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).
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.
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).
utils::sessionInfo()
#> R version 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] PRONE_0.99.1
#>
#> loaded via a namespace (and not attached):
#> [1] rlang_1.1.4 magrittr_2.0.3
#> [3] GetoptLong_1.0.5 clue_0.3-65
#> [5] matrixStats_1.3.0 compiler_4.4.0
#> [7] png_0.1-8 vctrs_0.6.5
#> [9] reshape2_1.4.4 stringr_1.5.1
#> [11] ProtGenerics_1.37.0 shape_1.4.6.1
#> [13] pkgconfig_2.0.3 crayon_1.5.3
#> [15] fastmap_1.2.0 magick_2.8.3
#> [17] XVector_0.45.0 labeling_0.4.3
#> [19] utf8_1.2.4 rmarkdown_2.27
#> [21] UCSC.utils_1.1.0 preprocessCore_1.67.0
#> [23] purrr_1.0.2 xfun_0.45
#> [25] MultiAssayExperiment_1.31.3 zlibbioc_1.51.1
#> [27] cachem_1.1.0 GenomeInfoDb_1.41.1
#> [29] jsonlite_1.8.8 highr_0.11
#> [31] DelayedArray_0.31.3 BiocParallel_1.39.0
#> [33] parallel_4.4.0 cluster_2.1.6
#> [35] R6_2.5.1 RColorBrewer_1.1-3
#> [37] bslib_0.7.0 stringi_1.8.4
#> [39] ComplexUpset_1.3.3 limma_3.61.2
#> [41] GenomicRanges_1.57.1 jquerylib_0.1.4
#> [43] Rcpp_1.0.12 SummarizedExperiment_1.35.1
#> [45] iterators_1.0.14 knitr_1.47
#> [47] IRanges_2.39.0 splines_4.4.0
#> [49] Matrix_1.7-0 igraph_2.0.3
#> [51] tidyselect_1.2.1 abind_1.4-5
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#> [57] affy_1.83.0 lattice_0.22-6
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#> [61] withr_3.0.0 Biobase_2.65.0
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#> [65] circlize_0.4.16 pillar_1.9.0
#> [67] affyio_1.75.0 BiocManager_1.30.23
#> [69] MatrixGenerics_1.17.0 DT_0.33
#> [71] foreach_1.5.2 stats4_4.4.0
#> [73] MSnbase_2.31.1 MALDIquant_1.22.2
#> [75] ncdf4_1.22 generics_0.1.3
#> [77] S4Vectors_0.43.0 ggplot2_3.5.1
#> [79] munsell_0.5.1 scales_1.3.0
#> [81] glue_1.7.0 lazyeval_0.2.2
#> [83] tools_4.4.0 data.table_1.15.4
#> [85] mzID_1.43.0 QFeatures_1.15.1
#> [87] vsn_3.73.0 mzR_2.39.0
#> [89] XML_3.99-0.17 Cairo_1.6-2
#> [91] grid_4.4.0 impute_1.79.0
#> [93] tidyr_1.3.1 crosstalk_1.2.1
#> [95] MsCoreUtils_1.17.0 colorspace_2.1-0
#> [97] patchwork_1.2.0 GenomeInfoDbData_1.2.12
#> [99] PSMatch_1.9.0 cli_3.6.3
#> [101] fansi_1.0.6 S4Arrays_1.5.1
#> [103] ComplexHeatmap_2.21.0 dplyr_1.1.4
#> [105] AnnotationFilter_1.29.0 pcaMethods_1.97.0
#> [107] gtable_0.3.5 sass_0.4.9
#> [109] digest_0.6.36 BiocGenerics_0.51.0
#> [111] SparseArray_1.5.10 htmlwidgets_1.6.4
#> [113] rjson_0.2.21 farver_2.1.2
#> [115] htmltools_0.5.8.1 lifecycle_1.0.4
#> [117] httr_1.4.7 GlobalOptions_0.1.2
#> [119] statmod_1.5.0 gridtext_0.1.5
#> [121] MASS_7.3-61
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.