mulinkr

The mulinkr package writes QFeatures objects and their assay links to .h5mu files and reads them back into R.

Here we will use the example data feat3 from QFeatures.

library(mulinkr)
data("feat3", package = "QFeatures")
feat3
#> An instance of class QFeatures (type: bulk) with 7 sets:
#> 
#>  [1] psms1: SummarizedExperiment with 7 rows and 2 columns 
#>  [2] psms2: SummarizedExperiment with 8 rows and 2 columns 
#>  [3] psmsall: SummarizedExperiment with 10 rows and 4 columns 
#>  [4] peptides: SummarizedExperiment with 3 rows and 4 columns 
#>  [5] proteins: SummarizedExperiment with 2 rows and 4 columns 
#>  [6] normpeptides: SummarizedExperiment with 3 rows and 4 columns 
#>  [7] normproteins: SummarizedExperiment with 2 rows and 4 columns

We first prepare the object for writing. This makes feature names unique across assays when needed and converts date columns to character.

preparedFeat3 <- prepareQFeatures(feat3)
#> Warning: Prefixed feature row names with assay names because they were not
#> globally unique across the QFeatures object.

Then we write the prepared object to a temporary .h5mu file.

filePath <- tempfile(fileext = ".h5mu")
writeLinkH5MU(preparedFeat3, filePath)

The created .h5mu file can then be used in python or in R.

In python, the file can be read as a mulink object.

import mudata as md
import mulink

mdata = md.read_h5mu("yourFile.h5mu")

print(mdata)

In R, the object can be reimported back as a QFeatures object.

newQFeatures <- readLinkH5MU(filePath)
#> Warning: sampleMap[['assay']] coerced with as.factor()
newQFeatures
#> An instance of class QFeatures (type: bulk) with 7 sets:
#> 
#>  [1] psms1: SummarizedExperiment with 7 rows and 2 columns 
#>  [2] psms2: SummarizedExperiment with 8 rows and 2 columns 
#>  [3] psmsall: SummarizedExperiment with 10 rows and 4 columns 
#>  [4] peptides: SummarizedExperiment with 3 rows and 4 columns 
#>  [5] proteins: SummarizedExperiment with 2 rows and 4 columns 
#>  [6] normpeptides: SummarizedExperiment with 3 rows and 4 columns 
#>  [7] normproteins: SummarizedExperiment with 2 rows and 4 columns

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 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-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mulinkr_0.99.1   BiocStyle_2.40.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1            dplyr_1.2.1                
#>  [3] farver_2.1.2                S7_0.2.2                   
#>  [5] fastmap_1.2.0               SingleCellExperiment_1.34.0
#>  [7] lazyeval_0.2.3              nipals_1.0                 
#>  [9] digest_0.6.39               lifecycle_1.0.5            
#> [11] cluster_2.1.8.2             ProtGenerics_1.44.0        
#> [13] magrittr_2.0.5              compiler_4.6.1             
#> [15] rlang_1.3.0                 sass_0.4.10                
#> [17] tools_4.6.1                 igraph_2.3.3               
#> [19] yaml_2.3.12                 knitr_1.51                 
#> [21] S4Arrays_1.12.0             htmlwidgets_1.6.4          
#> [23] DelayedArray_0.38.2         plyr_1.8.9                 
#> [25] RColorBrewer_1.1-3          abind_1.4-8                
#> [27] purrr_1.2.2                 BiocGenerics_0.58.1        
#> [29] desc_1.4.3                  grid_4.6.1                 
#> [31] stats4_4.6.1                Rhdf5lib_2.0.0             
#> [33] ggplot2_4.0.3               scales_1.4.0               
#> [35] MASS_7.3-65                 MultiAssayExperiment_1.38.0
#> [37] SummarizedExperiment_1.42.0 cli_3.6.6                  
#> [39] rmarkdown_2.32              ragg_1.5.2                 
#> [41] generics_0.1.4              metapod_1.20.0             
#> [43] otel_0.2.0                  scp_1.22.0                 
#> [45] reshape2_1.4.5              BiocBaseUtils_1.14.2       
#> [47] cachem_1.1.0                rhdf5_2.56.0               
#> [49] stringr_1.6.0               AnnotationFilter_1.36.0    
#> [51] BiocManager_1.30.27         XVector_0.52.0             
#> [53] matrixStats_1.5.0           vctrs_0.7.3                
#> [55] Matrix_1.7-5                slam_0.1-56                
#> [57] jsonlite_2.0.0              bookdown_0.48              
#> [59] IRanges_2.46.0              S4Vectors_0.50.2           
#> [61] IHW_1.40.0                  ggrepel_0.9.8              
#> [63] clue_0.3-68                 systemfonts_1.3.2          
#> [65] jquerylib_0.1.4             tidyr_1.3.2                
#> [67] glue_1.8.1                  pkgdown_2.2.1              
#> [69] QFeatures_1.22.0            stringi_1.8.9              
#> [71] gtable_0.3.6                GenomicRanges_1.64.0       
#> [73] lpsymphony_1.40.0           tibble_3.3.1               
#> [75] pillar_1.11.1               htmltools_0.5.9            
#> [77] Seqinfo_1.2.0               rhdf5filters_1.24.1        
#> [79] MuData_1.16.0               R6_2.6.1                   
#> [81] textshaping_1.0.5           evaluate_1.0.5             
#> [83] lattice_0.22-9              Biobase_2.72.0             
#> [85] bslib_0.12.0                Rcpp_1.1.2                 
#> [87] fdrtool_1.2.18              SparseArray_1.12.2         
#> [89] xfun_0.60                   MsCoreUtils_1.24.0         
#> [91] fs_2.1.0                    MatrixGenerics_1.24.0      
#> [93] pkgconfig_2.0.3

Léopold Guyot

5 September 2026