A bug in reduce function

[wong-ziyi]

The use of missing() function in Line 1226 of utils.R makes addIdentificationData() function not working when id is a data.frame in R 4.3.2.

> quantFile <- dir(system.file(package = "MSnbase", dir = "extdata"), full.name = TRUE, pattern = "mzXML$")
> id <- readMzIdData(identFile)
> key <- "spectrumID"
> reduce(id, key)
Error in .local(x, ...) : Need a key column to reduce the data.frame

However, it works when passing key=key to reduce() function as shown below:

> reduce(id, key=key)
                             sequence spectrumID chargeState rank passThreshold          experimentalMassToCharge            calculatedMassToCharge peptideRef modNum
1 VESITARHGEVLQLRPK;IKPQAVIETLHRLTEGK     scan=1         3;3  1;2     TRUE;TRUE 645.374145507812;645.374145507812 645.037475585938;645.045837402344  Pep2;Pep3    0;0
2                       IDGQWVTHQWLKK     scan=2           3    1          TRUE                  546.958618164062                  546.963256835938       Pep1      0
3                             LVILLFR     scan=5           2    1          TRUE                  437.804016113281                  437.299652099609       Pep4      0
      isDecoy post pre   start     end  DatabaseAccess DBseqLength DatabaseSeq                                                                         DatabaseDescription
1 FALSE;FALSE  A;A R;K 170;372 186;388 ECA0984;ECA3829     231;572           ; ECA0984 DNA mismatch repair protein;ECA3829 acetolactate synthase isozyme III large subunit
2       FALSE    A   K      50      62         ECA1028         275                              ECA1028 2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase
3       FALSE    L   K      22      28         ECA0510         166                                        ECA0510 putative capsular polysacharide biosynthesis transferase
  scan.number.s. acquisitionNum                      spectrumFile                          idFile MS.GF.RawScore MS.GF.DeNovoScore          MS.GF.SpecEValue
1            1;1            1;1 dummyiTRAQ.mzXML;dummyiTRAQ.mzXML dummyiTRAQ.mzid;dummyiTRAQ.mzid        -39;-39             77;77 5.527468e-05;5.527468e-05
2              2              2                  dummyiTRAQ.mzXML                 dummyiTRAQ.mzid            -30                39              9.399048e-06
3              5              5                  dummyiTRAQ.mzXML                 dummyiTRAQ.mzid            -42                 5             0.00025778305
         MS.GF.EValue modPeptideRef modName modMass modLocation subOriginalResidue subReplacementResidue subLocation
1 79.369576;79.369576         NA;NA   NA;NA   NA;NA       NA;NA              NA;NA                 NA;NA       NA;NA
2           13.466147          <NA>    <NA>    <NA>        <NA>               <NA>                  <NA>        <NA>
3           366.38422          <NA>    <NA>    <NA>        <NA>               <NA>                  <NA>        <NA>

This is because the missing() function is used to determine whether an argument was supplied. Please check an example below:

> i <- function(a, b) {
+   c(missing(a), missing(b))
+ }
> i()
[1] TRUE TRUE
> i(a = 1)
[1] FALSE  TRUE
> i(b = 2)
[1]  TRUE FALSE
> i(1, 2)
[1] FALSE FALSE
> a<-1
> b<-2
> i(a, b)
[1] FALSE FALSE

Therefore, Line 183 of functions-addIdentificationData.R will disable .addDataFrameIdentificationData () function in any situation.

> sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=ja_JP.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=ja_JP.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=ja_JP.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=ja_JP.UTF-8 LC_IDENTIFICATION=C       

time zone: Asia/Tokyo
tzcode source: system (glibc)

