Pep2Prn: Interim protein data

In qzhang503/proteoQ: Processing and Informatic Analysis of Mass Spectrometrirc Data

Interim protein data

Description

Pep2Prn summarizes Peptide.txt to an interim protein report in Protein.txt.

Usage

Pep2Prn(
  method_pep_prn = c("median", "mean", "weighted_mean", "lfq_max", "top_3_mean",
    "lfq_top_2_sum", "lfq_top_3_sum", "lfq_all"),
  use_unique_pep = TRUE,
  cut_points = Inf,
  rm_outliers = FALSE,
  rm_allna = FALSE,
  mc = TRUE,
  ...
)

Arguments

method_pep_prn	Character string; the method to summarize the log2FC and the intensity of peptides by protein entries. The descriptive statistics includes c("mean", "median", "weighted_mean", "top_3_mean", "lfq_max", "lfq_top_2_sum", "lfq_top_3_sum", "lfq_all") with median being the default for TMT and lfq_top_3_sum for LFQ. The representative log10-intensity of reporter (or LFQ) ions at the peptide levels will be the weight when summarizing log2FC with various "top_n" statistics or "weighted_mean".
use_unique_pep	Logical. If TRUE, only entries that are TRUE or equal to 1 under the column pep_isunique in Peptide.txt will be used, for summarizing the log2FC and the intensity of peptides into protein values. The default is to use unique peptides only. For MaxQuant data, the levels of uniqueness are according to the pep_unique_by in normPSM. The argument currently do nothing to Spectrum Mill data where both unique and shared peptides will be kept.
cut_points	A named, numeric vector defines the cut points (knots) for the median-centering of log2FC by sections. For example, at cut_points = c(mean_lint = seq(4, 7, .5)), log2FC will be binned according to the intervals of -Inf, 4, 4.5, ..., 7, Inf under column mean_lint (mean log10 intensity) in the input data. The default is cut_points = Inf, or equivalently -Inf, where the log2FC under each sample will be median-centered as one piece. See also prnHist for data binning in histogram visualization.
rm_outliers	Logical; if TRUE, PSM outlier removals will be performed for peptides with more than two identifying PSMs. Dixon's method will be used when 2 < n \le 25 and Rosner's method will be used when n > 25. The default is FALSE.
rm_allna	Logical; if TRUE, removes data rows that are exclusively NA across ratio columns of log2_R126 etc. The setting also applies to log2_R000 in LFQ.
mc	Logical. At the TRUE default, performs median-centering of log2FC after the peptide-to-protein aggregation. Otherwise, the summarized log2FC values will be left as they are.
...	filter_: Variable argument statements for the filtration of data rows. Each statement contains a list of logical expression(s). The lhs needs to start with filter_. The logical condition(s) at the rhs needs to be enclosed in exprs with round parenthesis. For example, pep_len is a column key in Peptide.txt. The statement of filter_peps_at = exprs(pep_len <= 50) will remove peptide entries with pep_len > 50.

Details

Fields other than log2FC and intensity are summarized with median statistics.

Value

The primary output in ".../Protein/Protein.txt".

See Also

Metadata
load_expts for metadata preparation and a reduced working example in data normalization

Data normalization
normPSM for extended examples in PSM data normalization
PSM2Pep for extended examples in PSM to peptide summarization
mergePep for extended examples in peptide data merging
standPep for extended examples in peptide data normalization
Pep2Prn for extended examples in peptide to protein summarization
standPrn for extended examples in protein data normalization.
purgePSM and purgePep for extended examples in data purging
pepHist and prnHist for extended examples in histogram visualization.
extract_raws and extract_psm_raws for extracting MS file names

Variable arguments of 'filter_...'
contain_str, contain_chars_in, not_contain_str, not_contain_chars_in, start_with_str, end_with_str, start_with_chars_in and ends_with_chars_in for data subsetting by character strings

Missing values
pepImp and prnImp for missing value imputation

Informatics
pepSig and prnSig for significance tests
pepVol and prnVol for volcano plot visualization
prnGSPA for gene set enrichment analysis by protein significance pVals
gspaMap for mapping GSPA to volcano plot visualization
prnGSPAHM for heat map and network visualization of GSPA results
prnGSVA for gene set variance analysis
prnGSEA for data preparation for online GSEA.
pepMDS and prnMDS for MDS visualization
pepPCA and prnPCA for PCA visualization
pepLDA and prnLDA for LDA visualization
pepHM and prnHM for heat map visualization
pepCorr_logFC, prnCorr_logFC, pepCorr_logInt and prnCorr_logInt for correlation plots
anal_prnTrend and plot_prnTrend for trend analysis and visualization
anal_pepNMF, anal_prnNMF, plot_pepNMFCon, plot_prnNMFCon, plot_pepNMFCoef, plot_prnNMFCoef and plot_metaNMF for NMF analysis and visualization

Custom databases
Uni2Entrez for lookups between UniProt accessions and Entrez IDs
Ref2Entrez for lookups among RefSeq accessions, gene names and Entrez IDs
prepGO for gene ontology
prepMSig for molecular signatures
prepString and anal_prnString for STRING-DB

Column keys in PSM, peptide and protein outputs
system.file("extdata", "psm_keys.txt", package = "proteoQ")
system.file("extdata", "peptide_keys.txt", package = "proteoQ")
system.file("extdata", "protein_keys.txt", package = "proteoQ")

Examples

# ===================================
# Peptides to proteins
# ===================================

## !!!require the brief working example in `?load_expts`

# use unique peptides
Pep2Prn()

# include shared peptides
Pep2Prn(use_unique_pep = FALSE)  

# alignment of data by segments
Pep2Prn(cut_points = seq(4, 7, 0.5))

qzhang503/proteoQ documentation built on March 16, 2024, 5:27 a.m.