prnPCA: PCA plots

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

PCA plots

Description

prnPCA visualizes the principal component analysis (PCA) for peptide data.

prnPCA visualizes the principal component analysis (PCA) for protein data.

Usage

pepPCA(
  col_select = NULL,
  col_group = NULL,
  col_color = NULL,
  col_fill = NULL,
  col_shape = NULL,
  col_size = NULL,
  col_alpha = NULL,
  color_brewer = NULL,
  fill_brewer = NULL,
  size_manual = NULL,
  shape_manual = NULL,
  alpha_manual = NULL,
  choice = c("prcomp"),
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  center_features = TRUE,
  scale_features = TRUE,
  show_ids = TRUE,
  show_ellipses = FALSE,
  dimension = 2,
  folds = 1,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  theme = NULL,
  type = c("obs", "feats"),
  ...
)

prnPCA(
  col_select = NULL,
  col_group = NULL,
  col_color = NULL,
  col_fill = NULL,
  col_shape = NULL,
  col_size = NULL,
  col_alpha = NULL,
  color_brewer = NULL,
  fill_brewer = NULL,
  size_manual = NULL,
  shape_manual = NULL,
  alpha_manual = NULL,
  choice = c("prcomp"),
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  center_features = TRUE,
  scale_features = TRUE,
  show_ids = TRUE,
  show_ellipses = FALSE,
  dimension = 2,
  folds = 1,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  theme = NULL,
  type = c("obs", "feats"),
  ...
)

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.
col_group	Character string to a column key in expt_smry.xlsx. Samples corresponding to non-empty entries under col_group will be used for sample grouping in the indicated analysis. At the NULL default, the column key Group will be used. No data annotation by groups will be performed if the fields under the indicated group column is empty.
col_color	Character string to a column key in expt_smry.xlsx. Values under which will be used for the color aesthetics in plots. At the NULL default, the column key Color will be used. If NA, bypasses the aesthetics (a means to bypass the look-up of column Color and handle duplication in aesthetics).
col_fill	Character string to a column key in expt_smry.xlsx. Values under which will be used for the fill aesthetics in plots. At the NULL default, the column key Fill will be used. If NA, bypasses the aesthetics (a means to bypass the look-up of column Fill and handle duplication in aesthetics).
col_shape	Character string to a column key in expt_smry.xlsx. Values under which will be used for the shape aesthetics in plots. At the NULL default, the column key Shape will be used. If NA, bypasses the aesthetics (a means to bypass the look-up of column Shape and handle duplication in aesthetics).
col_size	Character string to a column key in expt_smry.xlsx. Values under which will be used for the size aesthetics in plots. At the NULL default, the column key Size will be used. If NA, bypasses the aesthetics (a means to bypass the look-up of column Size and handle duplication in aesthetics).
col_alpha	Character string to a column key in expt_smry.xlsx. Values under which will be used for the alpha (transparency) aesthetics in plots. At the NULL default, the column key Alpha will be used. If NA, bypasses the aesthetics (a means to bypass the look-up of column Alpha and handle duplication in aesthetics).
color_brewer	Character string to the name of a color brewer for use in ggplot2::scale_color_brewer, i.e., color_brewer = Set1. At the NULL default, the setting in ggplot2 will be used.
fill_brewer	Character string to the name of a color brewer for use in ggplot2::scale_fill_brewer, i.e., fill_brewer = Spectral. At the NULL default, the setting in ggplot2 will be used.
size_manual	Numeric vector to the scale of sizes for use in ggplot2::scale_size_manual, i.e., size_manual = c(8, 12). At the NULL default, the setting in ggplot2 will be used.
shape_manual	Numeric vector to the scale of shape IDs for use in ggplot2::scale_shape_manual, i.e., shape_manual = c(5, 15). At the NULL default, the setting in ggplot2 will be used.
alpha_manual	Numeric vector to the scale of transparency of objects for use in ggplot2::scale_alpha_manual , i.e., alpha_manual = c(.5, .9). At the NULL default, the setting in ggplot2 will be used.
choice	Character string; the PCA method in c("prcomp"). The default is "prcomp".
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; always TRUE for PCA.
impute_na	Logical; if TRUE, data with the imputation of missing values will be used. The default is FALSE.
center_features	Logical; if TRUE, adjusts log2FC to center zero by features (proteins or peptides). The default is TRUE. Note the difference to data alignment with method_align in standPrn or standPep where log2FC are aligned by observations (samples).
scale_features	Logical; if TRUE, adjusts log2FC to the same scale of variance by features (protein or peptide entries). The default is TRUE. Note the difference to data scaling with scale_log2r where log2FC are scaled by observations (samples).
show_ids	Logical; if TRUE, shows the sample IDs in MDS/PCA plots. The default is TRUE.
show_ellipses	Logical; if TRUE, shows the ellipses by sample groups according to col_group. The default is FALSE.
dimension	Numeric; The desired dimension for pairwise visualization. The default is 2.
folds	Not currently used. Integer; the degree of folding data into subsets. The default is one without data folding.
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.
type	Character string indicating the type of PCA by either observations or features. At the type = obs default, observations (samples) are in rows and features (peptides or proteins) in columns for prcomp. The principal components are then plotted by observations. Alternatively at type = feats, features (peptides or proteins) are in rows and observations (samples) are in columns. The principal components are then plotted by features.
...	filter_: Variable argument statements for the row filtration against data in a primary file linked to df. See also normPSM for the format of filter_ statements. Arguments passed to prcomp: rank., tol etc. At type = obs, argument scale becomes scale_features and center matches center_features. At type = feats, the setting of scale_log2r will be applied for data scaling and data centering be automated by standPep or standPrn. Additional arguments for ggsave: width, the width of plot; height, the height of plot ...

