anal_prnTrend: Trend analysis of protein data

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

Trend analysis of protein data

Description

anal_prnTrend performs unsupervised clustering of protein log2FC.

Usage

anal_prnTrend(
  col_select = NULL,
  col_group = NULL,
  col_order = NULL,
  choice = c("cmeans", "clara", "kmeans", "pam", "fanny"),
  n_clust = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = FALSE,
  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.
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_order	Character string to a column key in expt_smry.xlsx. Numeric values under which will be used for the left-to-right arrangement of samples in graphic outputs or top-to-bottom arrangement in text outputs. At the NULL default, the column key Order will be used. If values under column Order are left blank, samples will be ordered by their names.
choice	Character string; the clustering method in c("cmeans", "clara", "kmeans", "pam", "fanny"). The default is "cmeans".
n_clust	Numeric vector; the number(s) of clusters that data will be divided into. At the NULL default, it will be determined by the gap method in clusGap. The n_clust overwrites the argument centers in cmeans.
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.
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_: 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 arguments for cmeans, kmeans, clara, pam. Note that centers in cmeans or kmeans is replaced with n_clust. The same applies to k in clara or pam. With cmeans, m is according to Schwaemmle and Jensen if not provided; x is disabled with input data being determined automatically.

Details

The option of complete_cases will be forced to TRUE at impute_na = FALSE

Value

Classified log2FC.

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
cluego for the visualization of anal_prnTrend and plot_prnTrend via Cytoscape/ClueGO
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

# ===================================
# Trend analysis
# ===================================

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

## global option
scale_log2r <- TRUE


# ===================================
# Analysis
# ===================================
## base (proteins, with sample order supervision)
anal_prnTrend(
  impute_na = FALSE,
  col_order = Order,
  n_clust = c(5:6), 
)

## against selected samples
anal_prnTrend(
  col_select = BI,
  impute_na = FALSE,
  col_order = Order,
  n_clust = c(5:6), 
  filename = sel.txt,
)

## row filtration (proteins)
anal_prnTrend(
  impute_na = FALSE,
  col_order = Order,
  n_clust = c(5:6), 
  filter_prots_by = exprs(prot_n_pep >= 2),
)

## manual m degree of fuzziness (proteins)
anal_prnTrend(
  impute_na = FALSE,
  col_order = Order,
  n_clust = c(5:6), 
  filter_prots = exprs(prot_n_pep >= 2),
  m = 1.5,
  filename = my_m.txt,
)

## 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)
anal_prnTrend(
  impute_na = FALSE,
  col_order = Order,
  n_clust = c(5:6), 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)

## additional row filtration by pVals (impute_na = TRUE)
# if not yet, run prerequisitive NA imputation and corresponding 
# significance tests at `impute_na = TRUE`
pepImp(m = 2, maxit = 2)
prnImp(m = 5, maxit = 5)

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)

anal_prnTrend(
  impute_na = TRUE,
  col_order = Order,
  n_clust = c(5:6), 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)


# ===================================
# Visualization
# ===================================
## base (proteins, no NA imputation) 
plot_prnTrend(
  col_order = Order, 
)

# at specific cluster ID(s)
# (`cluster` is a column key in `Protein_Trend_[...].txt`)
plot_prnTrend(
  impute_na = FALSE, 
  col_order = Order,
  filter2_by_clusters = exprs(cluster == 5),
  width = 8, 
  height = 10,
  filename = cl5.png,
)

# manual selection of secondary input data file(s)
# may be used for optimizing individual plots
plot_prnTrend(
  df2 = c("Protein_Trend_Z_nclust5.txt"),
  col_order = Order, 
  filename = n5.png,
)

# manual secondary input(s) at specific rank(s)
plot_prnTrend(
  df2 = c("Protein_Trend_Z_nclust5.txt"),
  impute_na = FALSE, 
  col_order = Order,
  filter2_by_clusters = exprs(cluster == 5),
  width = 8, 
  height = 10,
  filename = n5_cl5.png,
)

## NA imputation
# also save as pdf
plot_prnTrend(
  impute_na = TRUE,
  col_order = Order,
  filename = my.pdf,
)

## against selected samples
plot_prnTrend(
  col_order = Order, 
  col_select = BI,
  filename = bi.png,
)

## custom theme
library(ggplot2)
my_trend_theme <- theme_bw() + theme(
  axis.text.x  = element_text(angle=60, vjust=0.5, size=24),
  axis.ticks.x  = element_blank(), 
  axis.text.y  = element_text(angle=0, vjust=0.5, size=24),
  axis.title.x = element_text(colour="black", size=24),
  axis.title.y = element_text(colour="black", size=24),
  plot.title = element_text(face="bold", colour="black",
                            size=20, hjust=.5, vjust=.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(),
  panel.background = element_rect(fill = '#0868ac', colour = 'red'),
  
  strip.text.x = element_text(size = 24, colour = "black", angle = 0),
  strip.text.y = element_text(size = 24, colour = "black", angle = 90),
  
  plot.margin = unit(c(5.5, 55, 5.5, 5.5), "points"), 
  
  legend.key = element_rect(colour = NA, fill = 'transparent'),
  legend.background = element_rect(colour = NA,  fill = "transparent"),
  legend.position = "none",
  legend.title = element_text(colour="black", size=18),
  legend.text = element_text(colour="black", size=18),
  legend.text.align = 0,
  legend.box = NULL
)

plot_prnTrend(
  col_order = Order, 
  col_select = BI,
  theme = my_trend_theme,
  filename = my_theme.png,
)

## no grouping 
# each sample under column `Select` forms its own group
anal_prnTrend(
  col_group = Select,
  col_order = Order,
  n_clust = 6, 
  filter_prots = exprs(prot_n_pep >= 2),
  filename = sample_ids_as_groups.txt,
)

plot_prnTrend(
  df2 = "sample_ids_as_groups_Protein_Trend_Z_nclust6.txt",
  filter2_by_clusters = exprs(cluster == 4),
  width = 24,
  height = 16,
)

## grouped by column `Term_2` in metadata
anal_prnTrend(
  col_group = Term_2,
  col_order = Order,
  n_clust = 6, 
  filter_prots = exprs(prot_n_pep >= 2),
  filename = term_2_grouping.txt,
)

plot_prnTrend(
  df2 = "term_2_grouping_Protein_Trend_Z_nclust6.txt",
  filter2_by_clusters = exprs(cluster == 3),
  width = 6,
  height = 6,
)

## Cytoscape visualization
# (Make sure that Cytoscape is open.)
# Human
cluego(
  df2 = "Protein_Trend_Z_nclust5.txt", 
  species = c(human = "Homo Sapiens"), 
  n_clust = c(3, 5)
)

# Mouse
cluego(
  df2 = "Protein_Trend_Z_nclust5.txt", 
  species = c(mouse = "Mus Musculus"), 
  n_clust = c(3:4)
)

qzhang503/proteoQ documentation built on Dec. 14, 2024, 12:27 p.m.