Retrieving Results from Sirius

library(RSirius)
library(RuSirius)
#> Warning: package 'generics' was built under R version 4.6.1

Introduction

Note: this vignette is pre-computed. See the session info for information on packages used and the date the vignette was rendered. The vignette requires a running Sirius instance. To reproduce this analysis, you will need Sirius 6.5 installed and running.

After running Sirius computations (see the “Importing Spectra” vignette), you can retrieve various types of results. RuSirius provides two main functions:

summary() - Quick overview of top results per feature
results() - Detailed results with multiple candidates

Connecting to a Project

First, connect to an existing Sirius project that has completed computations. Here we reuse the test_spectra project created and computed in the “Importing Spectra” vignette:

srs <- Sirius(projectId = "test_spectra", path = getwd(), port = 9999)

Quick Summary with `summary()`

The summary() function provides the top annotation for each feature. This is useful for a quick overview of your results.

Formula Identification Summary

# Top formula candidates
summary_formula <- summary(srs, result.type = "formulaId")
head(summary_formula)
#>     alignedFeatureId         compoundId externalFeatureId  ionMass charge
#> 1 898503867363106669 898503867363106666               421 217.1185      1
#> 2 898503867421826927 898503867363106668               895   0.0000      1
#> 3 898503867400855406 898503867363106667               707 445.1181      1
#>   detectedAdducts        quality hasMs1 hasMsMs          formulaId
#> 1        [M + ?]+ NOT_APPLICABLE   TRUE    TRUE 898503890498887619
#> 2        [M + ?]+ NOT_APPLICABLE   TRUE   FALSE 898503886988255141
#> 3        [M + ?]+ NOT_APPLICABLE   TRUE    TRUE 898503893137105049
#>   molecularFormula    adduct rank siriusScoreNormalized siriusScore
#> 1        C9H16N2O4  [M + H]+    1             1.0000000    93.65962
#> 2             C3HN  [M + H]+    1             1.0000000     0.00000
#> 3     C15H20F6N2O5 [M + Na]+    2             0.2575964    32.53867
#>   isotopeScore treeScore chainsUnknown       inchiKey
#> 1    0.0000000  93.65962         FALSE HJJBFVPLKKKFTP
#> 2    0.0000000   0.00000         FALSE           <NA>
#> 3    0.2012745  32.33740         FALSE SBPVPWWNQIKSKL
#>                                                      smiles
#> 1                                CCOC(=O)C(=NN(C)C)C(=O)OCC
#> 2                                                      <NA>
#> 3 CC(CC(=O)OC(C)(C)C)NC(=O)C1=NOC(C1)C(C(F)(F)F)(C(F)(F)F)O
#>                                                                                                                  structureName
#> 1                                                                         Malonic acid, oxo-, diethyl ester, dimethylhydrazone
#> 2                                                                                                                         <NA>
#> 3 tert-butyl-(3R)-3-{[5-(2,2,2-trifluoro-1-hydroxy-1-trifluoromethyl-ethyl)-4,5-dihydro-isoxazole-3-carbonyl]-amino}-butanoate
#>   xlogP rank.1  csiScore tanimotoSimilarity mcesDistToTopHit type   level
#> 1   1.8      1 -230.1218          0.2098765                0  NPC PATHWAY
#> 2    NA     NA        NA                 NA               NA <NA>    <NA>
#> 3   2.2      1 -257.9119          0.3937500                0  NPC PATHWAY
#>   levelIndex        name          description id probability index type.1
#> 1          0 Fatty acids Pathway: Fatty acids  3   0.9712808     3    NPC
#> 2         NA        <NA>                 <NA> NA          NA    NA   <NA>
#> 3          0   Alkaloids   Pathway: Alkaloids  0   0.4939631     0    NPC
#>      level.1 levelIndex.1              name.1                   description.1
#> 1 SUPERCLASS            1        Fatty amides        Superclass: Fatty amides
#> 2       <NA>           NA                <NA>                            <NA>
#> 3 SUPERCLASS            1 Ornithine alkaloids Superclass: Ornithine alkaloids
#>   id.1 probability.1 index.1 type.2 level.2 levelIndex.2                 name.2
#> 1   27     0.6177541      27    NPC   CLASS            2         Primary amides
#> 2   NA            NA      NA   <NA>    <NA>           NA                   <NA>
#> 3   48     0.4983223      48    NPC   CLASS            2 Pyrimidine nucleosides
#>                   description.2 id.2 probability.2 index.2 confidenceExactMatch
#> 1         Class: Primary amides  587    0.17305040     587           0.05037627
#> 2                          <NA>   NA            NA      NA                   NA
#> 3 Class: Pyrimidine nucleosides  603    0.04471469     603           0.05173050
#>   confidenceApproxMatch expansiveSearchState computing
#> 1            0.05037627                  OFF     FALSE
#> 2                    NA                 <NA>     FALSE
#> 3            0.05173050                  OFF     FALSE

Key columns include confidenceApproxMatch (overall confidence for the feature) and confidenceExactMatch (confidence for the top hit).

