Input data takes too long to load.

[esalphaz16-ops]

I was learning to run xcms for the first time. So I ran the following lines, at least so far.

library(xcms)
library(MSnbase)
library(pander)
library(MsExperiment)
library(RColorBrewer)
library(pheatmap)
library(S4Vectors)

SnowParam(4)

x <- c("MS_Convert_mzML/Blank1.mzML", "MS_Convert_mzML/Infected1.mzML", "MS_Convert_mzML/Uninfected1.mzML")
pd <- data.frame(sample_name = tools::file_path_sans_ext(basename(x)),
                 sample_group = c("Blank", "Infected", "Uninfected"),
                 stringsAsFactors = FALSE)
raw_data <- readMsExperiment(spectraFiles = x, sampleData = pd, mode =  "OnDisk")

The last line doesn't seem to get executed even after hours of me running it.

For context, my three input files are of the sizes - 731 MB, 727 MB and 726 MB.

I have an ASUS TUF Gaming A15 laptop with 16 GB RAM. Here's a detail of my RSession.

Can someone tell me what should I do?

Here's the session info.

> sessionInfo()
R version 4.5.0 (2025-04-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_India.utf8  LC_CTYPE=English_India.utf8    LC_MONETARY=English_India.utf8
[4] LC_NUMERIC=C                   LC_TIME=English_India.utf8    

time zone: Asia/Calcutta
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] pheatmap_1.0.13     RColorBrewer_1.1-3  MsExperiment_1.10.1 pander_0.6.6        MSnbase_2.34.1     
 [6] ProtGenerics_1.40.0 S4Vectors_0.46.0    mzR_2.42.0          Rcpp_1.1.0          Biobase_2.68.0     
[11] BiocGenerics_0.54.0 generics_0.1.4      xcms_4.6.3          BiocParallel_1.42.1

loaded via a namespace (and not attached):
 [1] DBI_1.2.3                   rlang_1.1.6                 magrittr_2.0.3              clue_0.3-66                
 [5] MassSpecWavelet_1.74.0      matrixStats_1.5.0           compiler_4.5.0              vctrs_0.6.5                
 [9] reshape2_1.4.4              stringr_1.5.1               pkgconfig_2.0.3             MetaboCoreUtils_1.16.1     
[13] crayon_1.5.3                XVector_0.48.0              UCSC.utils_1.4.0            preprocessCore_1.70.0      
[17] purrr_1.1.0                 xfun_0.52                   MultiAssayExperiment_1.34.0 GenomeInfoDb_1.44.1        
[21] jsonlite_2.0.0              progress_1.2.3              DelayedArray_0.34.1         parallel_4.5.0             
[25] prettyunits_1.2.0           cluster_2.1.8.1             R6_2.6.1                    stringi_1.8.7              
[29] limma_3.64.1                GenomicRanges_1.60.0        SummarizedExperiment_1.38.1 iterators_1.0.14           
[33] knitr_1.50                  IRanges_2.42.0              BiocBaseUtils_1.10.0        Matrix_1.7-3               
[37] igraph_2.1.4                tidyselect_1.2.1            rstudioapi_0.17.1           abind_1.4-8                
[41] doParallel_1.0.17           codetools_0.2-20            affy_1.86.0                 lattice_0.22-6             
[45] tibble_3.3.0                plyr_1.8.9                  evaluate_1.0.4              Spectra_1.18.2             
[49] pillar_1.11.0               affyio_1.78.0               BiocManager_1.30.26         MatrixGenerics_1.20.0      
[53] foreach_1.5.2               MALDIquant_1.22.3           ncdf4_1.24                  hms_1.1.3                  
[57] ggplot2_3.5.2               scales_1.4.0                glue_1.8.0                  MsFeatures_1.16.0          
[61] lazyeval_0.2.2              tools_4.5.0                 mzID_1.46.0                 QFeatures_1.18.0           
[65] vsn_3.76.0                  fs_1.6.6                    XML_3.99-0.18               grid_4.5.0                 
[69] impute_1.82.0               tidyr_1.3.1                 MsCoreUtils_1.20.0          colorspace_2.1-1           
[73] GenomeInfoDbData_1.2.14     PSMatch_1.12.0              cli_3.6.5                   S4Arrays_1.8.1             
[77] dplyr_1.1.4                 AnnotationFilter_1.32.0     pcaMethods_2.0.0            gtable_0.3.6               
[81] digest_0.6.37               SparseArray_1.8.1           farver_2.1.2                lifecycle_1.0.4            
[85] httr_1.4.7                  statmod_1.5.0               MASS_7.3-65    

[sneumann]

Hi, thanks for reporting, hours is certainly not the expected performance for three files.
What is windows task manager reporting, is your R process running at 100% load ?
What if you run on just one of the files ? What MS setup did you use, e.g. instrument, and MS1/MS2 and Scan rate ?
Yours,
Steffen

[esalphaz16-ops]

1. R is occupying around 2 GB RAM. Not at 100% Load
2. I did try running it on one file, same result.
3. The MS Setup was of SCIEX X500R for untargeted proteomics. I don't know the scan rate right now but these files contain both MS1 and MS2 scans.

[sneumann]

any chance to share one of the files with me ? Blank would be fine. Yours, Steffen

[stanstrup]

From the file size I am guessing they might be continuous mode data. If that is so, you'd want to centroid first in msconvert.

[esalphaz16-ops]

The files are too big for upload apparently, around 700 MB each. What should I do ?

[sneumann]

Could you place them somewhere in a cloud and send a dropbox/google drive/onedrive/nextcloud/... Link to my eMail you find on https://www.ipb-halle.de/en/research/program-center-metacom ? Yours, Steffen

[jorainer]

This seems very odd. The readMsExperiment() function is only reading general header info from the mzML files, but no MS data. It must thus work even on computers with little main memory. Can you re-run the last line without the mode parameter? That parameter was used in readMsData(), but it is not an argument in the readMsExperiment() function:

raw_data <- readMsExperiment(spectraFiles = x, sampleData = pd)