prnGSPA: GSPA of peptide data
In qzhang503/proteoQ: Processing and Informatic Analysis of Mass Spectrometrirc Data
pepGSPA R Documentation
GSPA of peptide data
Description
pepGSPA performs the analysis of Gene Set Probability Asymmetricity
(GSPA) against peptide log2FC data.
prnGSPA performs the analysis of Gene Set Probability Asymmetricity
(GSPA) against protein log2FC data.
Usage
pepGSPA(
gset_nms = c("go_sets", "c2_msig", "kinsub"),
method = "mean",
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
pval_cutoff = 0.05,
logFC_cutoff = log2(1.2),
gspval_cutoff = 0.05,
gslogFC_cutoff = log2(1.2),
min_size = 10,
max_size = Inf,
min_delta = 4,
min_greedy_size = 1,
use_adjP = FALSE,
fml_nms = NULL,
df = NULL,
filepath = NULL,
filename = NULL,
...
)
prnGSPA(
gset_nms = c("go_sets", "c2_msig", "kinsub"),
method = "mean",
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
pval_cutoff = 0.05,
logFC_cutoff = log2(1.2),
gspval_cutoff = 0.05,
gslogFC_cutoff = log2(1.2),
min_size = 10,
max_size = Inf,
min_delta = 4,
min_greedy_size = 1,
use_adjP = FALSE,
fml_nms = NULL,
df = NULL,
filepath = NULL,
filename = NULL,
...
)
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.
method
Character string or vector; the method to assess the p-values of
GSPA. The default is mean and the alternative is limma. See
also section Details for the calculations.
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.
pval_cutoff
Numeric value or vector; the cut-off in protein
significance pVal. Entries with pVals less significant than
the threshold will be excluded from enrichment analysis. The default is 0.05
for all formulas matched to or specified in argument fml_nms.
Formula-specific threshold is allowed by supplying a vector of cut-off
values.
logFC_cutoff
Numeric value or vector; the cut-off in protein
log2FC. Entries with absolute log2FC smaller than the
threshold will be excluded from enrichment analysis. The default magnitude
is log2(1.2) for all formulas matched to or specified in argument
fml_nms. Formula-specific threshold is allowed by supplying a vector
of absolute values in log2FC.
gspval_cutoff
Numeric value or vector; the cut-off in gene-set
significance pVal. Only enrichment terms with pVals more
significant than the threshold will be reported. The default is 0.05 for all
formulas matched to or specified in argument fml_nms.
Formula-specific threshold is allowed by supplying a vector of cut-off
values.
gslogFC_cutoff
Numeric value or vector; the cut-off in gene-set
enrichment fold change. Only enrichment terms with absolute fold change
greater than the threshold will be reported. The default magnitude is
log2(1.2) for all formulas matched to or specified in argument
fml_nms. Formula-specific threshold is allowed by supplying a vector
of absolute values in log2FC.
min_size
Numeric value or vector; minimum number of protein entries for
consideration in gene set tests. The number is after data filtration by
pval_cutoff, logFC_cutoff or varargs expressions under
filter_. The default is 10 for all formulas matched to or specified
in argument fml_nms. Formula-specific threshold is allowed by
supplying a vector of sizes.
max_size
Numeric value or vector; maximum number of protein entries for
consideration in gene set tests. The number is after data filtration by
pval_cutoff, logFC_cutoff or varargs expressions under
filter_. The default in infinite for all formulas matched to or
specified in argument fml_nms. Formula-specific threshold is allowed
by supplying a vector of sizes.
min_delta
Numeric value or vector; the minimum count difference between
the up- and the down-expressed group of proteins for consideration in gene
set tests. For example at min_delta = 4, a gene set will 6
upregulated proteins and 2 down-expressed proteins, or vice versa, will be
assessed. The number is after data filtration by pval_cutoff,
logFC_cutoff or varargs expressions under filter_. The default
is 4 for all formulas matched to or specified in argument fml_nms.
Formula-specific threshold is allowed by supplying a vector of sizes.
min_greedy_size
Numeric value or vector; minimum number of unique
protein entries for a gene set to be considered essential. The default in
1 for all formulas matched to or specified in argument
fml_nms. Formula-specific threshold is allowed by supplying a vector
of sizes.
use_adjP
Logical; if TRUE, use Benjamini-Hochberg pVals. The default is
FALSE.
fml_nms
Character string or vector; the formula name(s). By default,
the formula(s) will match those used in pepSig or
prnSig.
