anal_pepNMF: NMF Classification

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

NMF Classification

Description

anal_pepNMF performs the NMF classification of peptide log2FC. The function is a wrapper of nmf.

anal_prnNMF performs the NMF classification of protein log2FC. The function is a wrapper of nmf.

Usage

anal_pepNMF(
  col_select = NULL,
  col_group = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = TRUE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  rank = NULL,
  nrun = if (length(rank) > 1) 50 else 1,
  seed = NULL,
  ...
)

anal_prnNMF(
  col_select = NULL,
  col_group = NULL,
  scale_log2r = TRUE,
  complete_cases = FALSE,
  impute_na = TRUE,
  df = NULL,
  filepath = NULL,
  filename = NULL,
  rank = NULL,
  nrun = if (length(rank) > 1) 50 else 1,
  seed = 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.
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 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	Use system default.
filename	A representative file name to outputs. By default, it will be determined automatically by the name of the current call.
rank	Numeric vector; the factorization rank(s) in nmf. The default is c(4:8)
nrun	Numeric; the number of runs in nmf. The default is 50.
seed	Integer; a seed for reproducible analysis.
...	filter_: Logical expression(s) 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 nmf.

Details

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

Value

NMF classification of log2FC data.

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

# ===================================
# NMF
# ===================================

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

## global option
scale_log2r <- TRUE

library(NMF)

# ===================================
# Analysis
# ===================================
## base (proteins)
library(NMF)

anal_prnNMF(
  impute_na = FALSE,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
)

# passing a different `method`
anal_prnNMF(
  impute_na = FALSE,
  col_group = Group,
  method = "lee",
  rank = c(3:4),
  nrun = 20, 
  filename = lee.txt,
)

## row filtration and selected samples (proteins)
anal_prnNMF(
  impute_na = FALSE,
  col_select = BI,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
  filter_prots = exprs(prot_n_pep >= 3),
  filename = bi_npep3.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_prnNMF(
  impute_na = FALSE,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = pval.txt,
)

## 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_prnNMF(
  impute_na = TRUE,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
  filename = pval2.txt,
)

## analogous peptides
anal_pepNMF(
  impute_na = TRUE,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)

anal_pepNMF(
  impute_na = FALSE,
  col_group = Group,
  rank = c(3:4),
  nrun = 20, 
  filter_prots_by_npep = exprs(prot_n_pep >= 3), 
  filter_prots_by_pval = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6), 
)


# ===================================
# consensus heat maps
# ===================================
## no NA imputation 
# proteins, all available ranks
library(NMF)

plot_prnNMFCon(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 14,
  height = 14,
)

# analogous peptides
plot_pepNMFCon(
  impute_na = FALSE,
  col_select = BI,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  color = colorRampPalette(RColorBrewer::brewer.pal(n = 7, name = "Spectral"))(50), 
  width = 10,
  height = 10,
  filename = bi.pdf,
)

# manual selection of input data file(s)
# may be used for optimizing individual plots
plot_prnNMFCon(
  df2 = c("Protein_NMF_Z_rank3_consensus.txt", "Protein_NMF_Z_rank4_consensus.txt"),
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 14,
  height = 14,
)

## NA imputation 
# proteins, all available ranks
plot_prnNMFCon(
  impute_na = TRUE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 14,
  height = 14,
)

# analogous peptides
plot_pepNMFCon(
  impute_na = TRUE,
  col_select = BI,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 10,
  height = 10,
  filename = bi_con.png,
)


# ===================================
# coefficient heat maps
# ===================================
## no NA imputation 
# proteins, all available ranks
plot_prnNMFCoef(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 12,
  height = 12,
)

# manual selection of input data file(s)
# may be used for optimizing individual plots
plot_prnNMFCoef(
  df2 = c("Protein_NMF_Z_rank3_coef.txt"),  
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 12,
  height = 12,
)

# analogous peptides
plot_pepNMFCoef(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  color = colorRampPalette(brewer.pal(n = 7, name = "Spectral"))(50), 
  width = 12,
  height = 12,
)

## NA imputation 
# proteins, all available ranks
plot_prnNMFCoef(
  impute_na = TRUE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 10,
  height = 10,
)

# analogous peptides
plot_pepNMFCoef(
  impute_na = TRUE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 10,
  height = 10,
)


# ===================================
# Metagene heat maps
# ===================================
## no NA imputation 
# proteins, all available ranks
plot_metaNMF(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  
  # additional arguments for `pheatmap`
  fontsize = 8,
  fontsize_col = 5,
)

# proteins, selected sample(s)
plot_metaNMF(
  impute_na = FALSE,
  col_select = BI_1,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  fontsize = 8,
  fontsize_col = 5,
  cellwidth = 6, 
  filename = bi1.png,
)

# proteins, selected sample(s) and row ordering
plot_metaNMF(
  impute_na = FALSE,
  col_select = BI_1,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  fontsize = 8,
  fontsize_col = 5,
  cellwidth = 6, 
  cluster_rows = FALSE,
  arrange_prots_by = exprs(gene),
  filename = bi1_row_by_genes.png,
)

# manual selection of input .rda file(s)
# may be used for optimizing individual plots
plot_metaNMF(
  df2 = c("Protein_NMF_Z_rank3.rda"),  
  impute_na = FALSE,
  col_select = BI_1,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  fontsize = 8,
  fontsize_col = 5,
  cellwidth = 6, 
  cluster_rows = FALSE,
  arrange_prots_by = exprs(gene),
  filename = bi1_row_by_genes.png,
)

## NA imputation 
# proteins, all available ranks
plot_metaNMF(
  impute_na = TRUE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  fontsize = 8,
  fontsize_col = 5,
)

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