Structure Database Summary

# Top structure hits
summary_structure <- summary(srs, result.type = "structure")

De Novo Structure Summary

# Top MSNovelist predictions
summary_denovo <- summary(srs, result.type = "deNovo")

Spectral Library Match Summary

# Best spectral matches
summary_spectral <- summary(srs, result.type = "spectralDbMatch")

Detailed Results with `results()`

The results() function returns multiple candidates per feature, giving you more options to explore.

Formula Candidates

# Get top 5 formula candidates per feature
formulas <- results(srs,
                    result.type = "formulaId",
                    topFormula = 5,
                    return.type = "data.frame")
head(formulas)
#>            formulaId molecularFormula    adduct rank siriusScoreNormalized
#> 1 898503890498887619        C9H16N2O4  [M + H]+    1          1.000000e+00
#> 2 898503890498887620        C7H14N5O3  [M + H]+    2          2.026043e-09
#> 3 898503890498887621        C7H18N2O4 [M + Na]+    3          1.248176e-22
#> 4 898503890498887622       C5H19N3O4P  [M + H]+    4          2.027193e-24
#> 5 898503890498887623       C3H17N6O3P  [M + H]+    5          1.566101e-24
#> 6 898503886988255141             C3HN  [M + H]+    1          1.000000e+00
#>   siriusScore isotopeScore treeScore lipidAnnotation xcms_fts
#> 1    93.65962            0  93.65962           FALSE      421
#> 2    73.64244            0  73.64244           FALSE      421
#> 3    43.22443            0  43.22443           FALSE      421
#> 4    39.10423            0  39.10423           FALSE      421
#> 5    38.84616            0  38.84616           FALSE      421
#> 6     0.00000            0   0.00000           FALSE      895
#>           sirius_fts
#> 1 898503867363106669
#> 2 898503867363106669
#> 3 898503867363106669
#> 4 898503867363106669
#> 5 898503867363106669
#> 6 898503867421826927

Structure Database Results

# Get structure candidates for each formula
structures <- results(srs,
                      result.type = "structureDb",
                      topFormula = 3,
                      topStructure = 5,
                      return.type = "data.frame")

Key columns: inchiKey, smiles, structureName, csiScore.

Compound Class Predictions (CANOPUS)

# Get predicted compound classes
classes <- results(srs,
                   result.type = "compoundClass",
                   topFormula = 1,
                   return.type = "data.frame")
#> Warning in value[[3L]](cond): Formula: 898503886988255141 for feature:
#> 898503867421826927 does not have compound classes information.

Returns ClassyFire and NPC classifications with confidence scores.

De Novo Structures (MSNovelist)

# Get de novo predicted structures
denovo <- results(srs,
                  result.type = "deNovo",
                  topFormula = 1,
                  topStructure = 5,
                  return.type = "data.frame")

Spectral Library Matches

# Get spectral library matches
spectral <- results(srs,
                    result.type = "spectralDbMatch",
                    topSpectralMatches = 5,
                    return.type = "data.frame")

Fragmentation Trees

# Get fragmentation tree data
fragtrees <- results(srs,
                     result.type = "fragTree",
                     topFormula = 1,
                     return.type = "data.frame")

Filtering by Feature

You can retrieve results for specific features only:

# Get feature IDs
feature_ids <- featuresId(srs)

# Get results for first two features only
subset_results <- results(srs,
                          features = feature_ids[1:2],
                          result.type = "structureDb",
                          return.type = "data.frame")

Return Types

Results can be returned as a data.frame (default) or as a list:

# As list - useful for per-feature processing
results_list <- results(srs,
                        result.type = "formulaId",
                        return.type = "list")

Access individual features by name:

# Access results for a specific feature
results_list[["feature_id_here"]]

Mapping to Original IDs

If you imported data from xcms, results include the original xcms feature IDs in the xcms_fts column for easy mapping back to your original data.

