wenbostar / proteoqc

proteoQC: an R package for proteomics data quality assessment.

proteoQC

proteoQC is an R package for proteomics data quality assessment. This package creates an HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. The official page is the Bioconductor landing page (release or devel versions).

Installation

To install proteoQC

library("BiocInstaller")
biocLite("proteoQC")

If you need the github version (not recommended unless you know what you are doing)

biocLite("wenbostar/proteoQC")

Citation

To cite the proteoQC package in publications, please use:

Wen B and Gatto L (2017). proteoQC: An R package for proteomics data quality control. R package version 1.15.0, https://github.com/wenbostar/proteoQC.

List of citations

proteoQC has been cited in the following manuscripts:

1. Gatto, Laurent, et al. "Visualization of proteomics data using R and Bioconductor." Proteomics 15.8 (2015): 1375-1389.
2. Bittremieux, Wout, et al. "Computational quality control tools for mass spectrometry proteomics." Proteomics 17.3-4 (2017).
3. Samandi, Sondos, et al. "Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins." eLife 6 (2017): e27860.
4. Belghit I, Lock E J, Fumière O, et al. Species-Specific Discrimination of Insect Meals for Aquafeeds by Direct Comparison of Tandem Mass Spectra. Animals, 2019, 9(5): 222.
5. Walzer M., Vizcaíno J.A. (2020) Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics. In: Matthiesen R. (eds) Mass Spectrometry Data Analysis in Proteomics. Methods in Molecular Biology, vol 2051. Humana, New York, NY

Contribution

Contributions to the package are more than welcome.

proteoqc's Issues

Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded

Hi, After installing the developing version proteoQC R package from github, then I ran the following codes,

library(proteoQC)

mgf <- "FASP_210107_1.mgf"
fasta <- "uniprot-proteome_20210223.fasta"

msQCpipe(spectralist = mgf, 
         fasta = fasta, 
         outdir = "./qc",
         miss  = 0,
         enzyme = 1, varmod = 2, fixmod = 1,
         tol = 10, itol = 0.6, cpu = 20,
         mode = "identification")

it reported errors like these:

2021-02-25 17:28:22 
Loading spectra
 (mgf).............................................................................. loaded.
Spectra matching criteria = 120254
Starting threads .................... started.
Computing models:
	testing 1 2 3testing 1 2 3testing 1 2 3testing 1  | 50 ks 
	2 3testing 1 2 3testing 1 2 3testing 1 2 3testing | 100 ks 
	 1 2 3testing 1 2 3testing 1 2 3testing 1 2 3test | 150 ks 
	in
		sequences modelled = 152 ks
Model refinement:
	partial cleavage ..... done.
	unanticipated cleavage ..... done.
	modified N-terminus ..... done.
	finishing refinement ... done.
Merging results:
	from 2......3......4......5......6......7......8......9......10......11......12......13......14......15......16......17......18......19......20......

Creating report:
	initial calculations  ..... done.
	sorting  ..... done.
	finding repeats ..... done.
	evaluating results ..... done.
	calculating expectations ..... done.
	writing results ..... done.

Valid models = 103883


Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded
	at java.util.Arrays.copyOfRange(Arrays.java:3664)
	at java.lang.String.<init>(String.java:207)
	at com.sun.org.apache.xerces.internal.xni.XMLString.toString(XMLString.java:188)
	at com.sun.org.apache.xerces.internal.parsers.AbstractDOMParser.characters(AbstractDOMParser.java:1228)
	at com.sun.org.apache.xerces.internal.impl.XMLDocumentFragmentScannerImpl.scanDocument(XMLDocumentFragmentScannerImpl.java:455)
	at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:842)
	at com.sun.org.apache.xerces.internal.parsers.XML11Configuration.parse(XML11Configuration.java:771)
	at com.sun.org.apache.xerces.internal.parsers.XMLParser.parse(XMLParser.java:141)
	at com.sun.org.apache.xerces.internal.parsers.DOMParser.parse(DOMParser.java:243)
	at com.sun.org.apache.xerces.internal.jaxp.DocumentBuilderImpl.parse(DocumentBuilderImpl.java:339)
	at javax.xml.parsers.DocumentBuilder.parse(DocumentBuilder.java:205)
	at de.proteinms.xtandemparser.parser.XTandemParser.parseXTandemFile(XTandemParser.java:121)
	at de.proteinms.xtandemparser.parser.XTandemParser.<init>(XTandemParser.java:82)
	at de.proteinms.xtandemparser.xtandem.XTandemFile.<init>(XTandemFile.java:89)
	at cn.bgi.XTandemParser.main(XTandemParser.java:73)
Process file: ./qc/result/FASP_210107_1.mgf_xtandem.xml... 
Error in read.table(logfile, sep = "\t", header = TRUE, stringsAsFactors = FALSE) : no lines available in input

