lfqspikedall: Conduct LFQ and assess performance by collectively...

In JianboFu0406/EVALFQ111: R Package for Evaluating Label-Free Proteome Quantification

Description

The ProteoLFQ 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 "??ProteoLFQ" command in R.

Usage

lfqspikedall(
  data_s,
  spiked,
  assum_a = "Y",
  assum_b = "Y",
  assum_c = "Y",
  Ca = "1",
  Cb = "1",
  Cc = "1",
  Cd = "1",
  Ce = "1"
)

Arguments

data_s	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.
spiked	The file should provide the concentrations of known proteins (such as spiked proteins). This file is required, if the user want to conduct assessment using criteria (e) This file should contain the class of samples and the Sample ID. The Sample ID should be unique and defined by the preference of ProteoLFQ users, and the class of samples refers to the group of Sample ID. The ID of the spiked proteins should be consistent in both “data_s" and "spiked”. Detail information are described in the online "Example".
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”.
Ce	Criterion (e): accuracy of LFQ based on spiked and background proteins (Nat Biotechnol. 34:1130-6, 2016). If set 1, the user chooses to assess LFQ workflows using Criterion (e). If set 0, the user excludes Criterion (e) from performance assessment. The default setting of this value is “1”.

Value

preprocessed spiked matrix

Examples

allranks <- lfqspikedall(data_s=my_spiked,
spiked=spiked_data, assum_a="Y", assum_b="Y", assum_c="Y", Ca="1", Cb="1", Cc="1", Cd="1", Ce="1")

JianboFu0406/EVALFQ111 documentation built on Aug. 10, 2020, 1:49 p.m.