,
Thomas Burger [aut, ctb] | Title: | Handle Ambiguity of Protein Identifications from Shotgun Proteomics |
|---|---|
| Description: | In shotgun proteomics, shared peptides (i.e., peptides that might originate from different proteins sharing homology, from different proteoforms due to alternative mRNA splicing, post-translational modifications, proteolytic cleavages, and/or allelic variants) represent a major source of ambiguity in protein identifications. The 'net4pg' package allows to assess and handle ambiguity of protein identifications. It implements methods for two main applications. First, it allows to represent and quantify ambiguity of protein identifications by means of graph connected components (CCs). In graph theory, CCs are defined as the largest subgraphs in which any two vertices are connected to each other by a path and not connected to any other of the vertices in the supergraph. Here, proteins sharing one or more peptides are thus gathered in the same CC (multi-protein CC), while unambiguous protein identifications constitute CCs with a single protein vertex (single-protein CCs). Therefore, the proportion of single-protein CCs and the size of multi-protein CCs can be used to measure the level of ambiguity of protein identifications. The package implements a strategy to efficiently calculate graph connected components on large datasets and allows to visually inspect them. Secondly, the 'net4pg' package allows to exploit the increasing availability of matched transcriptomic and proteomic datasets to reduce ambiguity of protein identifications. More precisely, it implement a transcriptome-based filtering strategy fundamentally consisting in the removal of those proteins whose corresponding transcript is not expressed in the sample-matched transcriptome. The underlying assumption is that, according to the central dogma of biology, there can be no proteins without the corresponding transcript. Most importantly, the package allows to visually inspect the effect of the filtering on protein identifications and quantify ambiguity before and after filtering by means of graph connected components. As such, it constitutes a reproducible and transparent method to exploit transcriptome information to enhance protein identifications. All methods implemented in the 'net4pg' package are fully described in Fancello and Burger (2022) <doi:10.1186/s13059-022-02701-2>. |
| Authors: | Laura Fancello [aut, cre] ,
Thomas Burger [aut, ctb] |
| Maintainer: | Laura Fancello <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-10-30 04:00:48 UTC |
| Source: | https://github.com/laurafancello/net4pg |
Get peptides and peptide-to-protein mappings for each connected component. For each connected component, first extract its protein members; then, extract all specific and shared peptides mapping on those protein; finally, extract the subset of incidence matrix representing peptide-to-protein mappings.
cc_composition(cc.proteins, incM)cc_composition(cc.proteins, incM)
cc.proteins |
a |
incM |
a |
a list of two elements: i. a list of vectors
(one for each connected component) enumerating peptides mapping on protein
members of each connected component; ii. a list of matrices or
vectors (one for each connected component) representing
peptide-to-protein mappings for each connected component; matrices are used
if multiple peptides identify protein members of that connected component,
vectors if only a single peptide.
Laura Fancello
# Read the tab-delimited file containing the proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected component multProteinCC <- get_cc(adjM) # For each connected component, extract peptides mapping on its protein # members and the subset of the incidence matrix describing peptide-to-protein # mapping cc.peptides.incM <- cc_composition(cc.proteins = multProteinCC$cc , incM = incM)# Read the tab-delimited file containing the proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected component multProteinCC <- get_cc(adjM) # For each connected component, extract peptides mapping on its protein # members and the subset of the incidence matrix describing peptide-to-protein # mapping cc.peptides.incM <- cc_composition(cc.proteins = multProteinCC$cc , incM = incM)
Provides the CC size distribution, which is the number of CCs including a given number of protein members, and the proportion of single- vs multi-protein CCs.
cc_stats(incM, cc.proteins, reducedIncM)cc_stats(incM, cc.proteins, reducedIncM)
incM |
a |
cc.proteins |
a |
reducedIncM |
a |
a list of four outputs: i. a vector containing all
those proteins which belong to single-protein connected component (each
protein is a single-protein connected component); ii. an integer
representing the number of single-protein CCs; iii. an integer
representing the number of multi-protein CCs; iv. a data.frame
describing size distribution of connected components (number of connected
components per number of protein members)
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM) # CCs size and percentage of single-vs multi-protein CCs CCstatsOut <- cc_stats(incM = incM_reduced , cc.proteins = multProteinCC$ccs , reducedIncM = TRUE)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM) # CCs size and percentage of single-vs multi-protein CCs CCstatsOut <- cc_stats(incM = incM_reduced , cc.proteins = multProteinCC$ccs , reducedIncM = TRUE)
Generate an adjacency matrix representing protein-to-protein connections, based on shared peptides. It is generated by cross product of the incidence matrix of peptide-to-protein mappings.
get_adj_matrix(incM)get_adj_matrix(incM)
incM |
a |
a numeric matrix containing the adjacency matrix,
with value >0 or 0 indicating whether or not two proteins are identified by
shared peptide(s)
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename=incM_filename , colnames_filename=colnames_filename , rownames_filename=rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename=incM_filename , colnames_filename=colnames_filename , rownames_filename=rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced)
Build a graph of protein-to-protein connections from adjacency matrix and calculate its connected components.
get_cc(adjM)get_cc(adjM)
adjM |
a |
a list of two elements: i. a graph representing
protein-to-protein connections encoded by the adjacency matrix; ii. a
list of vectors (one for each connected component) enumerating
protein members of each connected component.
