prnEucDist: Distance plots

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

Distance plots

Description

pepEucDist visualizes the heat map of Euclidean distances for peptide data.

prnEucDist visualizes the heat map of Euclidean distances for protein data.

Usage

pepEucDist(
  col_select = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  adjEucDist = FALSE,
  annot_cols = NULL,
  annot_colnames = NULL,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  ...
)

prnEucDist(
  col_select = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  adjEucDist = FALSE,
  annot_cols = NULL,
  annot_colnames = NULL,
  df = NULL,
  filepath = NULL,
  filename = 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.
impute_na	Logical; if TRUE, data with the imputation of missing values will be used. The default is FALSE.
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.
annot_cols	A character vector of column keys in expt_smry.xlsx. The values under the selected keys will be used to color-code sample IDs on the top of the indicated plot. The default is NULL without column annotation.
annot_colnames	A character vector of replacement name(s) to annot_cols. The default is NULL without name replacement.
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.
...	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. 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 parameters for plotting: width, the width of plot height, the height of plot Additional arguments for pheatmap: cluster_rows, clustering_method, clustering_distance_rows... Notes about pheatmap: annotation_col disabled; instead use keys indicated in annot_cols annotation_row disabled; instead use keys indicated in annot_rows

Details

An Euclidean distance matrix of log2FC is returned by dist for heat map visualization.

Value

Heat map visualization of distance matrices.

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
# Mascot
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

# ===================================
# Euclidean distance
# ===================================

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

## global option
scale_log2r <- TRUE

pepEucDist(
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
)

prnEucDist(
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),

  # arguments for `pheatmap`
  display_numbers = TRUE,
  number_color = "grey30",
  number_format = "%.1f",
  clustering_distance_rows = "euclidean",
  clustering_distance_cols = "euclidean",
  fontsize = 16,
  fontsize_row = 20,
  fontsize_col = 20,
  fontsize_number = 8,
  cluster_rows = TRUE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  border_color = "grey60",
  cellwidth = 24,
  cellheight = 24,
  
  filter_prots_by = exprs(prot_n_pep >= 5),
  filename = "filter.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)
prnEucDist(
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = pvalcutoff.png, 
)

# analogous peptides
pepEucDist(
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
  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)

prnEucDist(
  impute_na = TRUE,
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = filpvals_impna.png, 
)

# analogous peptides
pepEucDist(
  impute_na = TRUE,
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = filpvals_impna.png, 
)

## custom color
pepEucDist(
  impute_na = TRUE,
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  width = 14,
  height = 12,
  filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  color = colorRampPalette(c("blue", "white", "red"))(500),
  filename = my_palette.png, 
)

## Not run: 
prnEucDist(
  col_color = "column_key_not_existed",
  col_shape = "another_missing_column_key",
  annot_cols = c("bad_column_key", "yet_another_bad_column_key")
)
## End(Not run)

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