mspip: Fills missing values by Peptide Identity Propagation (PIP)

In DavisLaboratory/msImpute: Imputation of label-free mass spectrometry peptides

Fills missing values by Peptide Identity Propagation (PIP)

Description

Peptide identity (sequence and charge) is propagated from MS-MS or PASEF identified features in evidence.txt to MS1 features in allPeptides.txt that are detected but not identified. A confidence score (probability) is assigned to every propagation. The confidence scores can be used as observation-level weights in limma::lmFit to account for uncertainty in inferred peptide intensity values.

Usage

mspip(
  path_txt,
  k = 10,
  thresh = 0,
  skip_weights = TRUE,
  tims_ms = FALSE,
  group_restriction = NULL,
  nlandmarks = 50
)

Arguments

path_txt	character. The path to MaxQuant txt directory
k	numeric. The k nearest neighbors to be used for identity propagation. default to 10.
thresh	numeric. The uncertainty threshold for calling a Identity Transfer as confident. Sequence to peptide feature assignments with confidence score (probability) above a threshold (specified by thresh) are considered as confident assignments.The rest of the assignments are discarded and not reported in the output.
skip_weights	logical. If TRUE, the propagation confidence scores are also reported. The confidence scores can be used as observation-level weights in limma linear models to improve differential expression testing. default to FALSE.
tims_ms	logical. Is data acquired by TIMS-MS? default to FALSE.
group_restriction	A data.frame with two columns named Raw.file and group, specifying run file and the (experimental) group to which the run belongs. Use this option for Unbalanced PIP
nlandmarks	numeric. Number of landmark peptides used for measuring neighborhood/coelution similarity. Default to 50.

Details

Data completeness is maximised by Peptide Identity Propagation (PIP) from runs where a peptide is identified by MSMS or PASEF to runs where peptide is not fragmented (hence MS2 information is not available), but is detected at the MS1 level. mspip reports a confidence score for each peptide that was identified by PIP. The intensity values of PIP peptides can be used to reduce missing values, while the reported confidence scores can be used to weight the contribution of these peptide intensity values to variance estimation in linear models fitted in limma.

Author(s)

Soroor Hediyeh-zadeh

See Also

evidenceToMatrix

DavisLaboratory/msImpute documentation built on Jan. 5, 2024, 3:50 a.m.