Load Data (TMT)
Here, we are directly working with the SummarizedExperiment data. For more information on how to create the SummarizedExperiment from a proteomics data set, please refer to the “Get Started” vignette.
The example TMT data set originates from (Biadglegne et al. 2022).
data("tuberculosis_TMT_se")
se <- tuberculosis_TMT_sePreprocessing
As we have seen in the Preprocessing phase, that samples “1.HC_Pool1” and “1.HC_Pool2” have been removed from the data set due to their high amount of missing values (more than 80% of NAs per sample), before imputing the data we will here remove these two samples.
se <- remove_samples_manually(se, "Label", c("1.HC_Pool1", "1.HC_Pool2"))
#> 2 samples removed.Impute Data
Currently, there is only a mixed imputation method available in
PRONE: k-nearest neighbor imputation for proteins with missing values at
random and a left-shifted Gaussian distribution for proteins with
missing values not at random. Imputation can be performed on a selection
of normalized data sets using the “ain” parameter in the
impute_SE function. The default is to impute all assays
(ain = NULL).
se <- impute_se(se, ain = NULL)
#> Condition of SummarizedExperiment used!
#> All assays of the SummarizedExperiment will be used.
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).
#> Imputing along margin 1 (features/rows).Session Info
utils::sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Sonoma 14.4
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Europe/Berlin
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices datasets utils methods base
#>
#> other attached packages:
#> [1] PRONE_0.99.6
#>
#> loaded via a namespace (and not attached):
#> [1] rlang_1.1.4 magrittr_2.0.3
#> [3] clue_0.3-65 matrixStats_1.3.0
#> [5] compiler_4.4.1 systemfonts_1.1.0
#> [7] vctrs_0.6.5 reshape2_1.4.4
#> [9] stringr_1.5.1 ProtGenerics_1.36.0
#> [11] pkgconfig_2.0.3 crayon_1.5.3
#> [13] fastmap_1.2.0 XVector_0.44.0
#> [15] utf8_1.2.4 rmarkdown_2.27
#> [17] UCSC.utils_1.0.0 preprocessCore_1.66.0
#> [19] ragg_1.3.2 purrr_1.0.2
#> [21] xfun_0.46 MultiAssayExperiment_1.30.3
#> [23] zlibbioc_1.50.0 cachem_1.1.0
#> [25] GenomeInfoDb_1.40.1 jsonlite_1.8.8
#> [27] DelayedArray_0.30.1 BiocParallel_1.38.0
#> [29] parallel_4.4.1 cluster_2.1.6
#> [31] R6_2.5.1 bslib_0.7.0
#> [33] stringi_1.8.4 limma_3.60.4
#> [35] GenomicRanges_1.56.1 jquerylib_0.1.4
#> [37] iterators_1.0.14 Rcpp_1.0.13
#> [39] SummarizedExperiment_1.34.0 knitr_1.48
#> [41] IRanges_2.38.1 Matrix_1.7-0
#> [43] igraph_2.0.3 tidyselect_1.2.1
#> [45] rstudioapi_0.16.0 abind_1.4-5
#> [47] yaml_2.3.10 ggtext_0.1.2
#> [49] doParallel_1.0.17 codetools_0.2-20
#> [51] affy_1.82.0 lattice_0.22-6
#> [53] tibble_3.2.1 plyr_1.8.9
#> [55] withr_3.0.0 Biobase_2.64.0
#> [57] evaluate_0.24.0 desc_1.4.3
#> [59] xml2_1.3.6 pillar_1.9.0
#> [61] affyio_1.74.0 BiocManager_1.30.23
#> [63] MatrixGenerics_1.16.0 renv_1.0.7
#> [65] foreach_1.5.2 stats4_4.4.1
#> [67] MSnbase_2.30.1 MALDIquant_1.22.2
#> [69] ncdf4_1.22 generics_0.1.3
#> [71] S4Vectors_0.42.1 ggplot2_3.5.1
#> [73] munsell_0.5.1 scales_1.3.0
#> [75] glue_1.7.0 lazyeval_0.2.2
#> [77] tools_4.4.1 data.table_1.15.4
#> [79] mzID_1.42.0 QFeatures_1.14.2
#> [81] vsn_3.72.0 mzR_2.38.0
#> [83] fs_1.6.4 XML_3.99-0.17
#> [85] grid_4.4.1 impute_1.78.0
#> [87] tidyr_1.3.1 MsCoreUtils_1.16.0
#> [89] colorspace_2.1-0 GenomeInfoDbData_1.2.12
#> [91] PSMatch_1.8.0 cli_3.6.3
#> [93] textshaping_0.4.0 fansi_1.0.6
#> [95] S4Arrays_1.4.1 dplyr_1.1.4
#> [97] AnnotationFilter_1.28.0 pcaMethods_1.96.0
#> [99] gtable_0.3.5 sass_0.4.9
#> [101] digest_0.6.36 BiocGenerics_0.50.0
#> [103] SparseArray_1.4.8 htmlwidgets_1.6.4
#> [105] htmltools_0.5.8.1 pkgdown_2.1.0
#> [107] lifecycle_1.0.4 httr_1.4.7
#> [109] statmod_1.5.0 gridtext_0.1.5
#> [111] MASS_7.3-61References
Biadglegne, Fantahun, Johannes R. Schmidt, Kathrin M. Engel, Jörg
Lehmann, Robert T. Lehmann, Anja Reinert, Brigitte König, Jürgen
Schiller, Stefan Kalkhof, and Ulrich Sack. 2022. “Mycobacterium
Tuberculosis Affects Protein and Lipid Content of Circulating Exosomes
in Infected Patients Depending on Tuberculosis Disease State.”
Biomedicines 10 (4): 783. https://doi.org/10.3390/biomedicines10040783.