# Get the feature mapping
mapFeatures(srs)
#>           fts_sirius fts_xcms
#> 1 898503867363106669      421
#> 2 898503867421826927      895
#> 3 898503867400855406      707

Session Info

sessionInfo()
#> R version 4.6.0 (2026-04-24 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26300)
#> 
#> Matrix products: default
#>   LAPACK version 3.12.1
#> 
#> locale:
#> [1] LC_COLLATE=English_United Kingdom.utf8 
#> [2] LC_CTYPE=English_United Kingdom.utf8   
#> [3] LC_MONETARY=English_United Kingdom.utf8
#> [4] LC_NUMERIC=C                           
#> [5] LC_TIME=English_United Kingdom.utf8    
#> 
#> time zone: Europe/Paris
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#> [1] RuSirius_1.0.6      Spectra_1.22.2      BiocParallel_1.46.0
#> [4] S4Vectors_0.50.1    BiocGenerics_0.58.1 generics_0.1.4     
#> [7] ProtGenerics_1.44.0 RSirius_6.5.4      
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1            dplyr_1.2.1                
#>  [3] farver_2.1.2                blob_1.3.0                 
#>  [5] Biostrings_2.80.1           filelock_1.0.3             
#>  [7] S7_0.2.2                    bitops_1.0-9               
#>  [9] fastmap_1.2.0               lazyeval_0.2.3             
#> [11] RCurl_1.98-1.19             BiocFileCache_3.2.0        
#> [13] digest_0.6.39               lifecycle_1.0.5            
#> [15] cluster_2.1.8.2             rsvg_2.7.0                 
#> [17] KEGGREST_1.52.2             RSQLite_3.53.3             
#> [19] magrittr_2.0.5              compiler_4.6.0             
#> [21] rlang_1.2.0                 tools_4.6.0                
#> [23] yaml_2.3.12                 igraph_2.3.3               
#> [25] data.table_1.18.4           knitr_1.51                 
#> [27] S4Arrays_1.12.0             htmlwidgets_1.6.4          
#> [29] curl_7.1.0                  bit_4.6.0                  
#> [31] DelayedArray_0.38.2         plyr_1.8.9                 
#> [33] xml2_1.6.0                  RColorBrewer_1.1-3         
#> [35] abind_1.4-8                 purrr_1.2.2                
#> [37] grid_4.6.0                  ggplot2_4.0.3              
#> [39] scales_1.4.0                MASS_7.3-65                
#> [41] MultiAssayExperiment_1.38.0 SummarizedExperiment_1.42.0
#> [43] cli_3.6.6                   crayon_1.5.3               
#> [45] otel_0.2.0                  httr_1.4.8                 
#> [47] reshape2_1.4.5              rjson_0.2.23               
#> [49] DBI_1.3.0                   cachem_1.1.0               
#> [51] stringr_1.6.0               parallel_4.6.0             
#> [53] AnnotationDbi_1.74.0        BiocManager_1.30.27        
#> [55] XVector_0.52.0              AnnotationFilter_1.36.0    
#> [57] matrixStats_1.5.0           base64enc_0.1-6            
#> [59] vctrs_0.7.3                 Matrix_1.7-5               
#> [61] jsonlite_2.0.0              IRanges_2.46.0             
#> [63] bit64_4.8.2                 clue_0.3-68                
#> [65] tidyr_1.3.2                 glue_1.8.1                 
#> [67] codetools_0.2-20            QFeatures_1.22.0           
#> [69] DT_0.34.0                   stringi_1.8.7              
#> [71] gtable_0.3.6                BiocVersion_3.23.1         
#> [73] GenomicRanges_1.64.0        tibble_3.3.1               
#> [75] MetaboAnnotation_1.16.0     pillar_1.11.1              
#> [77] rappdirs_0.3.4              htmltools_0.5.9            
#> [79] Seqinfo_1.2.0               R6_2.6.1                   
#> [81] dbplyr_2.6.0                httr2_1.2.3                
#> [83] evaluate_1.0.5              Biobase_2.72.0             
#> [85] lattice_0.22-9              AnnotationHub_4.2.1        
#> [87] png_0.1-9                   memoise_2.0.1              
#> [89] MetaboCoreUtils_1.20.1      Rcpp_1.1.1-1.1             
#> [91] SparseArray_1.12.2          gridExtra_2.3.1            
#> [93] CompoundDb_1.16.0           ChemmineR_3.64.0           
#> [95] xfun_0.59                   MsCoreUtils_1.24.0         
#> [97] fs_2.1.0                    MatrixGenerics_1.24.0      
#> [99] pkgconfig_2.0.3