Bayesian linear mixed-effects model for protein-level quantification in proteomics. Currently works with PSM output from ProteinPilot, ProteomeDiscoverer and MSstats.
install.packages("devtools") library(devtools) install_github("biospi/bayesprot", dependencies = T)
Firstly, you need to use an 'import' function to convert from the upstream tool format to BayesProt's standardised data.frame format. Then you use the 'bayesprot' function output a submission zip file for transfer to your HPC cluster.
Importing from MSstats
BayesProt can import data prepared by MSstats. For example:
library(bayesprot) library(data.table) dd.input <- fread("E1508100903_6600_32Fixed.tsv", check.names=T) dd <- importMSstats(dd.input) bayesprot(dd, id = "SWATHBenchmark")
Importing from ProteomeDiscoverer (TMT)
If you are analysing more than one 6 or 12-plex, please ensure you run all through ProteomeDiscoverer at the same time. BayesProt then takes in the PSM output. For example:
Importing from Protein Pilot (iTraq)
If you are analysing more than one 4 or 8-plex, please ensure you run all through Protein Pilot at the same time. Since Protein Pilot doesn't understand datasets merged in this way, you also need to supply a data.frame which associates each fraction with each run. For example
library(readxl) library(bayesprot) fractions <- read_excel("fractions.xlsx") dd <- importProteinPilot("20140910_NR_JXU_GRP 1 SET 1-2-3_combined_PeptideSummary.txt", fractions) bayesprot(dd, id = "JXU1")