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Bayesian linear mixed-effects model for protein-level quantification in proteomics
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README.md

BayesProt v1.2.0

Bayesian linear mixed-effects model for protein-level quantification in proteomics. Currently works with PSM output from ProteinPilot, ProteomeDiscoverer and MSstats.

Installation

install.packages("devtools")
library(devtools)
install_github("biospi/bayesprot", dependencies = T)

Usage

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:

todo

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

Fraction Run
1 A
2 A
3 A
4 B
5 B
6 B
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")
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