This guide serves as an introduction to the PRONE R package, designed to facilitate the preparation of your data set for utilization of the PRONE package’s functionalities. It begins by delineating the underlying data structure essential for the application of the package, followed by a brief description of how to apply different normalization techniques to your data. Additionally, this tutorial shows how to export the normalized data at the end.
Beyond the scope of this introductory tutorial, PRONE encompassess a broad spectrum of functionalities, ranging from preprocessing steps, imputation, normalization and evaluation of the performance of different normalization techniques, to the identification of differentially expressed proteins. These functionalities are detailed in dedicated vignettes, offering detailed insights and instructions for leveraging full capabilities of the PRONE package:
Furthermore, PRONE provides additional functionalities for the analysis of spike-in data sets, which are detailed in the following vignette:
PRONE uses the SummarizedExperiment class as storage for protein intensities and meta data information on the proteomics data set. Hence, before being able to execute the functionalities of PRONE, the data needs to be saved accordingly. For this, the load_data()
function was implemented and requires different parameters which are explained in the following:
If you have a TMT data set with samples being measured in different batches than you have to specify the batch information. If reference samples were included in each batch, then additionally specify the samples names of the reference samples.
Attention: You need to make sure that the sample names are saved in a column named “Column” in the meta-data table and are named accordingly in the protein intensity table.
The example TMT data set originates from (Biadglegne et al. 2022).
data_path <- readPRONE_example("tuberculosis_TMT_proteinGroups.txt")
md_path <- readPRONE_example("tuberculosis_TMT_metadata.txt")
data <- read.csv(data_path, sep ="\t")
md <- read.csv(md_path, sep = "\t")
md$Column <- stringr::str_replace_all(md$Column, " ", ".")
ref_samples <- md[md$Group == "Common.reference",]$Column
se <- load_data(data, md, protein_column = "Protein.IDs", gene_column = "Gene.names", ref_samples = ref_samples, batch_column = "Pool", condition_column = "Group", label_column = "Label")
The example data set originates from (Vehmas et al. 2016). This data set is used for the subsequent examples in this tutorial.
data_path <- readPRONE_example("MouseP450_LFQ_protein_table.csv")
data <- read.csv(data_path, row.names = 1)
# Create metadata (since it was not given)
md <- data.frame(Column = colnames(data)[seq_len((ncol(data)-2))])
md$Animal <- sapply(strsplit(as.character(md$Column),"_"), "[", 1)
md$Condition <- sapply(strsplit(as.character(md$Column),"_"), "[", 2)
se <- load_data(data, md, protein_column = "Accession", gene_column = NULL, ref_samples = NULL, batch_column = NULL, condition_column = "Condition", label_column = NULL)
The SummarizedExperiment object contains the protein intensities as “assay”, the meta-data table as “colData”, and additional columns for instance resulting from MaxQuant as “rowData”. Furthermore, information on the different columns, for instance, which columns contains the batch information, can be found in the “metadata” slot.
se
#> class: SummarizedExperiment
#> dim: 1499 12
#> metadata(4): condition batch refs label
#> assays(2): raw log2
#> rownames(1499): 1 2 ... 1498 1499
#> rowData names(3): Protein.IDs Peptides.used.for.quantitation IDs
#> colnames(12): X2206_WT X2208_WT ... X2285_Arom X2253_Arom
#> colData names(3): Column Animal Condition
The different data types can be accessed by using the assays()
function. Currently, only the raw data and log2-transformed data are stored in the SummarizedExperiment object.
As already mentioned in the introduction section, many functionalities are available in PRONE. All these functionalities are mainly based on the SummarizedExperiment object.
In this tutorial, we will only perform simple normalization of the data using median and LoessF normalization.
The normalized intensities will be saved as additional assays in the SummarizedExperiment object.
Again, more information on the individual processes can be find in dedicated vignettes.
Finally, you can easily download the normalized data by using the export_data()
function. This function will save the specified assays as CSV files and the SummarizedExperiment object as an RDS file in a specified output directory. Make sure that the output directory exists.
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)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> 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
#> [53] yaml_2.3.8 doParallel_1.0.17
#> [55] ggtext_0.1.2 codetools_0.2-20
#> [57] affy_1.83.0 lattice_0.22-6
#> [59] tibble_3.2.1 plyr_1.8.9
#> [61] withr_3.0.0 Biobase_2.65.0
#> [63] evaluate_0.24.0 xml2_1.3.6
#> [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 NormalyzerDE_1.23.0
#> [119] GlobalOptions_0.1.2 statmod_1.5.0
#> [121] gridtext_0.1.5 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.
Vehmas, Anni P., Marion Adam, Teemu D. Laajala, Gabi Kastenmüller, Cornelia Prehn, Jan Rozman, Claes Ohlsson, et al. 2016. “Liver Lipid Metabolism Is Altered by Increased Circulating Estrogen to Androgen Ratio in Male Mouse.” Journal of Proteomics 133 (February): 66–75. https://doi.org/10.1016/j.jprot.2015.12.009.