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.
...
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 of /Model/Protein[_impNA]_pVals.txt.
See also prnHM for the format of arrange_ statements.
Details
The significance pVals of individual proteins are first obtained from
prnSig, followed by log10 transformation and separation into up-
or down-expressed groups for each gene set. At the default of method =
mean, the geometric mean of pVals, P, are each calculated for
the groups of up or down regulated proteins, with a penalty-like term
-log10(P)=(\sum_{i=1}^{n}-log10(p)+m)/(n+m)
where n and m are the numbers of entries with p values
\le or > a significance cut-off, respectively. The quotient of the
two P values is then used to represent the significance of gene set
enrichment. The arguments pval_cutoff and logFC_cutoff are used
to discriminate low influence genes. Additional subsetting of data via the
vararg approach of filter_ is feasible. At method =
limma, moderated t-tests are performed against -log10(pVals) between
the up and the down groups via eBayes.
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
# ===================================
# GSPA tests
# ===================================
## !!!require the brief working example in `?load_expts`
## global option
scale_log2r <- TRUE
## base
# prerequisites in significance tests (impute_na = FALSE)
# as `pVals` are required
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)
# GSPA analysis
prnGSPA(
impute_na = FALSE,
pval_cutoff = 5E-2, # protein pVal threshold
logFC_cutoff = log2(1.2), # protein log2FC threshold
gspval_cutoff = 5E-2, # gene-set pVal threshold
gslogFC_cutoff = log2(1.2), # gene-set log2FC threshold
gset_nms = c("go_sets", "c2_msig"),
)
# GSPA visualization under volcano plots
# forced lines of `pval_cutoff` and `logFC_cutoff`
# from the corresponding `prnGSPA`;
# more examples of aesthetics in `?gspaMap`
gspaMap(
impute_na = FALSE,
topn_gsets = 50,
show_sig = pVal,
xco = 1.2, # optional x-axis cut-off lines
yco = 0.05, # a optional y-axis cut-off line
)
## row filtration
prnGSPA(
impute_na = FALSE,
gset_nms = c("go_sets", "c2_msig"),
filter_prots_by_npep = exprs(prot_n_pep >= 2),
filter_prots_by_species = exprs(species == "human"),
filename = row_filters.txt,
)
# by default, volcano-plot visualization for all GSPA result files,
# which are "Protein_GSPA_Z.txt" and "row_filters_Protein_GSPA_Z.txt"
# up to this point
# will match the primary `fliter_` varargs to
# those in the corresponding `prnGSPA(...)`
gspaMap(
impute_na = FALSE,
topn_labels = 0,
topn_gsets = 50,
show_sig = pVal,
)
# or manually supply specific secondary file(s) with `df2`;
gspaMap(
df2 = "row_filters_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 10,
show_sig = pVal,
)
# may apply additionally secondary `fliter2_` varargs against `df2`
gspaMap(
df2 = "row_filters_Protein_GSPA_Z.txt",
filter2_ess_size = exprs(ess_size >= 1),
impute_na = FALSE,
topn_gsets = 10,
topn_labels = 0,
show_sig = pVal,
)
# can also visualize results for specific formula(e) and/or
# specific `df2`
gspaMap(
fml_nms = "W16_vs_W2",
df2 = "row_filters_Protein_GSPA_Z.txt",
filter2_ess_size = exprs(ess_size >= 1),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
