Hello,

I'm trying to utilize the novelty detection and clustering built into the pRoloc package for analyzing proteomics data. Was struggling to get it to work on my own data, so went back to the example data.

source("https://bioconductor.org/biocLite.R")
biocLite(c("MSnbase", "pRoloc", "pRolocdata", "pRolocGUI"))
extdatadir <- system.file("extdata", package = "pRolocdata")
csvfile <- dir(extdatadir, full.names = TRUE, pattern = "hyperLOPIT-SIData-ms3-rep12-intersect.csv")
basename(csvfile)
getEcols(csvfile, split = ",", n = 2)
hl <- readMSnSet2(csvfile, ecol = 8:27, fnames = 1, skip = 1)
fvarLabels(hl)
fvarLabels(hl)[1:3] <- c("uniprot.accession", "uniprot.id", "description")
fvarLabels(hl)[4:6] <- paste0("peptides.expt", 1:3)
fData(hl)[1:4, c(1:2, 4:6)]
pData(hl)$Replicate <- rep(1:2, each = 10)
pData(hl)$Tag <- sub("\\.1$", "", sub("^X", "", sampleNames(hl)))
pData(hl)
expinfo <- dir(extdatadir, full.names = TRUE, pattern = "hyperLOPIT-SIData-fraction-info.csv")
fracinfo <- read.csv(expinfo, row.names=1, skip = 2, header = FALSE, stringsAsFactors = FALSE)
pData(hl)$Gradient.Fraction <- c(fracinfo[, 1], fracinfo[, 2])
pData(hl)$Iodixonal.Density <- c(fracinfo[, 4], fracinfo[, 5])
pData(hl)
getMarkers(hl10.10, fcol = "phenoDisco.Input")
hl10.10 <- phenoDisco(hl, fcol = "phenoDisco.Input", times = 10, GS = 10)

Using the call for hl10.10, the code runs 23 iterations (the times = 10 means it should only do 10). No clustering data is produced, as seen by calling getMarkers(hl10.10, fcol = "phenoDisco.Input"). Typically new phenotype groups would be listed.

A series of warnings are produced, all of which are:

1: In serialize(data, node$con) :
  'package:stats' may not be available when loading

I hope I haven't forgotten to include any information. Thanks for your time.

sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=English_Canada.1252  LC_CTYPE=English_Canada.1252   
[3] LC_MONETARY=English_Canada.1252 LC_NUMERIC=C                   
[5] LC_TIME=English_Canada.1252    
attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods  
[9] base     
other attached packages:
 [1] pRolocGUI_1.20.0     pRolocdata_1.24.0    pRoloc_1.26.0        BiocParallel_1.20.1 
 [5] MLInterfaces_1.66.2  cluster_2.1.0        annotate_1.64.0      XML_3.98-1.20       
 [9] AnnotationDbi_1.48.0 IRanges_2.20.1       MSnbase_2.12.0       ProtGenerics_1.18.0 
[13] S4Vectors_0.24.1     mzR_2.20.0           Rcpp_1.0.3           Biobase_2.46.0      
[17] BiocGenerics_0.32.0 
loaded via a namespace (and not attached):
  [1] snow_0.4-3            backports_1.1.5       BiocFileCache_1.10.2 
  [4] plyr_1.8.5            igraph_1.2.4.2        lazyeval_0.2.2       
  [7] splines_3.6.2         ggvis_0.4.5           crosstalk_1.0.0      
 [10] ggplot2_3.2.1         digest_0.6.23         foreach_1.4.7        
 [13] htmltools_0.4.0       viridis_0.5.1         gdata_2.18.0         
 [16] magrittr_1.5          memoise_1.1.0         doParallel_1.0.15    
 [19] mixtools_1.1.0        sfsmisc_1.1-4         limma_3.42.0         
 [22] recipes_0.1.9         gower_0.2.1           askpass_1.1          
 [25] lpSolve_5.6.13.3      prettyunits_1.1.0     colorspace_1.4-1     
 [28] blob_1.2.0            rappdirs_0.3.1        xfun_0.12            
 [31] dplyr_0.8.3           crayon_1.3.4          RCurl_1.95-4.12      
 [34] hexbin_1.28.0         genefilter_1.68.0     zeallot_0.1.0        
 [37] impute_1.60.0         survival_3.1-8        iterators_1.0.12     
 [40] glue_1.3.1            gtable_0.3.0          ipred_0.9-9          
 [43] zlibbioc_1.32.0       kernlab_0.9-29        prabclus_2.3-2       
 [46] DEoptimR_1.0-8        scales_1.1.0          vsn_3.54.0           
 [49] mvtnorm_1.0-12        DBI_1.1.0             viridisLite_0.3.0    
 [52] xtable_1.8-4          progress_1.2.2        bit_1.1-15.1         
 [55] proxy_0.4-23          mclust_5.4.5          preprocessCore_1.48.0
 [58] DT_0.11               lava_1.6.6            prodlim_2019.11.13   
 [61] sampling_2.8          htmlwidgets_1.5.1     httr_1.4.1           
 [64] threejs_0.3.1         FNN_1.1.3             RColorBrewer_1.1-2   
 [67] fpc_2.2-3             modeltools_0.2-22     pkgconfig_2.0.3      
 [70] flexmix_2.3-15        nnet_7.3-12           dbplyr_1.4.2         
 [73] caret_6.0-85          reshape2_1.4.3        tidyselect_0.2.5     
 [76] rlang_0.4.2           later_1.0.0           munsell_0.5.0        
 [79] mlbench_2.1-1         tools_3.6.2           LaplacesDemon_16.1.1 
 [82] generics_0.0.2        RSQLite_2.2.0         pls_2.7-2            
 [85] stringr_1.4.0         fastmap_1.0.1         mzID_1.24.0          
 [88] ModelMetrics_1.2.2.1  knitr_1.26            bit64_0.9-7          
 [91] robustbase_0.93-5     randomForest_4.6-14   purrr_0.3.3          
 [94] dendextend_1.13.2     ncdf4_1.17            nlme_3.1-143         
 [97] mime_0.8              biomaRt_2.42.0        compiler_3.6.2       
[100] rstudioapi_0.10       curl_4.3              e1071_1.7-3          
[103] affyio_1.56.0         tibble_2.1.3          stringi_1.4.5        
[106] lattice_0.20-38       Matrix_1.2-18         gbm_2.1.5            
[109] vctrs_0.2.1           pillar_1.4.3          lifecycle_0.1.0      
[112] BiocManager_1.30.10   MALDIquant_1.19.3     data.table_1.12.8    
[115] bitops_1.0-6          httpuv_1.5.2          R6_2.4.1             
[118] pcaMethods_1.78.0     affy_1.64.0           hwriter_1.3.2        
[121] promises_1.1.0        gridExtra_2.3         codetools_0.2-16     
[124] MASS_7.3-51.5         gtools_3.8.1          assertthat_0.2.1     
[127] openssl_1.4.1         withr_2.1.2           diptest_0.75-7       
[130] hms_0.5.3             grid_3.6.2            rpart_4.1-15         
[133] timeDate_3043.102     coda_0.19-3           class_7.3-15         
[136] segmented_1.1-0       pROC_1.16.1           lubridate_1.7.4      
[139] shiny_1.4.0           base64enc_0.1-3    

The times argument doesn't correspond to the number of iterations that are displayed on the console, so no worries on that front.

The phenoDisco results are stored in a new feature variable named pd. Try getMarkers(hl10.10, fcol = "pd") to access them; the code you show, i.e. with fcol = "phenoDisco.Input") will get you the input markers.

By the way, the recommended way to install packages is BiocManager::install() rather than biocLite.