Guillaume Voisinne 2019 - 03 - 19
Analysis of affinity purification/tandem MS data with R
- flexible : no strict input format
- publication-ready figures
More info on the website
The package is available from github:
devtools::install_github("VoisinneG/InteRact")
A shiny based GUI can be accessed here
Load example protein group dataset and run InteRact:
data("proteinGroups_Cbl")
names(proteinGroups_Cbl)[1:10]## [1] "Protein.IDs" "Majority.protein.IDs"
## [3] "Peptide.counts..all." "Peptide.counts..razor.unique."
## [5] "Peptide.counts..unique." "Protein.names"
## [7] "Gene.names" "Fasta.headers"
## [9] "Number.of.proteins" "Peptides"
res <- InteRact(proteinGroups_Cbl, bait_gene_name = "Cbl")## Contaminant proteins discarded
## Proteins with no gene name available discarded
## Number of theoretically observable peptides unavailable : used MW instead
## Merge protein groups associated to the same gene name (sum of intensities)
## Rescale median intensity across conditions
## Replace missing values and perform interactome analysis for 1 replicates
## Nrep=1
## Averaging 1 interactomes
Identify specific interactors
res <- identify_interactors(res,
p_val_thresh = 0.001,
fold_change_thresh = 3,
n_success_min = 1,
consecutive_success = TRUE)
print(res$interactor)## [1] "Cbl" "Mccc1" "Sh3kbp1" "Ubash3a" "Crkl" "Pik3r1"
## [7] "Ywhah" "Ywhag" "Pik3ca" "Pik3r2" "Inpp5d" "StrepTag"
## [13] "Pik3cd" "Grap" "Sdha" "Tbce" "Pccb" "Rbm25"
## [19] "Myl6" "Unc13d"
Show summary table
sum_tbl <- summary_table(res)
head(sum_tbl[, c("bait", "names", "max_fold_change" ,"max_stoichio", "is_interactor")])## bait names max_fold_change max_stoichio is_interactor
## 1 Cbl Cbl 4309.24604 1.0000000 1
## 2 Cbl StrepTag 1095.88799 1.8517938 1
## 3 Cbl Pccb 22.03694 1.2265462 1
## 4 Cbl Crkl 1342.62339 0.6320675 1
## 5 Cbl Ywhah 124.87974 0.2295337 1
## 6 Cbl Ywhag 150.97868 0.2230696 1