## customized thresholds
# (formula names defined a priori in `pepSig(...)`)
# (higher thresholds for "W16_vs_W2"; also set `max_size` to
# avoid trapping with large terms, such as "cell parts"...)
prnGSPA(
impute_na = FALSE,
fml_nms = c("W2_bat", "W2_loc", "W16_vs_W2"),
pval_cutoff = c(5E-2, 5E-2, 1E-6),
logFC_cutoff = c(log2(1.2)),
gslogFC_cutoff = c(log2(1.2)),
gspval_cutoff = c(5E-2, 5E-2, 1E-4),
max_size = c(Inf, Inf, 120),
gset_nms = c("go_sets"),
filter_by_npep = exprs(prot_n_pep >= 2),
filter_prots_by_species = exprs(species == "human"),
filename = thresholds.txt,
)
gspaMap(
df2 = "thresholds_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 10,
topn_labels = 0,
show_sig = pVal,
)
# "limma" for "W16_vs_W2"
prnGSPA(
impute_na = FALSE,
fml_nms = c("W2_bat", "W2_loc", "W16_vs_W2"),
method = c("mean", "mean", "limma"),
max_size = c(Inf, Inf, 120),
pval_cutoff = c(5E-2),
logFC_cutoff = c(log2(1.2)),
gspval_cutoff = c(5E-2),
gset_nms = c("go_sets", "c2_msig"),
filter_by = exprs(prot_n_pep >= 2, species == "human"),
filename = methods.txt,
)
gspaMap(
df2 = "methods_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 25,
topn_labels = 0,
show_sig = pVal,
)
## NA imputation
# 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)
prnGSPA(
impute_na = TRUE,
gset_nms = c("go_sets"),
filter_prots_by_npep = exprs(prot_n_pep >= 2),
)
# ===================================
# Distance heat maps of gene sets
# (also interactive networks)
# ===================================
# a `term` is a subset of an `ess_term` if the distance is zero
prnGSPAHM(
filter2_by = exprs(distance <= .7),
annot_cols = "ess_idx",
annot_colnames = "Eset index",
annot_rows = "ess_size",
filename = show_some_redundancy.png,
)
# human terms only
prnGSPAHM(
filter2_num = exprs(distance <= .95),
filter2_sp = exprs(start_with_str("hs", term)),
annot_cols = "ess_idx",
annot_colnames = "Eset index",
filename = show_more_connectivity.png,
)
# custom color palette
prnGSPAHM(
annot_cols = c("ess_idx", "ess_size"),
annot_colnames = c("Eset index", "Size"),
filter2_by = exprs(distance <= .95),
color = colorRampPalette(c("blue", "white", "red"))(100),
filename = custom_colors.png,
)
# can also visualize results for specific `df2` and formula(e)
prnGSPAHM(
df2 = "thresholds_Protein_GSPA_Z_essmap.txt",
fml_nms = "W16_vs_W2",
annot_cols = c("ess_idx", "ess_size"),
annot_colnames = c("Eset index", "Size"),
filter2_by = exprs(distance <= .95),
filename = w16_vs_w2.png,
)
# ===================================
# custom GO databases
# ===================================
# start over
unlink(file.path(dat_dir, "Protein/GSPA"), recursive = TRUE, force = TRUE)
# prepare GO databases (see also ?prepGO)
prepGO(
species = human,
db_path = "~/proteoQ/dbs/go",
gaf_url = "http://current.geneontology.org/annotations/goa_human.gaf.gz",
obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
filename = go_hs.rds,
)
prepGO(
species = mouse,
db_path = "~/proteoQ/dbs/go",
gaf_url = "http://current.geneontology.org/annotations/mgi.gaf.gz",
obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
filename = go_mm.rds,
)
head(readRDS(file.path("~/proteoQ/dbs/go", "go_hs.rds")))
head(readRDS(file.path("~/proteoQ/dbs/go", "go_mm.rds")))
# analysis
prnGSPA(
impute_na = FALSE,
gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
"~/proteoQ/dbs/go/go_mm.rds"),
)
# It is possible that the system "go_sets" may be partially different to the
# custom GO. Be explicit on `gset_nms`; otherwise, the default of `gset_nms =
# c("go_sets", "c2_msig")` will be applied.
gspaMap(
gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
"~/proteoQ/dbs/go/go_mm.rds"),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
# ===================================
# custom MSig databases
# ===================================
# start over
unlink(file.path(dat_dir, "Protein/GSPA"), recursive = TRUE, force = TRUE)
# prepare MSig databases (see also ?prepMSig)
prepMSig(species = human)
prepMSig(species = mouse)
head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_hs.rds")))
head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_mm.rds")))
# analysis
prnGSPA(
impute_na = FALSE,
gset_nms = c("~/proteoQ/dbs/msig/msig_hs.rds",
"~/proteoQ/dbs/msig/msig_mm.rds"),
)