Details

The utility is a wrapper of prcomp against log2FC. The results are then visualized by either observations or features. See also https://proteoq.netlify.app/post/wrapping-pca-into-proteoq/ for data centering by either observations or features.

Value

PCA plots.

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

# ===================================
# PCA
# ===================================

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

## global option
scale_log2r <- TRUE

# peptides, all samples
pepPCA(
  col_select = Select, 
  filter_peps_by = exprs(pep_n_psm >= 3),
  show_ids = FALSE, 
  filename = "peps_rowfil.png",
)

# peptides, samples under column `BI`
pepPCA(
  col_select = BI, 
  col_shape = Shape,   
  col_color = Alpha, 
  filter_peps_by = exprs(pep_n_psm >= 10),
  show_ids = FALSE, 
  filename = "peps_rowfil_colsel.png",
)

# proteins
prnPCA(
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filename = "prns_rowfil.png",
)

# subset by mean deviation values
# deviations to means may not be symmetric;
prnPCA(
  col_select = Select, 
  filter_peps_by = exprs(prot_mean_z >= -.25, prot_mean_z <= .3),
  show_ids = FALSE, 
  filename = "subset_by_mean_dev.png",
)

# proteins, custom palette
prnPCA(
  col_shape = Shape,
  color_brewer = Set1,
  show_ids = FALSE,
  filename = "my_palette.png",
)

# proteins, by features
prnPCA(
  type = feats,
  scale_log2r = TRUE,
  filename = "by_feats.png",
)

## additional row filtration by pVals (proteins, 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)` now a column key)
prnPCA(
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = pvalcutoff.png, 
)

# analogous peptides
prnPCA(
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = pvalcutoff.png, 
)

## additional row filtration by pVals (proteins, 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)

prnPCA(
  impute_na = TRUE,
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = filpvals_impna.png, 
)

# analogous peptides
pepPCA(
  impute_na = TRUE,
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = filpvals_impna.png,
)

## a higher dimension
pepPCA(
  show_ids = FALSE,
  rank. = 5, 
  dimension = 3,
  filename = d3.pdf,
)

prnPCA(
  show_ids = TRUE,
  rank. = 4, 
  dimension = 3,
  filename = d3.png,
)

prnPCA(
  type = feats,
  rank. = 4, 
  dimension = 3,
  filename = feat_d3.png,
)

# show ellipses
# (column `expt_smry.xlsx::Color` codes `labs`.)
prnPCA(
  show_ids = FALSE,
  show_ellipses = TRUE,
  col_group = Color, 
  rank. = 4, 
  dimension = 3,
  filename = d3_labs.png,
)

# (column `expt_smry.xlsx::Shape` codes `WHIMs`.)
prnPCA(
  show_ids = FALSE,
  show_ellipses = TRUE,
  col_group = Shape, 
  rank. = 4, 
  dimension = 3,
  filename = d3_whims.png,
)

## custom theme
library(ggplot2)
my_theme <- theme_bw() + theme(
  axis.text.x  = element_text(angle=0, vjust=0.5, size=20),
  axis.text.y  = element_text(angle=0, vjust=0.5, size=20),
  axis.title.x = element_text(colour="black", size=20),
  axis.title.y = element_text(colour="black", size=20),
  plot.title = element_text(face="bold", colour="black", size=20, hjust=0.5, vjust=0.5),
  
  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=14),
  legend.text.align = 0,
  legend.box = NULL
)

pepPCA(
  impute_na = TRUE,
  col_color = Color,
  col_shape = Shape,
  show_ids = FALSE,
  filter_peps_by = exprs(prot_n_pep >= 5),
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  theme = my_theme, 
  filename = my_theme.png,
)

## direct uses of ggplot2
library(ggplot2)
res <- prnPCA(filename = foo.png)

# names(res)

p_fil <- ggplot(res$pca, aes(PC1, PC2)) +
  geom_point(aes(colour = Color, shape = Shape, alpha = Alpha), size = 4, stroke = 0.02) + 
  scale_alpha_manual(values = c(.5, .9)) + 
  stat_ellipse(aes(fill = Shape), geom = "polygon", alpha = .4) + 
  guides(fill = FALSE) + 
  labs(title = "", 
       x = paste0("PC1 (", res$prop_var[1], ")"), 
       y = paste0("PC2 (", res$prop_var[2], ")")) +
  coord_fixed() 

ggsave(file.path(dat_dir, "Protein/PCA/my_ggplot2_fil.png"))

## Not run: 
# Ambiguous matches of `scale` to `scale_log2r` or `scale_features`
prnPCA(scale = TRUE)

# need to match correct column key(s) in `expt_smry.xlsx`
prnPCA(
  col_color = "column_key_not_existed",
  col_shape = "another_missing_column_key"
)  

## End(Not run)

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