Run this code below to install MSstatsBioNet from bioconductor
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("MSstatsBioNet")We will be taking a subset of the dataset found in this paper.
The table is the output of the MSstatsPTM function
groupComparisonPTM (filtered down to the columns that are
actually needed)
input = data.table::fread(system.file(
"extdata/garrido-2024.csv",
package = "MSstatsBioNet"
))
head(input)
#> Protein Label log2FC SE DF pvalue
#> <char> <char> <num> <num> <int> <num>
#> 1: P00533_S1039_S1042 t0 vs t1 -0.3200363 0.14994312 9 0.061580951
#> 2: P00533_S1064 t0 vs t1 0.3566531 0.08915347 9 0.003108364
#> 3: P00533_S991_S995 t0 vs t1 -0.1229037 0.10858298 9 0.286937655
#> 4: P00533_T693 t0 vs t1 -0.0233444 0.17724459 9 0.898113182
#> 5: P00533_T693_S695 t0 vs t1 -0.1659957 0.15000754 9 0.297173769
#> 6: P00533_Y1110 t0 vs t1 0.2106324 0.09279031 9 0.049364434
#> adj.pvalue issue
#> <num> <lgcl>
#> 1: 0.28024590 NA
#> 2: 0.06863598 NA
#> 3: 0.57907374 NA
#> 4: 0.96083634 NA
#> 5: 0.58809108 NA
#> 6: 0.25258914 NAFirst, we need to convert the group comparison results to a format
that can be processed by INDRA. We can use the
annotateProteinInfoFromIndra function to obtain these
mappings.
In the below example, we convert uniprot IDs to their corresponding Hgnc IDs. We can also extract other information, such as hgnc gene name and protein function.
library(MSstatsBioNet)
annotated_df = annotateProteinInfoFromIndra(input, "Uniprot")
head(annotated_df)
#> Protein Label log2FC SE DF pvalue
#> <char> <char> <num> <num> <int> <num>
#> 1: P00533_S1039_S1042 t0 vs t1 -0.3200363 0.14994312 9 0.061580951
#> 2: P00533_S1064 t0 vs t1 0.3566531 0.08915347 9 0.003108364
#> 3: P00533_S991_S995 t0 vs t1 -0.1229037 0.10858298 9 0.286937655
#> 4: P00533_T693 t0 vs t1 -0.0233444 0.17724459 9 0.898113182
#> 5: P00533_T693_S695 t0 vs t1 -0.1659957 0.15000754 9 0.297173769
#> 6: P00533_Y1110 t0 vs t1 0.2106324 0.09279031 9 0.049364434
#> adj.pvalue issue GlobalProtein UniprotId EntityNamespace EntityId
#> <num> <lgcl> <char> <char> <char> <char>
#> 1: 0.28024590 NA P00533 P00533 HGNC 3236
#> 2: 0.06863598 NA P00533 P00533 HGNC 3236
#> 3: 0.57907374 NA P00533 P00533 HGNC 3236
#> 4: 0.96083634 NA P00533 P00533 HGNC 3236
#> 5: 0.58809108 NA P00533 P00533 HGNC 3236
#> 6: 0.25258914 NA P00533 P00533 HGNC 3236
#> EntityName IsTranscriptionFactor IsKinase IsPhosphatase
#> <char> <lgcl> <lgcl> <lgcl>
#> 1: EGFR FALSE TRUE FALSE
#> 2: EGFR FALSE TRUE FALSE
#> 3: EGFR FALSE TRUE FALSE
#> 4: EGFR FALSE TRUE FALSE
#> 5: EGFR FALSE TRUE FALSE
#> 6: EGFR FALSE TRUE FALSEThe package provides a function getSubnetworkFromIndra
that retrieves a subnetwork of proteins from the INDRA database based on
differential abundance analysis results. This function may help finding
off target subnetworks.
subnetwork <- getSubnetworkFromIndra(annotated_df, pvalueCutoff = 0.05, statement_types = c("Phosphorylation"), logfc_cutoff = 1, force_include_other = c("HGNC:3236"))
#> Warning in getSubnetworkFromIndra(annotated_df, pvalueCutoff = 0.05, statement_types = c("Phosphorylation"), : NOTICE: This function includes third-party software components
#> that are licensed under the BSD 2-Clause License. Please ensure to
#> include the third-party licensing agreements if redistributing this
#> package or utilizing the results based on this package.