# It is possible that the system "c2_msig" may be partially different to the
# custom databases. Be explicit on `gset_nms`; otherwise, the default of
# `gset_nms = c("go_sets", "c2_msig")` will be applied.
gspaMap(
gset_nms = c("~/proteoQ/dbs/msig/msig_hs.rds",
"~/proteoQ/dbs/msig/msig_mm.rds"),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
## put custom `prepGO`, `prepMSig` preparation into `prnGSPA` analysis
# start over with fresh database preparation
unlink(file.path("~/proteoQ/dbs/go"), recursive = TRUE, force = TRUE)
unlink(file.path("~/proteoQ/dbs/msig"), recursive = TRUE, force = TRUE)
library(magrittr)
prnGSPA(
impute_na = FALSE,
pval_cutoff = 5E-2,
logFC_cutoff = log2(1.2),
gspval_cutoff = 5E-2,
gset_nms = c(prepGO(human), prepGO(mouse), prepMSig(human), prepMSig(mouse)),
filename = cmbn.txt,
) %>%
gspaMap(
# gset_nms = .,
df2 = "cmbn_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 20,
topn_labels = 0,
show_sig = pVal,
)
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| pepGSPA | R Documentation |
GSPA of peptide data
Description
pepGSPA performs the analysis of Gene Set Probability Asymmetricity
(GSPA) against peptide log2FC data.
prnGSPA performs the analysis of Gene Set Probability Asymmetricity
(GSPA) against protein log2FC data.
Usage
pepGSPA(
gset_nms = c("go_sets", "c2_msig", "kinsub"),
method = "mean",
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
pval_cutoff = 0.05,
logFC_cutoff = log2(1.2),
gspval_cutoff = 0.05,
gslogFC_cutoff = log2(1.2),
min_size = 10,
max_size = Inf,
min_delta = 4,
min_greedy_size = 1,
use_adjP = FALSE,
fml_nms = NULL,
df = NULL,
filepath = NULL,
filename = NULL,
...
)
prnGSPA(
gset_nms = c("go_sets", "c2_msig", "kinsub"),
method = "mean",
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
pval_cutoff = 0.05,
logFC_cutoff = log2(1.2),
gspval_cutoff = 0.05,
gslogFC_cutoff = log2(1.2),
min_size = 10,
max_size = Inf,
min_delta = 4,
min_greedy_size = 1,
use_adjP = FALSE,
fml_nms = NULL,
df = NULL,
filepath = NULL,
filename = NULL,
...
)
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 |
method |
Character string or vector; the method to assess the p-values of
GSPA. The default is |
scale_log2r |
Logical; if TRUE, adjusts |
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. |
pval_cutoff |
Numeric value or vector; the cut-off in protein
significance |
logFC_cutoff |
Numeric value or vector; the cut-off in protein
|
gspval_cutoff |
Numeric value or vector; the cut-off in gene-set
significance |
gslogFC_cutoff |
Numeric value or vector; the cut-off in gene-set
enrichment fold change. Only enrichment terms with absolute fold change
greater than the threshold will be reported. The default magnitude is
|
min_size |
Numeric value or vector; minimum number of protein entries for
consideration in gene set tests. The number is after data filtration by
|
max_size |
Numeric value or vector; maximum number of protein entries for
consideration in gene set tests. The number is after data filtration by
|
min_delta |
Numeric value or vector; the minimum count difference between
the up- and the down-expressed group of proteins for consideration in gene
set tests. For example at |
min_greedy_size |
Numeric value or vector; minimum number of unique
protein entries for a gene set to be considered essential. The default in
|
use_adjP |
Logical; if TRUE, use Benjamini-Hochberg pVals. The default is FALSE. |
fml_nms |
Character string or vector; the formula name(s). By default,
the formula(s) will match those used in |
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
|
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. |
... |
|
Details
The significance pVals of individual proteins are first obtained from
prnSig, followed by log10 transformation and separation into up-
or down-expressed groups for each gene set. At the default of method =
mean, the geometric mean of pVals, P, are each calculated for
the groups of up or down regulated proteins, with a penalty-like term
-log10(P)=(\sum_{i=1}^{n}-log10(p)+m)/(n+m)
where n and m are the numbers of entries with p values
\le or > a significance cut-off, respectively. The quotient of the
two P values is then used to represent the significance of gene set
enrichment. The arguments pval_cutoff and logFC_cutoff are used
to discriminate low influence genes. Additional subsetting of data via the
vararg approach of filter_ is feasible. At method =
limma, moderated t-tests are performed against -log10(pVals) between
the up and the down groups via eBayes.