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename=incM_filename , colnames_filename=colnames_filename , rownames_filename=rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename=incM_filename , colnames_filename=colnames_filename , rownames_filename=rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM)
Read in input the incidence matrix of peptide-to-protein mappings generated from valid proteomic identifications
peptide_stats(incM)peptide_stats(incM)
incM |
a |
a list of three elements: i. number of shared peptides;
ii. number of specific peptides; iii. percentage of specific peptides
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Calculate percentage of shared vs specific peptides peptideStatsOut <- peptide_stats(incM = incM)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Calculate percentage of shared vs specific peptides peptideStatsOut <- peptide_stats(incM = incM)
Plot a bipartite subgraph representing the connected component to which the user-selected protein belongs. Peptide-to-protein mappings of that protein and of all other proteins belonging to the same CC are represented. The function takes in input a single Ensembl protein identifier (i.e. ENSPXXX for human, ENSMUSPXXX for mouse), it identifies the connected component it belongs to and plots all peptide-to-protein mappings of that connected component.
plot_cc(prot, cc.proteins, cc.subincM, tagProt, tagContam, incM)plot_cc(prot, cc.proteins, cc.subincM, tagProt, tagContam, incM)
prot |
a |
cc.proteins |
a |
cc.subincM |
a |
tagProt |
a |
tagContam |
a |
incM |
a |
a list of four elements: i. CC identifier; ii. protein members of the CC; iii. peptide members of the CC; iv. bipartite subgraph representing peptide-to-protein mapping of that CC (if multi-protein CC)
Laura Fancello
library(igraph) # Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM) # For each connected component, extract peptides mapping on its protein # members and the subset of the incidence matrix describing # peptide-to-protein mappings cc.peptides.incM <- cc_composition(cc.proteins = multProteinCC$cc , incM = incM) # Plot bipartite graph representing peptide-to-protein mappings for the # connected component of the protein of interest (in this toy example protein # "ENSP261"; note that identifiers are not authentic but made up for the # example) subgraphCC <- plot_cc(prot="ENSP261" , cc.proteins=multProteinCC$ccs , cc.subincM=cc.peptides.incM$cc.subincM , tagProt = "ENSP" , tagContam="Contam" , incM=incM) plot.igraph(subgraphCC$g , layout = layout_as_bipartite , edge.width = 1 , edge.arrow.width = 0.3 , vertex.size = 10 , edge.arrow.size = 0.5 , vertex.size2 = 3 , vertex.label.cex = 0.8 , asp = 0.35 , margin = -0.1) + title(paste0("Protein ENSP261 in CC #", subgraphCC$cc_id), line = -1)library(igraph) # Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM) # Generate adjacency matrix describing protein-to-protein mappings adjM <- get_adj_matrix(incM_reduced) # Generate graph of protein-to-protein connections and calculate its # connected components multProteinCC <- get_cc(adjM) # For each connected component, extract peptides mapping on its protein # members and the subset of the incidence matrix describing # peptide-to-protein mappings cc.peptides.incM <- cc_composition(cc.proteins = multProteinCC$cc , incM = incM) # Plot bipartite graph representing peptide-to-protein mappings for the # connected component of the protein of interest (in this toy example protein # "ENSP261"; note that identifiers are not authentic but made up for the # example) subgraphCC <- plot_cc(prot="ENSP261" , cc.proteins=multProteinCC$ccs , cc.subincM=cc.peptides.incM$cc.subincM , tagProt = "ENSP" , tagContam="Contam" , incM=incM) plot.igraph(subgraphCC$g , layout = layout_as_bipartite , edge.width = 1 , edge.arrow.width = 0.3 , vertex.size = 10 , edge.arrow.size = 0.5 , vertex.size2 = 3 , vertex.label.cex = 0.8 , asp = 0.35 , margin = -0.1) + title(paste0("Protein ENSP261 in CC #", subgraphCC$cc_id), line = -1)
Read in input the incidence matrix of peptide-to-protein mappings generated from valid proteomic identifications.
read_inc_matrix(incM_filename, colnames_filename, rownames_filename)read_inc_matrix(incM_filename, colnames_filename, rownames_filename)
incM_filename |
the name of the tab-delimited file containing incidence matrix values; the input incidence matrix must contain along the columns protein identifiers and along the rows peptides; each cell must contain a 1 or 0 value indicating whether or not the peptide maps on the corresponding protein. |
colnames_filename |
name of the file containing incidence matrix column names, which are protein identifiers. The file must contain one identifier per line. Protein identifiers must be in Ensembl format (i.e., ENSPXXXXXXXXXXX for human). |
rownames_filename |
name of the file containing incidence matrix row names, which are peptide identifiers. The file must contain one identifier per line. Peptide identifiers can be in any format (i.e. numeric identifiers, amino acid sequences, ...) |
a logical matrix containing the incidence matrix with
its column and row names (respectively, protein and peptide identifiers)
and 0 or 1 values indicating whether or not the peptide maps on the
corresponding protein.
