--- title: "Retrieving Results from Sirius" output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Retrieving Results from Sirius} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignettePackage{RuSirius} %\VignetteDepends{RSirius, RuSirius} --- ``` r library(RSirius) library(RuSirius) ``` ## Introduction **Note**: this vignette is [**pre-computed**](https://ropensci.org/blog/2019/12/08/precompute-vignettes/). See the session info for information on packages used and the date the vignette was rendered. The vignette requires a running [Sirius](https://bio.informatik.uni-jena.de/software/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 feature - `results()` - Detailed results with multiple candidates ## Connecting to a Project First, connect to an existing Sirius project that has completed computations: ``` r srs <- Sirius(projectId = "my_analysis", path = getwd(), port = 9999) #> Error in `Sirius()`: #> ! unused argument (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 ``` r # 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 found ``` Key columns include `confidenceApproxMatch` (overall confidence for the feature) and `confidenceExactMatch` (confidence for the top hit). ### Structure Database Summary ``` r # Top structure hits summary_structure <- summary(srs, result.type = "structure") #> Error: #> ! object 'srs' not found ``` ### De Novo Structure Summary ``` r # Top MSNovelist predictions summary_denovo <- summary(srs, result.type = "deNovo") #> Error: #> ! object 'srs' not found ``` ### Spectral Library Match Summary ``` r # Best spectral matches summary_spectral <- summary(srs, result.type = "spectralDbMatch") #> Error: #> ! object 'srs' not found ``` ## Detailed Results with `results()` The `results()` function returns multiple candidates per feature, giving you more options to explore. ### Formula Candidates ``` r # 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 ``` ### Structure Database Results ``` r # Get structure candidates for each formula structures <- results(srs, result.type = "structureDb", topFormula = 3, topStructure = 5, return.type = "data.frame") #> Error: #> ! object 'srs' not found ``` Key columns: `inchiKey`, `smiles`, `structureName`, `csiScore`. ### Compound Class Predictions (CANOPUS) ``` r # Get predicted compound classes classes <- results(srs, result.type = "compoundClass", topFormula = 1, return.type = "data.frame") #> Error: #> ! object 'srs' not found ``` Returns ClassyFire and NPC classifications with confidence scores. ### De Novo Structures (MSNovelist) ``` r # Get de novo predicted structures denovo <- results(srs, result.type = "deNovo", topFormula = 1, topStructure = 5, return.type = "data.frame") #> Error: #> ! object 'srs' not found ``` ### Spectral Library Matches ``` r # Get spectral library matches spectral <- results(srs, result.type = "spectralDbMatch", topSpectralMatches = 5, return.type = "data.frame") #> Error: #> ! object 'srs' not found ``` ### Fragmentation Trees ``` r # Get fragmentation tree data fragtrees <- results(srs, result.type = "fragTree", topFormula = 1, return.type = "data.frame") #> Error: #> ! object 'srs' not found ``` ## Filtering by Feature You can retrieve results for specific features only: ``` r # 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 found ``` ## Return Types Results can be returned as a data.frame (default) or as a list: ``` r # As list - useful for per-feature processing results_list <- results(srs, result.type = "formulaId", return.type = "list") #> Error: #> ! object 'srs' not found ``` Access individual features by name: ``` r # 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. ``` r # Get the feature mapping mapFeatures(srs) #> Error: #> ! object 'srs' not found ``` ## Session Info ``` r 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 ```