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
# ===================================
# GSPA tests
# ===================================
## !!!require the brief working example in `?load_expts`
## global option
scale_log2r <- TRUE
## base
# prerequisites in significance tests (impute_na = FALSE)
# as `pVals` are required
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)
# GSPA analysis
prnGSPA(
impute_na = FALSE,
pval_cutoff = 5E-2, # protein pVal threshold
logFC_cutoff = log2(1.2), # protein log2FC threshold
gspval_cutoff = 5E-2, # gene-set pVal threshold
gslogFC_cutoff = log2(1.2), # gene-set log2FC threshold
gset_nms = c("go_sets", "c2_msig"),
)
# GSPA visualization under volcano plots
# forced lines of `pval_cutoff` and `logFC_cutoff`
# from the corresponding `prnGSPA`;
# more examples of aesthetics in `?gspaMap`
gspaMap(
impute_na = FALSE,
topn_gsets = 50,
show_sig = pVal,
xco = 1.2, # optional x-axis cut-off lines
yco = 0.05, # a optional y-axis cut-off line
)
## row filtration
prnGSPA(
impute_na = FALSE,
gset_nms = c("go_sets", "c2_msig"),
filter_prots_by_npep = exprs(prot_n_pep >= 2),
filter_prots_by_species = exprs(species == "human"),
filename = row_filters.txt,
)
# by default, volcano-plot visualization for all GSPA result files,
# which are "Protein_GSPA_Z.txt" and "row_filters_Protein_GSPA_Z.txt"
# up to this point
# will match the primary `fliter_` varargs to
# those in the corresponding `prnGSPA(...)`
gspaMap(
impute_na = FALSE,
topn_labels = 0,
topn_gsets = 50,
show_sig = pVal,
)
# or manually supply specific secondary file(s) with `df2`;
gspaMap(
df2 = "row_filters_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 10,
show_sig = pVal,
)
# may apply additionally secondary `fliter2_` varargs against `df2`
gspaMap(
df2 = "row_filters_Protein_GSPA_Z.txt",
filter2_ess_size = exprs(ess_size >= 1),
impute_na = FALSE,
topn_gsets = 10,
topn_labels = 0,
show_sig = pVal,
)
# can also visualize results for specific formula(e) and/or
# specific `df2`
gspaMap(
fml_nms = "W16_vs_W2",
df2 = "row_filters_Protein_GSPA_Z.txt",
filter2_ess_size = exprs(ess_size >= 1),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
## customized thresholds
# (formula names defined a priori in `pepSig(...)`)
# (higher thresholds for "W16_vs_W2"; also set `max_size` to
# avoid trapping with large terms, such as "cell parts"...)
prnGSPA(
impute_na = FALSE,
fml_nms = c("W2_bat", "W2_loc", "W16_vs_W2"),
pval_cutoff = c(5E-2, 5E-2, 1E-6),
logFC_cutoff = c(log2(1.2)),
gslogFC_cutoff = c(log2(1.2)),
gspval_cutoff = c(5E-2, 5E-2, 1E-4),
max_size = c(Inf, Inf, 120),
gset_nms = c("go_sets"),
filter_by_npep = exprs(prot_n_pep >= 2),
filter_prots_by_species = exprs(species == "human"),
filename = thresholds.txt,
)
gspaMap(
df2 = "thresholds_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 10,
topn_labels = 0,
show_sig = pVal,
)
# "limma" for "W16_vs_W2"
prnGSPA(
impute_na = FALSE,
fml_nms = c("W2_bat", "W2_loc", "W16_vs_W2"),
method = c("mean", "mean", "limma"),
max_size = c(Inf, Inf, 120),
pval_cutoff = c(5E-2),
logFC_cutoff = c(log2(1.2)),
gspval_cutoff = c(5E-2),
gset_nms = c("go_sets", "c2_msig"),
filter_by = exprs(prot_n_pep >= 2, species == "human"),
filename = methods.txt,
)
gspaMap(
df2 = "methods_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 25,
topn_labels = 0,
show_sig = pVal,
)
## NA imputation
# 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)
prnGSPA(
impute_na = TRUE,
gset_nms = c("go_sets"),
filter_prots_by_npep = exprs(prot_n_pep >= 2),
)
# ===================================
# Distance heat maps of gene sets
# (also interactive networks)
# ===================================
# a `term` is a subset of an `ess_term` if the distance is zero
prnGSPAHM(