As it showed, the bugs were generated by the "java.lang.OutOfMemoryError: GC overhead limit exceeded", but when I searched the bug via google, I couldn't find any useful information.
Thank you in advance if you could give me some advice when you are free.

System information for running proteoQC:

R version 4.0.2 (2020-06-22)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: CentOS Linux 8 (Core)

Matrix products: default
BLAS/LAPACK: /disk/share/anaconda3/lib/libopenblasp-r0.3.10.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] proteoQC_1.13.5     MSnbase_2.16.1      ProtGenerics_1.20.0 S4Vectors_0.28.1    mzR_2.24.1          Rcpp_1.0.4.6       
 [7] Biobase_2.50.0      BiocGenerics_0.36.0 VennDiagram_1.6.20  futile.logger_1.4.3 XML_3.99-0.5        tibble_3.0.4       
[13] dplyr_1.0.2        

loaded via a namespace (and not attached):
 [1] bitops_1.0-6          bit64_4.0.5           doParallel_1.0.16     progress_1.2.2        httr_1.4.2            tools_4.0.2          
 [7] R6_2.5.0              affyio_1.60.0         lazyeval_0.2.2        DBI_1.1.0             colorspace_2.0-0      ade4_1.7-16          
[13] tidyselect_1.1.0      prettyunits_1.1.1     bit_4.0.4             Nozzle.R1_1.1-1       curl_4.3              compiler_4.0.2       
[19] preprocessCore_1.52.0 formatR_1.7           rTANDEM_1.23.1        xml2_1.3.2            plotly_4.9.2.1        scales_1.1.1         
[25] affy_1.68.0           rappdirs_0.3.1        stringr_1.4.0         digest_0.6.27         rmarkdown_2.5         pkgconfig_2.0.3      
[31] htmltools_0.5.0       dbplyr_2.0.0          limma_3.44.3          htmlwidgets_1.5.2     rlang_0.4.9           rstudioapi_0.11      
[37] RSQLite_2.2.1         impute_1.64.0         generics_0.1.0        jsonlite_1.7.1        mzID_1.28.0           BiocParallel_1.24.0  
[43] RCurl_1.98-1.2        magrittr_2.0.1        MALDIquant_1.19.3     munsell_0.5.0         lifecycle_0.2.0       vsn_3.58.0           
[49] stringi_1.5.3         yaml_2.2.1            MASS_7.3-53           zlibbioc_1.36.0       plyr_1.8.6            BiocFileCache_1.14.0 
[55] rpx_1.26.1            blob_1.2.1            crayon_1.3.4          lattice_0.20-41       hms_0.5.3             knitr_1.30           
[61] pillar_1.4.7          seqinr_4.2-4          reshape2_1.4.4        codetools_0.2-16      futile.options_1.0.1  glue_1.4.2           
[67] evaluate_0.14         data.table_1.13.2     pcaMethods_1.80.0     lambda.r_1.2.4        BiocManager_1.30.10   vctrs_0.3.5          
[73] foreach_1.5.1         gtable_0.3.0          purrr_0.3.4           tidyr_1.1.2           assertthat_0.2.1      ggplot2_3.3.2        
[79] xfun_0.18             viridisLite_0.3.0     ncdf4_1.17            iterators_1.0.13      tinytex_0.26          memoise_1.1.0        
[85] IRanges_2.24.1        ellipsis_0.3.1