prnGSVA: GSVA of protein data

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

GSVA of protein data

Description

prnGSVA performs the GSVA against protein log2FC. It is a wrapper of gsva.

Usage

prnGSVA(
  gset_nms = c("go_sets", "c2_msig", "kinsub"),
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  var_cutoff = 0.5,
  pval_cutoff = 1e-04,
  logFC_cutoff = log2(1.1),
  lm_method = "limma",
  padj_method = "BH",
  ...
)

Arguments

gset_nms	Character string or vector containing the shorthanded name(s), full file path(s), or both, to gene sets for enrichment analysis. For species among "human", "mouse", "rat", the default of c("go_sets", "c2_msig", "kinsub") will utilize terms from gene ontology (GO), molecular signatures (MSig) and kinase-substrate network (PSP Kinase-Substrate). Custom GO, MSig and other data bases at given species are also supported. See also: prepGO for the preparation of custom GO; prepMSig for the preparation of custom MSig. For other custom data bases, follow the same format of list as GO or MSig.
scale_log2r	Logical; if TRUE, adjusts log2FC to the same scale of standard deviation across all samples. The default is TRUE. At scale_log2r = NA, the raw log2FC without normalization will be used.
complete_cases	Logical; if TRUE, only cases that are complete with no missing values will be used. The default is FALSE.
impute_na	Logical; if TRUE, data with the imputation of missing values will be used. The default is FALSE.
df	The name of a primary data file. By default, it will be determined automatically after matching the types of data and analysis with an id among c("pep_seq", "pep_seq_mod", "prot_acc", "gene"). A primary file contains normalized peptide or protein data and is among c("Peptide.txt", "Peptide_pVal.txt", "Peptide_impNA_pVal.txt", "Protein.txt", "Protein_pVal.txt", "protein_impNA_pVal.txt"). For analyses require the fields of significance p-values, the df will be one of c("Peptide_pVal.txt", "Peptide_impNA_pVal.txt", "Protein_pVal.txt", "protein_impNA_pVal.txt").
filepath	Use system default.
filename	A representative file name to outputs. By default, it will be determined automatically by the name of the current call.
var_cutoff	Numeric; the cut-off in the variances of protein log2FC. Entries with variances smaller than the threshold will be removed from GSVA. The default is 0.5.
pval_cutoff	Numeric; the cut-off in enrichment pVals. Terms with enrichment pVals smaller than the threshold will be removed from multiple test corrections. The default is 1e-04.
logFC_cutoff	Numeric; the cut-off in enrichment log2FC. Terms with absolute log2FC smaller than the threshold will be removed from multiple test corrections. The default is at log2(1.1).
lm_method	Character string indicating the linear modeling method for significance assessment of GSVA enrichment scores. The default is limma. At method = lm, the lm() in base R will be used for models without random effects and the lmer will be used for models with random effects.
padj_method	Character string; the method of multiple-test corrections for uses with p.adjust. The default is "BH". See ?p.adjust.methods for additional choices.
...	filter_: Logical expression(s) for the row filtration against data in a primary file of /Model/Protein[_impNA]_pVals.txt. See also normPSM for the format of filter_ statements. arrange_: Variable argument statements for the row ordering against data in a primary file linked to df. See also prnHM for the format of arrange_ statements. Additional arguments for gsva

Details

The formula(s) of contrast(s) used in pepSig will be taken by default.

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

# ===================================
# GSVA
# ===================================

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

## global option
scale_log2r <- TRUE

## base
prnGSVA(
  impute_na = FALSE,
  min.sz = 10,
  verbose = FALSE,
  parallel.sz = 0,
  mx.diff = TRUE,
  gset_nms = "go_sets",
)

## row filtration
prnGSVA(
  impute_na = FALSE,
  min.sz = 10,
  verbose = FALSE,
  parallel.sz = 0,
  mx.diff = TRUE,
  gset_nms = c("go_sets", "kegg_sets"),
  filter_prots = exprs(prot_n_pep >= 3), 
  filename = fil.txt,
)

## additional row filtration by pVals (impute_na = FALSE)
# if not yet, run prerequisitive significance tests at `impute_na = FALSE`
pepSig(
  impute_na = FALSE, 
  W2_bat = ~ Term["W2.BI.TMT2-W2.BI.TMT1", 
                  "W2.JHU.TMT2-W2.JHU.TMT1", 
                  "W2.PNNL.TMT2-W2.PNNL.TMT1"],
  W2_loc = ~ Term_2["W2.BI-W2.JHU", 
                    "W2.BI-W2.PNNL", 
                    "W2.JHU-W2.PNNL"],
  W16_vs_W2 = ~ Term_3["W16-W2"], 
)

prnSig(impute_na = FALSE)

# (`W16_vs_W2.pVal (W16-W2)` is now a column key)
prnGSVA(
  min.sz = 10,
  verbose = FALSE,
  parallel.sz = 0,
  mx.diff = TRUE,
  gset_nms = "go_sets",
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)

## additional row filtration by pVals (impute_na = TRUE)
# if not yet, run prerequisitive NA imputation
pepImp(m = 2, maxit = 2)
prnImp(m = 5, maxit = 5)

# if not yet, run prerequisitive significance tests at `impute_na = TRUE`
pepSig(
  impute_na = TRUE, 
  W2_bat = ~ Term["W2.BI.TMT2-W2.BI.TMT1", 
                  "W2.JHU.TMT2-W2.JHU.TMT1", 
                  "W2.PNNL.TMT2-W2.PNNL.TMT1"],
  W2_loc = ~ Term_2["W2.BI-W2.JHU", 
                    "W2.BI-W2.PNNL", 
                    "W2.JHU-W2.PNNL"],
  W16_vs_W2 = ~ Term_3["W16-W2"], 
)

prnSig(impute_na = TRUE)

prnGSVA(
  impute_na = TRUE, 
  min.sz = 10,
  verbose = FALSE,
  parallel.sz = 0,
  mx.diff = TRUE,
  gset_nms = "go_sets",
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)

## custom data bases
# see also ?proteoQ::prepGO
prepGO(human)
prepGO(mouse)

prnGSVA(
  impute_na = FALSE,
  min.sz = 10,
  verbose = FALSE,
  parallel.sz = 0,
  mx.diff = TRUE,
  gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
               "~/proteoQ/dbs/go/go_mm.rds"),
)

qzhang503/proteoQ documentation built on April 13, 2025, 8:31 a.m.