prnHist: Histogram visualization

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

Histogram visualization

Description

pepHist plots the histograms of peptide log2FC.

prnHist plots the histograms of protein log2FC.

Usage

pepHist(
  col_select = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  cut_points = c(mean_lint = NA),
  show_curves = TRUE,
  show_vline = TRUE,
  scale_y = TRUE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  theme = NULL,
  ...
)

prnHist(
  col_select = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  cut_points = c(mean_lint = NA),
  show_curves = TRUE,
  show_vline = TRUE,
  scale_y = TRUE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  theme = NULL,
  ...
)

Arguments

col_select	Character string to a column key in expt_smry.xlsx. At the NULL default, the column key of Select in expt_smry.xlsx will be used. In the case of no samples being specified under Select, the column key of Sample_ID will be used. The non-empty entries under the ascribing column will be used in indicated analysis.
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.
cut_points	A named, numeric vector defines the cut points (knots) in histograms. The default is cut_points = c(mean_lint = NA) where the cut points correspond to the quantile values under column mean_lint (mean log10 intensity) of input data. Values of log2FC will be then binned from -Inf to Inf according to the cut points. To disable data binning, set cut_points = Inf or -Inf. The binning of log2FC can also be achieved through a different numeric column, e.g., cut_points = c(prot_icover = seq(.25, .75, .25)). See also mergePep for data alignment with binning.
show_curves	Logical; if TRUE, shows the fitted curves. At the TRUE default, the curve parameters are based on the latest call to standPep or standPrn with method_align = MGKernel. This feature can inform the effects of data filtration on the alignment of logFC profiles. Also see standPep and standPrn for more examples.
show_vline	Logical; if TRUE, shows the vertical lines at x = 0. The default is TRUE.
scale_y	Logical; if TRUE, scale data on the y-axis. The default is TRUE.
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	A file path to output results. By default, it will be determined automatically by the name of the calling function and the value of id in the call.
filename	A representative file name to outputs. By default, the name(s) will be determined automatically. For text files, a typical file extension is .txt. For image files, they are typically saved via ggsave or pheatmap where the image type will be determined by the extension of the file name.
theme	A ggplot2 theme, i.e., theme_bw(), or a custom theme. At the NULL default, a system theme will be applied.
...	filter_: Variable argument statements for the row filtration of data against the column keys in Peptide.txt for peptides or Protein.txt for proteins. Each statement contains to 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 filter_peps_at = exprs(pep_len <= 50) will remove peptide entries with pep_len > 50. See also normPSM. Additional parameters for plotting with ggplot2: xmin, the minimum x at a log2 scale; the default is -2. xmax, the maximum x at a log2 scale; the default is +2. xbreaks, the breaks in x-axis at a log2 scale; the default is 1. binwidth, the binwidth of log2FC; the default is (xmax - xmin)/80. ncol, the number of columns; the default is 1. width, the width of plot; height, the height of plot. scales, should the scales be fixed across panels; the default is "fixed" and the alternative is "free".

Details

In the histograms, the log2FC under each TMT channel are color-coded by their contributing reporter-ion or LFQ intensity.

Value

Histograms of log2FC; raw histogram data: [...]_raw.txt; fitted data for curves: [...]_fitted.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

# ===================================
# Histogram
# ===================================

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

## examplary `MGKernel` alignment
standPep(
  method_align = MGKernel, 
  n_comp = 3, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
)

standPrn(
  method_align = MGKernel, 
  n_comp = 2, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
)

## (1) effects of data scaling
# peptide without log2FC scaling
pepHist(scale_log2r = FALSE)

# with scaling
pepHist(scale_log2r = TRUE)

## (2) sample column selection
# sample IDs indicated under column `Select` in `expt_smry.xlsx`
pepHist(col_select = Select, filename = colsel.png)

# protein data for samples under column `W2` in `expt_smry.xlsx`
prnHist(col_select = W2, filename = w2.png)

## (3) row filtration of data
# exclude oxidized methione or deamidated asparagine
pepHist(
  # filter_by = exprs(!grepl("[mn]", pep_seq_mod)),
  filter_by = exprs(not_contain_chars_in("mn", pep_seq_mod)),
  filename = "no_mn.png",
)