filter2_by = exprs(distance <= .7),
annot_cols = "ess_idx",
annot_colnames = "Eset index",
annot_rows = "ess_size",
filename = show_some_redundancy.png,
)
# human terms only
prnGSPAHM(
filter2_num = exprs(distance <= .95),
filter2_sp = exprs(start_with_str("hs", term)),
annot_cols = "ess_idx",
annot_colnames = "Eset index",
filename = show_more_connectivity.png,
)
# custom color palette
prnGSPAHM(
annot_cols = c("ess_idx", "ess_size"),
annot_colnames = c("Eset index", "Size"),
filter2_by = exprs(distance <= .95),
color = colorRampPalette(c("blue", "white", "red"))(100),
filename = custom_colors.png,
)
# can also visualize results for specific `df2` and formula(e)
prnGSPAHM(
df2 = "thresholds_Protein_GSPA_Z_essmap.txt",
fml_nms = "W16_vs_W2",
annot_cols = c("ess_idx", "ess_size"),
annot_colnames = c("Eset index", "Size"),
filter2_by = exprs(distance <= .95),
filename = w16_vs_w2.png,
)
# ===================================
# custom GO databases
# ===================================
# start over
unlink(file.path(dat_dir, "Protein/GSPA"), recursive = TRUE, force = TRUE)
# prepare GO databases (see also ?prepGO)
prepGO(
species = human,
db_path = "~/proteoQ/dbs/go",
gaf_url = "http://current.geneontology.org/annotations/goa_human.gaf.gz",
obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
filename = go_hs.rds,
)
prepGO(
species = mouse,
db_path = "~/proteoQ/dbs/go",
gaf_url = "http://current.geneontology.org/annotations/mgi.gaf.gz",
obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
filename = go_mm.rds,
)
head(readRDS(file.path("~/proteoQ/dbs/go", "go_hs.rds")))
head(readRDS(file.path("~/proteoQ/dbs/go", "go_mm.rds")))
# analysis
prnGSPA(
impute_na = FALSE,
gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
"~/proteoQ/dbs/go/go_mm.rds"),
)
# It is possible that the system "go_sets" may be partially different to the
# custom GO. Be explicit on `gset_nms`; otherwise, the default of `gset_nms =
# c("go_sets", "c2_msig")` will be applied.
gspaMap(
gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
"~/proteoQ/dbs/go/go_mm.rds"),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
# ===================================
# custom MSig databases
# ===================================
# start over
unlink(file.path(dat_dir, "Protein/GSPA"), recursive = TRUE, force = TRUE)
# prepare MSig databases (see also ?prepMSig)
prepMSig(species = human)
prepMSig(species = mouse)
head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_hs.rds")))
head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_mm.rds")))
# analysis
prnGSPA(
impute_na = FALSE,
gset_nms = c("~/proteoQ/dbs/msig/msig_hs.rds",
"~/proteoQ/dbs/msig/msig_mm.rds"),
)
# It is possible that the system "c2_msig" may be partially different to the
# custom databases. Be explicit on `gset_nms`; otherwise, the default of
# `gset_nms = c("go_sets", "c2_msig")` will be applied.
gspaMap(
gset_nms = c("~/proteoQ/dbs/msig/msig_hs.rds",
"~/proteoQ/dbs/msig/msig_mm.rds"),
impute_na = FALSE,
topn_gsets = 20,
show_sig = pVal,
)
## put custom `prepGO`, `prepMSig` preparation into `prnGSPA` analysis
# start over with fresh database preparation
unlink(file.path("~/proteoQ/dbs/go"), recursive = TRUE, force = TRUE)
unlink(file.path("~/proteoQ/dbs/msig"), recursive = TRUE, force = TRUE)
library(magrittr)
prnGSPA(
impute_na = FALSE,
pval_cutoff = 5E-2,
logFC_cutoff = log2(1.2),
gspval_cutoff = 5E-2,
gset_nms = c(prepGO(human), prepGO(mouse), prepMSig(human), prepMSig(mouse)),
filename = cmbn.txt,
) %>%
gspaMap(
# gset_nms = .,
df2 = "cmbn_Protein_GSPA_Z.txt",
impute_na = FALSE,
topn_gsets = 20,
topn_labels = 0,
show_sig = pVal,
)
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