Note that we will use the complete PPI and BioPlanet edgefiles available in “/Data_repository_path/” for this vignette. Lucy todo: these can come with the package

This tutorial will load and create networks from the breast cancer cohort (BRCA, N = 122), produced by

Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy Krug, K., Jaehnig, E. J., Satpathy, S., Blumenberg, L., Karpova, A., Anurag, M., et al. Cell 183, 1436PTMsToPathways::name6.e31

And downloaded from Supplemental data S2 from

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data Schraink, T., Blumenberg, L., Hussey, G., George, S., Miller, B., Mathew, N., Gonzalez-Robles, T.J., Sviderskiy, V., Papagiannakopoulos, T., Possemato, R., et al. Mol Cell Proteomics 22, 100596. 10.1016/j.mcpro.2023.100596

Data preprocessing

First, let’s load the PTMsToPathways package so its functions are available:

library(PTMsToPathways)

The BRCA data table described above is provided with the PTMsToPathways package and and can be read in as follows:

file_path <- system.file("extdata", "PhosphoDiscoData_mmc9.txt", package = "PTMsToPathways")
newphos <- utils::read.table(file_path, header = TRUE,
                               stringsAsFactors = FALSE, sep = "\t", comment.char = "#",
                               na.strings = "", quote = "", fill = TRUE)

It has 4237 rows and 124 columns representing 4237 phosphosites and 122 samples with phosphoproteomic data.

dim(newphos)
>> [1] 4237  124

The first two columns are gene_symbol and variable_sites_names which we will use to create row names for the PTM table to match the expected input format for the PTMsToPathways functions. The Raw Data Processing vignette gives another example of how to process raw data tables to create the expected input format for the PTMsToPathways functions. Here are the first few rows and columns of the data frame:

head(newphos[, 1:5])
>>   gene_symbol variable_sites_names X11BR047 X11BR043 X11BR049
>> 1       ACTN1                 S6s    0.2936  -1.9643  -0.9256
>> 2       BAZ2A              S1783s   -1.2947   1.5820  -0.4770
>> 3       BRCA2                S93s   -1.5739   0.5702  -1.1614
>> 4    C12orf45               S178s   -3.6418   0.3764  -1.4522
>> 5    C17orf49                S96s   -1.1685   0.2572   0.1649
>> 6    C6orf106               S215s   -2.7001   0.2895  -2.7325

Now we will process the first two columns to create row names for the PTM table. We extract the amino acid and the site number from the variable_sites_names column.

 newphos$Amino.Acid <- sapply(newphos$variable_sites_names, function(x) substring (x, 1, 1))
 newphos$Site <- trimws(substring(newphos$variable_sites_names, 2))
 newphos$Site <- sub("[a-z]$", "", newphos$Site)
 head(newphos[, c("gene_symbol", "variable_sites_names", "Amino.Acid", "Site")])
>>   gene_symbol variable_sites_names Amino.Acid Site
>> 1       ACTN1                 S6s           S    6
>> 2       BAZ2A              S1783s           S 1783
>> 3       BRCA2                S93s           S   93
>> 4    C12orf45               S178s           S  178
>> 5    C17orf49                S96s           S   96
>> 6    C6orf106               S215s           S  215

Now use the PTMsToPathways function name.peptide to create peptide names for the row names of the PTM table.

 newphos$Peptide.Name <- mapply(
   name.peptide, genes = newphos$gene_symbol,
   sites =  newphos$Site, aa = newphos$Amino.Acid)

Create ptmtable with PTMs as rows and samples as columns for use in the next steps, and remove the columns we used to create the row names.

phosdata <- newphos[, 3:ncol(newphos), ]
rownames(phosdata) <- newphos$Peptide.Name
phosdata <- phosdata[, !(names(phosdata) %in% c("gene_symbol", "variable_sites_names", "Amino.Acid", "Site", "Peptide.Name"))]
ptmtable <- phosdata
head(ptmtable[, 1:5])
>>                 X11BR047 X11BR043 X11BR049 X11BR023 X18BR010
>> ACTN1 p S6        0.2936  -1.9643  -0.9256  -2.6409  -0.8071
>> BAZ2A p S1783    -1.2947   1.5820  -0.4770   1.5900  -1.0283
>> BRCA2 p S93      -1.5739   0.5702  -1.1614   0.5241  -0.3049
>> C12orf45 p S178  -3.6418   0.3764  -1.4522   0.1373  -0.2151
>> C17orf49 p S96   -1.1685   0.2572   0.1649   1.8283  -1.0399
>> C6orf106 p S215  -2.7001   0.2895  -2.7325   1.6096  -1.7117

BRCANetworks

Nagashree Avabhrath, Mikhail Ukrainetz, Madison Moffett, Grant Smith, Lucia Williams, Mark Grimes

2026-04-10

Data preprocessing

Create Clusters and Co-Cluster Correlation Networks (CCCNs)

Build Cluster Filtered Networks (CFNs) and Pathway Crosstalk Networks (PCNs)