, smooth = "loess",

  • span = 0.2, family = "gaussian") s <- adjustRtime(res, param = pgp) Error in colnames<-(*tmp*, value = basename(fileNames(object))) : attempt to set 'colnames' on an object with less than two dimensions pgp <- PeakGroupsParam(minFraction = 0.5, extraPeaks = 1, smooth = "loess", span = 0.2, family = "gaussian") s <- adjustRtime(res, param = pgp) Performing retention time correction using 28 peak groups. Error in do_adjustRtime_peakGroups(chromPeaks(object), peakIndex = featureDefinitions(object)$peakidx, : Not enough peak groups even for linear smoothing available! In addition: Warning message: In do_adjustRtime_peakGroups(chromPeaks(object), peakIndex = featureDefinitions(object)$peakidx, : Too few peak groups for 'loess', reverting to linear method

res MSn experiment data ("XCMSnExp") Object size in memory: 8.2 Mb

      • Spectra data - - - MS level(s): 1 Number of spectra: 34080 MSn retention times: 0:1 - 29:60 minutes
      • Processing information - - - Data loaded [Wed Dec 5 12:51:52 2018] Filter: select MS level(s) 1 [Wed Dec 5 12:51:52 2018] MSnbase version: 2.7.3
      • Meta data - - - phenoData rowNames: 1 2 ... 6 (6 total) varLabels: sample_name sample_group varMetadata: labelDescription Loaded from: [1] blank_C18_NEG_23Oct18_3.mzXML... [6] Mouse_feces_C18_NEG_sa2_inj2.mzXML Use 'fileNames(.)' to see all files. protocolData: none featureData featureNames: F1.S00008 F1.S00011 ... F6.S14680 (34080 total) fvarLabels: fileIdx spIdx ... spectrum (29 total) fvarMetadata: labelDescription experimentData: use 'experimentData(object)'
      • xcms preprocessing - - - Chromatographic peak detection: method: centWave 224 peaks identified in 6 samples. On average 44.8 chromatographic peaks per sample. Correspondence: method: chromatographic peak density 56 features identified. Median mz range of features: 0 Median rt range of features: 3.7725

sessionInfo() R version 3.5.0 (2018-04-23) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

Matrix products: default BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

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] tools stats4 parallel stats graphics grDevices utils [8] datasets methods base

other attached packages: [1] dplyr_0.7.6 optparse_1.6.0 magrittr_1.5 [4] RColorBrewer_1.1-2 MAIT_1.15.0 pls_2.6-0 [7] CAMERA_1.37.0 xcms_3.4.1 MSnbase_2.8.2 [10] ProtGenerics_1.13.0 S4Vectors_0.19.19 mzR_2.15.1 [13] Rcpp_0.12.18 Biobase_2.41.2 BiocGenerics_0.27.1 [16] BiocParallel_1.15.8

loaded via a namespace (and not attached): [1] backports_1.1.2 spam_2.2-0 Hmisc_4.1-1 [4] plyr_1.8.4 igraph_1.2.2 lazyeval_0.2.1 [7] sp_1.3-1 splines_3.5.0 AlgDesign_1.1-7.3 [10] ggplot2_3.0.0 digest_0.6.15 foreach_1.4.4 [13] BiocInstaller_1.31.3 htmltools_0.3.6 gdata_2.18.0 [16] checkmate_1.8.5 cluster_2.0.7-1 doParallel_1.0.11 [19] sfsmisc_1.1-2 limma_3.37.3 recipes_0.1.3 [22] gower_0.1.2 gmodels_2.18.1 dimRed_0.1.0 [25] colorspace_1.3-2 crayon_1.3.4 graph_1.59.0 [28] bindr_0.1.1 impute_1.55.0 survival_2.42-6 [31] iterators_1.0.10 glue_1.3.0 DRR_0.0.3 [34] gtable_0.2.0 ipred_0.9-6 zlibbioc_1.27.0 [37] questionr_0.6.3 kernlab_0.9-27 ddalpha_1.3.4 [40] DEoptimR_1.0-8 maps_3.3.0 abind_1.4-5 [43] scales_1.0.0 vsn_3.49.1 miniUI_0.1.1.1 [46] xtable_1.8-2 spData_0.2.9.3 htmlTable_1.12 [49] magic_1.5-8 foreign_0.8-71 spdep_0.7-7 [52] preprocessCore_1.43.0 dotCall64_1.0-0 Formula_1.2-3 [55] lava_1.6.3 prodlim_2018.04.18 getopt_1.20.2 [58] htmlwidgets_1.2 gplots_3.0.1 acepack_1.4.1 [61] pkgconfig_2.0.1 XML_3.98-1.16 deldir_0.1-15 [64] nnet_7.3-12 caret_6.0-80 tidyselect_0.2.4 [67] rlang_0.2.1 reshape2_1.4.3 later_0.7.3 [70] munsell_0.5.0 broom_0.5.0 geometry_0.3-6 [73] stringr_1.3.1 mzID_1.19.0 ModelMetrics_1.2.0 [76] RhpcBLASctl_0.18-205 knitr_1.20 robustbase_0.93-2 [79] caTools_1.17.1.1 purrr_0.2.5 RANN_2.6 [82] bindrcpp_0.2.2 RBGL_1.57.0 nlme_3.1-137 [85] mime_0.5 RcppRoll_0.3.0 compiler_3.5.0 [88] rstudioapi_0.7 e1071_1.7-0 affyio_1.51.0 [91] klaR_0.6-14 MassSpecWavelet_1.47.0 tibble_1.4.2 [94] stringi_1.2.4 highr_0.7 fields_9.6 [97] lattice_0.20-35 Matrix_1.2-14 multtest_2.37.0 [100] LearnBayes_2.15.1 pillar_1.3.0 combinat_0.0-8 [103] MALDIquant_1.18 data.table_1.11.4 bitops_1.0-6 [106] httpuv_1.4.5 agricolae_1.2-8 R6_2.2.2 [109] latticeExtra_0.6-28 pcaMethods_1.73.0 affy_1.59.0 [112] promises_1.0.1 KernSmooth_2.23-15 gridExtra_2.3 [115] IRanges_2.15.16 codetools_0.2-15 boot_1.3-20 [118] MASS_7.3-50 gtools_3.8.1 assertthat_0.2.0 [121] CVST_0.2-2 withr_2.1.2 expm_0.999-2 [124] plsgenomics_1.5-1 grid_3.5.0 rpart_4.1-13 [127] timeDate_3043.102 coda_0.19-1 tidyr_0.8.1 [130] class_7.3-14 shiny_1.1.0 lubridate_1.7.4 [133] base64enc_0.1-3

jorainer commented 5 years ago

Yes, I guess for peak group alignment you have simply to few peaks in your data. Regarding obiwarp, I have no immediate solution/explanation what might have happened. You could try with a different binSize (e.g. ObiwarpParam(binSize = 0.4).

To really track the problem down I would need to get hand on the data.