saint_permF(file_baittable, file_inttable, prottable,
norm = c("none", "sumtotal", "upperquartile", "DESeq",
"TMM", "quantile"),
Filter = TRUE,
filter.method = c("IQR", "overallVar", "noVar"),
var.cutoff = NA, limit = 0, intern.norm = FALSE,
saint.options = "2000 10000 0 1 0")norm="none", no normalization of the data is performed.TRUE)."IQR", "overallVar" or "noVar", only used when Filter=TRUE.NA. Cutoff for filtering the data, defined by a quantile or shortest-interval (=NA, Default), only used when Filter=TRUE.TRUE, normalization is repeated on the filtered data (Default FALSE).Different .txt and .xls files are generated, enabling the user to follow the different intermediate results:
Pre-processing comprises normalization and filtering of the data:
Here, it can be chosen from five different normalization methods, adapted from microarray and RNA-seq analysis to AP-MS data. For further details see norm.inttable.
The filter consists of a biological filter and a statistical variance filter and aims to remove obvious contaminants from further analysis.
If filter.method="noVar", only the biological filter is conducted.
Both are conducted, if filter.method="IQR", here the variance is calculated by the inter-quartile-range, or if filter.method="overallVar", here the variance is calculated across all samples.
The var.cutoff defines the fraction of proteins with the lowest overall variance, which are considered as contaminants and are removed.
var.cutoff=NA refers to a cutoff defined by the mean of the shortest intervall containing 50% of the data (default). Alternatively, a quantile can be set as cutoff, e.g. a cutoff of 0.5 filters 50% of the data showing the smallest overall variance or IQR. see also varFilter
The parameter limit assures, that filtering results in a number of proteins above the number of expected true interaction proteins.
The corresponding parameters in SAINT [nburn][niter][lowMode][minFold]
[normalize] are set as recommended by SAINT. Further details on the parameter setting can be found in Choi et.al.(Current Protocols in Bioinformatics 2012).
Choi H, Liu G, Mellacheruvu D, et al. Analyzing Protein-Protein Interactions from Affinity Purification-Mass Spectrometry Data with SAINT. Current Protocols in Bioinformatics 2012. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology 2010.
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 2010.
Bolstad BM, Irizarry RA, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003.
Westfall PH, Young SS. Resampling-based multiple testing: examples and methods for p-value adjustment. 1993.
Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-throughput experiments. Proceedings of the National Academy of Sciences 2010.
#input dara
baitfile <- system.file("extdata", "baittab.txt", package="apmsWAPP")
intfile <- system.file("extdata", "inttable.txt", package="apmsWAPP")
protfile <- system.file("extdata", "prottable.txt", package="apmsWAPP")
# To run this example, a linux environment is required and SAINT needs
# to be installed!
# Important: Define a working directory for storage of the resulting
# files
# Pre-processing: quantile normalization and filtering
# Workflow call:
# saint_permF(baitfile,intfile,protfile, norm="quantile", Filter=TRUE,
# filter.method="overallVar", var.cutoff=0.3, intern.norm=FALSE)Run the code above in your browser using DataLab