#> See the LICENSE file for more details.
head(subnetwork$nodes)
#> id entityName entityId Site logFC adj.pvalue
#> <char> <char> <char> <char> <num> <num>
#> 1: P00533 EGFR 3236 S1039_S1042 -0.3200363 0.28024590
#> 2: P00533 EGFR 3236 S1064 0.3566531 0.06863598
#> 3: P00533 EGFR 3236 S991_S995 -0.1229037 0.57907374
#> 4: P00533 EGFR 3236 T693 -0.0233444 0.96083634
#> 5: P00533 EGFR 3236 T693_S695 -0.1659957 0.58809108
#> 6: P00533 EGFR 3236 Y1110 0.2106324 0.25258914
head(subnetwork$edges)
#> source target site interaction evidenceCount paperCount
#> 1 Q13480 P00533 <NA> Phosphorylation 2 1
#> 2 P00533 P28482 <NA> Phosphorylation 6 1
#> 3 P00533 Q13480 Y317 Phosphorylation 1 1
#> 4 P00533 Q13480 <NA> Phosphorylation 24 1
#> 5 P00533 Q13480 Y659 Phosphorylation 12 1
#> 6 P28482 Q13480 S454 Phosphorylation 3 1
#> evidenceLink
#> 1 https://db.indra.bio/statements/from_agents?subject=4066@HGNC&object=3236@HGNC&format=html
#> 2 https://db.indra.bio/statements/from_agents?subject=3236@HGNC&object=6871@HGNC&format=html
#> 3 https://db.indra.bio/statements/from_agents?subject=3236@HGNC&object=4066@HGNC&format=html
#> 4 https://db.indra.bio/statements/from_agents?subject=3236@HGNC&object=4066@HGNC&format=html
#> 5 https://db.indra.bio/statements/from_agents?subject=3236@HGNC&object=4066@HGNC&format=html
#> 6 https://db.indra.bio/statements/from_agents?subject=6871@HGNC&object=4066@HGNC&format=html
#> sourceCounts
#> 1 {"sparser": 1, "reach": 1}
#> 2 {"sparser": 2, "reach": 3, "rlimsp": 1}
#> 3 {"rlimsp": 1}
#> 4 {"reach": 14, "sparser": 5, "trips": 1, "bel_lc": 1, "rlimsp": 3}
#> 5 {"pc": 1, "pe": 1, "hprd": 8, "psp": 1, "signor": 1}
#> 6 {"hprd": 2, "signor": 1}
#> stmt_hash
#> 1 -16511170354231919
#> 2 4046712837468004
#> 3 16300905780198239
#> 4 -27721131241182418
#> 5 28792790420132101
#> 6 7148668566491587sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> 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=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [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: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MSstatsConvert_1.23.1 rmarkdown_2.31 shiny_1.13.0
#> [4] MSstatsBioNet_1.5.1 MSstats_4.21.0 BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 viridisLite_0.4.3 dplyr_1.2.1
#> [4] farver_2.1.2 S7_0.2.2 bitops_1.0-9
#> [7] fastmap_1.2.0 lazyeval_0.2.3 promises_1.5.0
#> [10] XML_3.99-0.23 digest_0.6.39 mime_0.13
#> [13] lifecycle_1.0.5 survival_3.8-6 statmod_1.5.2
#> [16] magrittr_2.0.5 compiler_4.6.0 r2r_0.1.2
#> [19] rlang_1.2.0 sass_0.4.10 tools_4.6.0
#> [22] yaml_2.3.12 data.table_1.18.4 knitr_1.51
#> [25] stopwords_2.3 htmlwidgets_1.6.4 curl_7.1.0
#> [28] marray_1.91.0 xml2_1.5.2 RColorBrewer_1.1-3
#> [31] KernSmooth_2.23-26 purrr_1.2.2 sys_3.4.3
#> [34] grid_4.6.0 preprocessCore_1.75.0 caTools_1.18.3
#> [37] xtable_1.8-8 log4r_0.4.4 ggplot2_4.0.3
#> [40] scales_1.4.0 gtools_3.9.5 MASS_7.3-65
#> [43] cli_3.6.6 crayon_1.5.3 reformulas_0.4.4
#> [46] generics_0.1.4 otel_0.2.0 httr_1.4.8
#> [49] minqa_1.2.8 cachem_1.1.0 splines_4.6.0
#> [52] parallel_4.6.0 BiocManager_1.30.27 vctrs_0.7.3
#> [55] boot_1.3-32 Matrix_1.7-5 jsonlite_2.0.0
#> [58] ggrepel_0.9.8 maketools_1.3.2 limma_3.69.2
#> [61] plotly_4.12.0 lgr_0.5.2 tidyr_1.3.2
#> [64] jquerylib_0.1.4 glue_1.8.1 nloptr_2.2.1
#> [67] stringi_1.8.7 gtable_0.3.6 later_1.4.8
#> [70] lme4_2.0-1 mlapi_0.1.1 tibble_3.3.1
#> [73] pillar_1.11.1 htmltools_0.5.9 gplots_3.3.0
#> [76] float_0.3-3 rsparse_0.5.3 R6_2.6.1
#> [79] Rdpack_2.6.6 evaluate_1.0.5 lattice_0.22-9
#> [82] rentrez_1.2.4 rbibutils_2.4.1 backports_1.5.1
#> [85] RhpcBLASctl_0.23-42 memoise_2.0.1 httpuv_1.6.17
#> [88] bslib_0.11.0 text2vec_0.6.6 Rcpp_1.1.1-1.1
#> [91] nlme_3.1-169 checkmate_2.3.4 xfun_0.58
#> [94] buildtools_1.0.0 pkgconfig_2.0.3