# phosphopeptide subset (error message if no matches)
pepHist(
  filter_peps = exprs(contain_chars_in("sty", pep_seq_mod)), 
  scale_y = FALSE, 
  filename = phospho.png,
)

# or use `grepl` directly
pepHist(
  filter_by = exprs(grepl("[sty]", pep_seq_mod)),
  filename = same_phospho.png,
)

## (4) between lead and lag
# leading profiles
pepHist(
  filename = lead.png,
)

# lagging profiles at
#   (1) n_psm >= 10
#   (2) and no methionine oxidation or asparagine deamidation
pepHist(
  filter_peps_by_npsm = exprs(pep_n_psm >= 10),
  filter_peps_by_mn = exprs(not_contain_chars_in("mn", pep_seq_mod)),
  filename = lag.png,
)

## (5) Data binning by `prot_icover`
pepHist(
  cut_points = c(prot_icover = NA),
  filename = prot_icover_coded.png,
)

## (6) custom theme
library(ggplot2)
my_histo_theme <- theme_bw() + theme(
  axis.text.x  = element_text(angle=0, vjust=0.5, size=18),
  axis.ticks.x  = element_blank(), # x-axis ticks
  axis.text.y  = element_text(angle=0, vjust=0.5, size=18),
  axis.title.x = element_text(colour="black", size=24),
  axis.title.y = element_text(colour="black", size=24),
  plot.title = element_text(colour="black", size=24, hjust=.5, vjust=.5),
  
  strip.text.x = element_text(size = 18, colour = "black", angle = 0),
  strip.text.y = element_text(size = 18, colour = "black", angle = 90),
  
  panel.grid.major.x = element_blank(),
  panel.grid.minor.x = element_blank(),
  panel.grid.major.y = element_blank(),
  panel.grid.minor.y = element_blank(),
  
  legend.key = element_rect(colour = NA, fill = 'transparent'),
  legend.background = element_rect(colour = NA,  fill = "transparent"),
  legend.title = element_blank(),
  legend.text = element_text(colour="black", size=18),
  legend.text.align = 0,
  legend.box = NULL
)

pepHist(
  theme = my_histo_theme,
  filename = my_theme.png,
)

pepHist(
  col_select = BI_1,
  theme = theme_dark(),
  filename = bi1_dark.png,
)


## (7) direct uses of ggplot2
library(ggplot2)
res <- pepHist(filename = default.png)

# names(res)

p <- ggplot() +
  geom_histogram(data = res$raw, aes(x = value, y = ..count.., fill = Int_index),
                 color = "white", alpha = .8, binwidth = .05, size = .1) +
  scale_fill_brewer(palette = "Spectral", direction = -1) +
  labs(title = "", x = expression("Ratio (" * log[2] * ")"), y = expression("Frequency")) +
  scale_x_continuous(limits = c(-2, 2), breaks = seq(-2, 2, by = 1),
                     labels = as.character(seq(-2, 2, by = 1))) +
  scale_y_continuous(limits = NULL) + 
  facet_wrap(~ Sample_ID, ncol = 5, scales = "fixed") # + 
  # my_histo_theme

p <- p + 
  geom_line(data = res$fitted, mapping = aes(x = x, y = value, colour = variable), size = .2) +
  scale_colour_manual(values = c("gray", "gray", "gray", "black"), name = "Gaussian",
                      breaks = c(c("G1", "G2", "G3"), paste(c("G1", "G2", "G3"), collapse = " + ")),
                      labels = c("G1", "G2", "G3", "G1 + G2 + G3"))

p <- p + geom_vline(xintercept = 0, size = .25, linetype = "dashed")

ggsave(file.path(dat_dir, "Peptide/Histogram/my_ggplot2.png"), 
       width = 22, height = 48, limitsize = FALSE)

## Not run: 
# sample selection
pepHist(
  col_select = "a_column_key_not_in_`expt_smry.xlsx`",
)

# data filtration
pepHist(
  filter_by = exprs(!grepl("[m]", a_column_key_not_in_data_table)),
)

prnHist(
  lhs_not_start_with_filter_ = exprs(n_psm >= 5),
)  

## End(Not run)

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