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file( "extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file( "extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file( "extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename)
Reduce the size of the incidence matrix describing peptide-to-protein mappings to ease downstream analyses. The original incidence matrix is reduced to only contain proteins with at least one shared peptide and all peptides mapping on such proteins. This means that only proteins ambiguously identified are retained, which is the most interesting ones when studying ambiguity of protein identifications.
reduce_inc_matrix(incM)reduce_inc_matrix(incM)
incM |
a |
a logical matrix containing a smaller incidence matrix
(with column and row names respectively reporting protein and peptide
identifiers) and 0 or 1 values indicating whether or not the peptide maps on
the corresponding protein. Only proteins with at least one shared peptide and
all peptides mapping on such protein are reported in such reduced incidence
matrix.
Laura Fancello
# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM)# Read the tab-delimited file containing he proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Only retain proteins with at least one shared peptide and all peptides # mapping on such proteins. incM_reduced <- reduce_inc_matrix(incM)
Implement the transcriptome-informed post-hoc filtering strategy. This strategy aims to reduce the ambiguity of protein identifications by exploiting sample-matched transcriptome information, when available. First, it takes in input the set of transcripts expressed in the sample-matched transcriptome (reported using the transcript identifier in Ensembl format, i.e., ENSTXXXX for human) and removes from proteomic identifications: i. all proteins with no expressed transcripts and peptides exclusively mapping on removed proteins ("all"); or ii. only those exclusively identified by shared peptides and peptides exclusively mapping on removed proteins ("sharedOnly"); or iii. only those exclusively identified by shared peptides, whose peptides are shared with at least one protein with expressed transcript, so they are not to be removed ("sharedNoRemove")
transcriptome_filter( incM, exprTranscriptsFile, proteinToTranscriptFile, tagContam, remove )transcriptome_filter( incM, exprTranscriptsFile, proteinToTranscriptFile, tagContam, remove )
incM |
a |
exprTranscriptsFile |
the name of the file containing the set of transcripts expressed in the sample-matched transcriptome (one per line). Transcript identifiers must be in the Ensembl format (i.e., ENSTXXXXXXXXXXX for human) |
proteinToTranscriptFile |
the name of a tab-delimited file with protein identifiers in the first column and the corresponding transcript identifiers in the second column. Protein and transcript identifiers must be in the Ensembl format (i.e. ENSPXXXXXXXXXXX and ENSTXXXXXXXXXXX for human) |
tagContam |
a |
remove |
|
a matrix representing a filtered incidence matrix of
peptide-to-protein mapping obtained by transcriptome-informed filtering.
Laura Fancello
# Read the tab-delimited file containing the proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Perform transcriptome-informed post-hoc filtering exprTranscriptsFile <- system.file("extdata" , "expressed_transcripts.txt" , package = "net4pg" , mustWork = TRUE) protein2transcriptFile <- system.file("extdata" , "protein_to_transcript" , package = "net4pg" , mustWork = TRUE) incM_filtered <- transcriptome_filter(incM , exprTranscriptsFile = exprTranscriptsFile , proteinToTranscriptFile = protein2transcriptFile , tagContam = "Contam" , remove = "all")# Read the tab-delimited file containing the proteome incidence matrix incM_filename <- system.file("extdata" , "incM_example" , package = "net4pg" , mustWork = TRUE) rownames_filename <- system.file("extdata" , "peptideIDs_incM_example" , package = "net4pg" , mustWork = TRUE) colnames_filename <- system.file("extdata" , "proteinIDs_incM_example" , package = "net4pg" , mustWork = TRUE) incM <- read_inc_matrix(incM_filename = incM_filename , colnames_filename = colnames_filename , rownames_filename = rownames_filename) # Perform transcriptome-informed post-hoc filtering exprTranscriptsFile <- system.file("extdata" , "expressed_transcripts.txt" , package = "net4pg" , mustWork = TRUE) protein2transcriptFile <- system.file("extdata" , "protein_to_transcript" , package = "net4pg" , mustWork = TRUE) incM_filtered <- transcriptome_filter(incM , exprTranscriptsFile = exprTranscriptsFile , proteinToTranscriptFile = protein2transcriptFile , tagContam = "Contam" , remove = "all")