lfqevalueall: Conduct LFQ and assess performance of all possible LFQ...

In idrblab/EVALFQ: R Package for Evaluating Label-Free Proteome Quantification

Conduct LFQ and assess performance of all possible LFQ workflows.

Description

The EVALFQ enables the label-free quantification of proteomic data and the performance assessment of each LFQ workflow from multiple perspectives. Moreover, it provides the unique function of ranking all possible LFQ workflows (>3,000 random combinations of transformation, normalization and imputation methods) based on their performances. All in all, this tool makes the performance assessment of whole LFQ workflow possible (collectively assessed by five well-established criteria with distinct underlying theories) and gives the ranking results of all possible workflows based on the criteria preferred and selected by the users. For function definitions and descriptions please use "??EVALFQ" command in R.

Usage

lfqevalueall(data_q, assum_a="Y", assum_b="Y",
assum_c="Y", Ca="1", Cb="1", Cc="1", Cd="1")

Arguments

data_q	This input file should be numeric type except the first and second column containing the names and label (control or case) of the studied samples, respectively. The intensity data should be provided in this input file with the following order: samples in row and proteins/peptides in column. Missing value (NA) of protein intensity are allowed.
assum_a	all proteins were assumed to be equally important.The authors will be asked to input a letter “Y” to indicate the corresponding assumption is held for the studied dataset and a letter “N” to denote the opposite.
assum_b	The level of protein abundance was assumed to be constant among all samples. The authors will be asked to input a letter “Y” to indicate the corresponding assumption is held for the studied dataset and a letter “N” to denote the opposite.
assum_c	The intensities of the vast majority of the proteins were assumed to be unchanged under the studied conditions. The authors will be asked to input a letter “Y” to indicate the corresponding assumption is held for the studied dataset and a letter “N” to denote the opposite.
Ca	Criterion (a): precision of LFQ based on the proteomes among replicates (Proteomics. 15:3140-51, 2015). If set 1, the user chooses to assess LFQ workflows using Criterion (a). If set 0, the user excludes Criterion (a) from performance assessment. The default setting of this value is “1”.
Cb	Criterion (b): classification ability of LFQ between distinct sample groups (Nat Biotechnol. 28:83-9, 2010). If set 1, the user chooses to assess LFQ workflows using Criterion (b). If set 0, the user excludes Criterion (b) from performance assessment. The default setting of this value is “1”.
Cc	Criterion (c): differential expression analysis by reproducibility-optimization (Nat Biotechnol. 32:896-902, 2014). If set 1, the user chooses to assess LFQ workflows using Criterion (c). If set 0, the user excludes Criterion (c) from performance assessment. The default setting of this value is “1”.
Cd	Criterion (d): reproducibility of the identified protein markers among different datasets (Mol Biosyst. 11:1235-40, 2015). If set 1, the user chooses to assess LFQ workflows using Criterion (d). If set 0, the user excludes Criterion (d) from performance assessment. The default setting of this value is “1”.

Value

preprocessed matrix

Examples

library(EVALFQ)
data_q <- PrepareInuputFiles(acquisitionmethods=2,
rawdataset = "MaxQuant_proteinGroups_LFQ.txt", lable = "MaxQuant_LFQ_Label.txt")
lfqevalueall(data_q = data_q,
assum_a="Y", assum_b="Y", assum_c="Y", Ca="1", Cb="1", Cc="1", Cd="1")

idrblab/EVALFQ documentation built on Sept. 29, 2022, 6:34 p.m.