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:
summary() - Quick overview of top results per
featureresults() - Detailed results with multiple
candidatesFirst, connect to an existing Sirius project that has completed computations:
summary()The summary() function provides the top annotation for
each feature. This is useful for a quick overview of your results.
# 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).
results()The results() function returns multiple candidates per
feature, giving you more options to explore.
# 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 found# 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.
# 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.
You can retrieve results for specific features only:
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:
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.
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