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

other attached packages:
 [1] reticulate_1.40.0     dplyr_1.1.4           limma_3.58.1          sva_3.50.0            BiocParallel_1.36.0   genefilter_1.84.0     mgcv_1.9-1           
 [8] nlme_3.1-166          reshape2_1.4.4        ggplot2_3.5.1         RColorBrewer_1.1-3    RforProteomics_1.40.0 MSnbase_2.28.1        ProtGenerics_1.34.0  
[15] S4Vectors_0.40.2      mzR_2.36.0            Rcpp_1.0.13-1         Biobase_2.62.0        BiocGenerics_0.48.1  

loaded via a namespace (and not attached):
 [1] DBI_1.2.3               bitops_1.0-9            RBGL_1.78.0             rlang_1.1.4             magrittr_2.0.3          clue_0.3-66             matrixStats_1.4.1      
 [8] compiler_4.3.2          RSQLite_2.3.7           png_0.1-8               vctrs_0.6.5             stringr_1.5.1           crayon_1.5.3            pkgconfig_2.0.3        
[15] fastmap_1.2.0           XVector_0.42.0          utf8_1.2.4              biocViews_1.70.0        rmarkdown_2.29          graph_1.80.0            preprocessCore_1.64.0  
[22] bit_4.5.0               xfun_0.49               zlibbioc_1.48.2         cachem_1.1.0            jsonlite_1.8.9          GenomeInfoDb_1.38.8     blob_1.2.4             
[29] parallel_4.3.2          cluster_2.1.6           R6_2.5.1                stringi_1.8.4           iterators_1.0.14        knitr_1.49              R.utils_2.12.3         
[36] IRanges_2.36.0          Matrix_1.6-5            splines_4.3.2           tidyselect_1.2.1        rstudioapi_0.17.1       yaml_2.3.10             doParallel_1.0.17      
[43] codetools_0.2-20        affy_1.80.0             RUnit_0.4.33            lattice_0.22-6          tibble_3.2.1            plyr_1.8.9              withr_3.0.2            
[50] KEGGREST_1.42.0         evaluate_1.0.1          survival_3.7-0          Biostrings_2.70.3       pillar_1.9.0            affyio_1.72.0           BiocManager_1.30.25    
[57] MatrixGenerics_1.14.0   foreach_1.5.2           MALDIquant_1.22.3       ncdf4_1.23              generics_0.1.3          RCurl_1.98-1.16         munsell_0.5.1          
[64] scales_1.3.0            xtable_1.8-4            glue_1.8.0              tools_4.3.2             mzID_1.40.0             vsn_3.70.0              locfit_1.5-9.10        
[71] annotate_1.80.0         XML_3.99-0.17           grid_4.3.2              impute_1.76.0           edgeR_4.0.16            MsCoreUtils_1.14.1      AnnotationDbi_1.64.1   
[78] colorspace_2.1-1        GenomeInfoDbData_1.2.11 cli_3.6.3               fansi_1.0.6             pcaMethods_1.94.0       gtable_0.3.6            R.methodsS3_1.8.2      
[85] digest_0.6.37           memoise_2.0.1           htmltools_0.5.8.1       R.oo_1.27.0             lifecycle_1.0.4         httr_1.4.7              statmod_1.5.0          
[92] bit64_4.5.2             MASS_7.3-60.0.1  

[wong-ziyi]

Moreover, the reduce() function in the Line 183 of functions-addIdentificationData.R shall be moved into a line larger than 195. Because running reduce() after filterIdentificationDataFrame() will cause an error: "Error in !x[, decoy] : invalid argument type". This is because the reduce() function combined multiple logical values (FALSE;TRUE;) into one cell under the "isDecoy" column. Those cells with combined logical values (as characters type) will cause an error of invalid argument type.

> library("msdata")
> f <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"
> idf <- msdata::ident(full.names = TRUE, pattern = f)
> iddf <- readMzIdData(idf)
> iddf <- reduce(iddf, key = "spectrumID")
> iddf <- filterIdentificationDataFrame(iddf, verbose = TRUE)
Starting with 5343 PSMs:
Error in !x[, decoy] : invalid argument type

The error above caused by running the running reduce() before filterIdentificationDataFrame().

> library("msdata")
> f <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"
> idf <- msdata::ident(full.names = TRUE, pattern = f)
> iddf <- readMzIdData(idf)
> iddf <- filterIdentificationDataFrame(iddf, verbose = TRUE)
Starting with 5802 PSMs:
 removed 2896 decoy hits
 removed 155 PSMs with rank > 1
 removed 41 non-proteotypic peptides
2710 PSMs left.
> iddf <- reduce(iddf, key = "spectrumID")

Works normally when running the reduce() after filterIdentificationDataFrame().