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 6.3 installed and running.
After running Sirius computations (see the “Importing Spectra” vignette), you can retrieve various types of results. RuSirius provides two main functions:
Connecting to a Project
First, connect to an existing Sirius project that has completed computations:
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")
#> Error:
#> ! object 'srs' not found
head(summary_formula)
#> Error in `h()`:
#> ! error in evaluating the argument 'x' in selecting a method for function 'head': object 'summary_formula' not foundKey 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")
#> Error:
#> ! object 'srs' not foundDe Novo Structure Summary
# Top MSNovelist predictions
summary_denovo <- summary(srs, result.type = "deNovo")
#> Error:
#> ! object 'srs' not foundSpectral Library Match Summary
# Best spectral matches
summary_spectral <- summary(srs, result.type = "spectralDbMatch")
#> Error:
#> ! object 'srs' not foundDetailed 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")
#> Error:
#> ! object 'srs' not found
head(formulas)
#> Error in `h()`:
#> ! error in evaluating the argument 'x' in selecting a method for function 'head': object 'formulas' not foundStructure Database Results
# Get structure candidates for each formula
structures <- results(srs,
result.type = "structureDb",
topFormula = 3,
topStructure = 5,
return.type = "data.frame")
#> Error:
#> ! object 'srs' not foundKey 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")
#> Error:
#> ! object 'srs' not foundReturns 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")
#> Error:
#> ! object 'srs' not foundSpectral Library Matches
# Get spectral library matches
spectral <- results(srs,
result.type = "spectralDbMatch",
topSpectralMatches = 5,
return.type = "data.frame")
#> Error:
#> ! object 'srs' not foundFragmentation Trees
# Get fragmentation tree data
fragtrees <- results(srs,
result.type = "fragTree",
topFormula = 1,
return.type = "data.frame")
#> Error:
#> ! object 'srs' not foundFiltering by Feature
You can retrieve results for specific features only:
# Get feature IDs
feature_ids <- featuresId(srs)
#> Error:
#> ! object 'srs' not found
# Get results for first two features only
subset_results <- results(srs,
features = feature_ids[1:2],
result.type = "structureDb",
return.type = "data.frame")
#> Error:
#> ! object 'feature_ids' not foundReturn 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")
#> Error:
#> ! object 'srs' not foundAccess 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)
#> Error:
#> ! object 'srs' not foundSession Info
sessionInfo()
#> R version 4.5.2 (2025-10-31 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_United States.utf8 LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: Europe/Rome
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MsDataHub_1.10.0 dplyr_1.2.0 RuSirius_0.2.0
#> [4] jsonlite_2.0.0 MetaboAnnotation_1.14.0 RSirius_6.3.3
#> [7] xcms_4.8.0 MsExperiment_1.12.0 ProtGenerics_1.42.0
#> [10] Spectra_1.20.1 BiocParallel_1.44.0 S4Vectors_0.48.0
#> [13] BiocGenerics_0.56.0 generics_0.1.4
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 MultiAssayExperiment_1.36.1 magrittr_2.0.4
#> [4] farver_2.1.2 MALDIquant_1.22.3 fs_1.6.6
#> [7] vctrs_0.7.1 memoise_2.0.1 RCurl_1.98-1.17
#> [10] base64enc_0.1-6 htmltools_0.5.9 S4Arrays_1.10.1
#> [13] BiocBaseUtils_1.12.0 progress_1.2.3 curl_7.0.0
#> [16] AnnotationHub_4.0.0 SparseArray_1.10.8 mzID_1.48.0
#> [19] htmlwidgets_1.6.4 plyr_1.8.9 httr2_1.2.2
#> [22] impute_1.84.0 cachem_1.1.0 igraph_2.2.1
#> [25] lifecycle_1.0.5 iterators_1.0.14 pkgconfig_2.0.3
#> [28] Matrix_1.7-4 R6_2.6.1 fastmap_1.2.0
#> [31] MatrixGenerics_1.22.0 clue_0.3-66 digest_0.6.39
#> [34] pcaMethods_2.2.0 rsvg_2.7.0 AnnotationDbi_1.72.0
#> [37] ExperimentHub_3.0.0 GenomicRanges_1.62.1 RSQLite_2.4.5
#> [40] filelock_1.0.3 httr_1.4.7 abind_1.4-8
#> [43] compiler_4.5.2 withr_3.0.2 bit64_4.6.0-1
#> [46] doParallel_1.0.17 S7_0.2.1 DBI_1.2.3
#> [49] MASS_7.3-65 ChemmineR_3.62.0 rappdirs_0.3.4
#> [52] DelayedArray_0.36.0 rjson_0.2.23 mzR_2.44.0
#> [55] tools_4.5.2 PSMatch_1.14.0 otel_0.2.0
#> [58] CompoundDb_1.14.2 glue_1.8.0 QFeatures_1.20.0
#> [61] grid_4.5.2 cluster_2.1.8.1 reshape2_1.4.5
#> [64] snow_0.4-4 gtable_0.3.6 preprocessCore_1.72.0
#> [67] tidyr_1.3.2 data.table_1.18.2.1 hms_1.1.4
#> [70] MetaboCoreUtils_1.19.2 xml2_1.5.2 XVector_0.50.0
#> [73] BiocVersion_3.22.0 foreach_1.5.2 pillar_1.11.1
#> [76] stringr_1.6.0 limma_3.66.0 BiocFileCache_3.0.0
#> [79] lattice_0.22-7 bit_4.6.0 tidyselect_1.2.1
#> [82] Biostrings_2.78.0 knitr_1.51 gridExtra_2.3
#> [85] IRanges_2.44.0 Seqinfo_1.0.0 SummarizedExperiment_1.40.0
#> [88] xfun_0.56 Biobase_2.70.0 statmod_1.5.1
#> [91] MSnbase_2.36.0 matrixStats_1.5.0 DT_0.34.0
#> [94] stringi_1.8.7 yaml_2.3.12 lazyeval_0.2.2
#> [97] evaluate_1.0.5 codetools_0.2-20 MsCoreUtils_1.22.1
#> [100] tibble_3.3.1 BiocManager_1.30.27 cli_3.6.5
#> [103] affyio_1.80.0 Rcpp_1.1.1 MassSpecWavelet_1.76.0
#> [106] dbplyr_2.5.1 png_0.1-8 XML_3.99-0.20
#> [109] parallel_4.5.2 ggplot2_4.0.2 blob_1.3.0
#> [112] prettyunits_1.2.0 AnnotationFilter_1.34.0 bitops_1.0-9
#> [115] MsFeatures_1.18.0 scales_1.4.0 affy_1.88.0
#> [118] ncdf4_1.24 purrr_1.2.1 crayon_1.5.3
#> [121] rlang_1.1.7 KEGGREST_1.50.0 vsn_3.78.1