prnMDS: MDS plots

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

MDS plots

Description

pepMDS visualizes the multidimensional scaling (MDS) of peptide log2FC.

prnMDS visualizes the multidimensional scaling (MDS) of protein log2FC.

Usage

pepMDS(
  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("cmdscale", "isoMDS"),
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  dist_co = log2(1),
  adjEucDist = FALSE,
  method = "euclidean",
  p = 2,
  k = 3,
  dimension = 2,
  folds = 1,
  center_features = TRUE,
  scale_features = TRUE,
  show_ids = TRUE,
  show_ellipses = FALSE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  theme = NULL,
  ...
)

prnMDS(
  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("cmdscale", "isoMDS"),
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  dist_co = log2(1),
  adjEucDist = FALSE,
  method = "euclidean",
  p = 2,
  k = 3,
  dimension = 2,
  folds = 1,
  center_features = TRUE,
  scale_features = TRUE,
  show_ids = TRUE,
  show_ellipses = FALSE,
  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.
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 MDS method in c("cmdscale", "isoMDS"). The default is "cmdscale".
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.
dist_co	Numeric; The cut-off in the absolute distance measured by d = abs(x_i - x_j). Data pairs, x_i and x_j, with corresponding d smaller than dist_co will be excluded from distance calculations by dist. The default is no distance cut-off at dist_co = log2(1).
adjEucDist	Logical; if TRUE, adjusts the inter-plex Euclidean distance by 1/sqrt(2) at method = "euclidean". The option adjEucDist = TRUE may be suitable when reference samples from each TMT plex undergo approximately the same sample handling process as the samples of interest. For instance, reference samples were split at the levels of protein lysates. Typically, adjEucDist = FALSE if reference samples were split near the end of a sample handling process, for instance, at the stages immediately before or after TMT labeling. Also see online README, section MDS for a brief reasoning.
method	Character string; the distance measure in one of c("euclidean", "maximum", "manhattan", "canberra", "binary") for dist. The default method is "euclidean".
p	Numeric; The power of the Minkowski distance in dist. The default is 2.
k	Numeric; The desired dimension for the solution passed to cmdscale. The default is 3.
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.
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.
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 against data in a primary file linked to df. See also normPSM for the format of filter_ statements. Additional parameters for ggsave: width, the width of plot; height, the height of plot ...

Details

An Euclidean distance matrix of log2FC is returned by dist, followed by a metric (cmdscale) or non-metric (isoMDS) MDS. The default is metric MDS with the input dissimilarities being euclidean distances. Note that the center_features alone will not affect the results of dist; it together with scale_features will be passed to scale.

Value

MDS 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

# ===================================
# MDS
# ===================================

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

# global option
scale_log2r <- TRUE

## peptides
# all samples
pepMDS(
  col_select = Select, 
  filter_peps_by = exprs(pep_n_psm >= 10),
  show_ids = FALSE, 
  filename = "peps_rowfil.png",
)

# selected samples
pepMDS(
  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",
)

# column `Alpha` will be used at the default of
# `col_alpha = NULL`;
# To bypass the aesthetics under column `Alpha`, 
# use `col_alpha = NA`
# (the same applies to other aesthetics, and PCA and LDA)
pepMDS(
  col_select = Select, 
  col_alpha = NA, 
  filter_peps_by = exprs(pep_n_psm >= 10),
  show_ids = FALSE, 
  filename = "peps_rowfil_no_alpha.png",
)


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

# custom palette
prnMDS(
  col_shape = Shape,
  color_brewer = Set1,
  show_ids = FALSE,
  filename = "my_palette.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)
prnMDS(
  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
pepMDS(
  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)

prnMDS(
  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
pepMDS(
  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,
)

## show ellipses
prnMDS(
  show_ellipses = TRUE,
  col_group = Shape, 
  show_ids = FALSE,
  filename = ellipses_by_whims.png,
)

prnMDS(
  show_ellipses = TRUE,
  col_group = Color, 
  show_ids = FALSE,
  filename = ellipses_by_labs.png,
)

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

prnMDS(
  show_ids = TRUE,
  k = 4, 
  dimension = 3,
  filename = d3.png,
)

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

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


# toy example of finding samples(s) that are 
# most different in large fold changes;
prnMDS(
  show_ids = TRUE, 
  dist_co = log2(4),
  filename = where_are_the_large_diffs.png,
)


## custom theme
library(ggplot2)
my_mds_theme <- theme_bw() + theme(
  axis.text.x  = element_text(angle=0, vjust=0.5, size=16),
  axis.text.y  = element_text(angle=0, vjust=0.5, size=16),
  axis.title.x = element_text(colour="black", size=18),
  axis.title.y = element_text(colour="black", size=18),
  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
)

pepMDS(
  impute_na = FALSE,
  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_mds_theme,
  filename = my_theme.png,
)

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

p_fil <- ggplot(res, aes(x = Coordinate.1, y = Coordinate.2)) +
  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 = "Coordinate 1", y = "Coordinate 2") +
  coord_fixed() 

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

## Not run: 
prnMDS(
  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.