prnHM: Visualization of heat maps
In qzhang503/proteoQ: Processing and Informatic Analysis of Mass Spectrometrirc Data
pepHM R Documentation
Visualization of heat maps
Description
pepHM applies dist and hclust
for the visualization of the heat maps of peptide log2FC via
pheatmap.
prnHM applies dist and hclust
for the visualization of the heat maps of protein log2FC via
pheatmap.
Usage
pepHM(
col_select = NULL,
col_order = NULL,
col_benchmark = NULL,
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
rm_allna = TRUE,
df = NULL,
filepath = NULL,
filename = NULL,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = NULL,
xmin = -1,
xmax = 1,
xmargin = 0.1,
hc_method_rows = "complete",
hc_method_cols = "complete",
p_dist_rows = 2,
p_dist_cols = 2,
x = NULL,
p = NULL,
method = NULL,
diag = NULL,
upper = NULL,
annotation_col = NULL,
annotation_row = NULL,
clustering_method = NULL,
...
)
prnHM(
col_select = NULL,
col_order = NULL,
col_benchmark = NULL,
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
rm_allna = TRUE,
df = NULL,
filepath = NULL,
filename = NULL,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = NULL,
xmin = -1,
xmax = 1,
xmargin = 0.1,
hc_method_rows = "complete",
hc_method_cols = "complete",
p_dist_rows = 2,
p_dist_cols = 2,
x = NULL,
p = NULL,
method = NULL,
diag = NULL,
upper = NULL,
annotation_col = NULL,
annotation_row = NULL,
clustering_method = 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_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.
col_benchmark
Not used.
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.
rm_allna
Logical; if TRUE, removes data rows that are exclusively NA
across ratio columns of log2_R126 etc. The setting also applies to
log2_R000 in LFQ.
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.
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.
annot_rows
A character vector of column keys that can be found from
input files of Peptide.txt, Protein.txt etc. The values
under the selected keys will be used to color-code peptides or proteins on
the side of the indicated plot. The default is NULL without row annotation.
xmin
Numeric; the minimum x at a log2 scale. The default is -1.
xmax
Numeric; the maximum x at a log2 scale. The default is 1.
xmargin
Numeric; the margin in heat scales. The default is 0.1.
hc_method_rows
A character string; the same agglomeration method for
hclust of data rows. The default is complete.
hc_method_cols
A character string; similar to hc_method_rows
but for column data.
p_dist_rows
Numeric; the power of the Minkowski distance in the measures
of row dist at clustering_distance_rows = "minkowski".
The default is 2.
p_dist_cols
Numeric; similar to p_dist_rows but for column data.
x
Dummy argument to avoid incurring the corresponding argument in
dist by partial argument matches.
p
Dummy argument to avoid incurring the corresponding argument in
dist by partial argument matches.
method
Dummy argument to avoid incurring the corresponding argument in
dist by partial argument matches.
diag
Dummy argument to avoid incurring the corresponding argument in
dist by partial argument matches.
upper
Dummy argument to avoid incurring the corresponding argument in
dist by partial argument matches.
annotation_col
Dummy argument to avoid incurring the corresponding
argument in pheatmap.
annotation_row
Dummy argument to avoid incurring the corresponding
argument in pheatmap.
clustering_method
Dummy argument to avoid incurring the corresponding
argument in pheatmap.
...
filter_: Variable argument statements for the row filtration
against data in a primary file linked to df. Each statement contains
to a list of logical expression(s). The lhs needs to start with
filter_. The logical condition(s) at the rhs needs to be
enclosed in exprs with round parenthesis. For example, pep_len
is a column key in Peptide.txt. The statement filter_peps_at =
exprs(pep_len <= 50) will remove peptide entries with pep_len > 50.
See also pepHist, normPSM.
arrange_: Variable argument statements for the row ordering against
data in a primary file linked to df. The lhs needs to start
with arrange_. The expression(s) at the rhs needs to be
enclosed in exprs with round parenthesis. For example,
arrange_peps_by = exprs(gene, prot_n_pep) will arrange entries by
gene, then by prot_n_pep.
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
clustering_method
breaks into hc_method_rows for row data and hc_method_cols for
column data
clustering_distance_rows = "minkowski" allowed
together with the powder of p_dist_rows and/or p_dist_cols
Details
Data rows without non-missing pairs will result in NA distances in inter-row
dissimilarities (dist). At complet_cases = TRUE,
the data subset that are complete without missing values will be used. At
impute_na = TRUE, all data rows will be used with NA imputation (see
prnImp). At the default of complet_cases = FALSE and
impute_na = FALSE, NA distances will be arbitrarily replaced with the
mean value of the row-distance matrix for hierarchical row clustering.
Similar to data rows, NA distances in data columns will be replaced with the
mean value of the column-distance matrix.
To avoid memory failure, row aggregation using the kmeans_k option
(pheatmap) may be considered for large data sets.
Value
Heat maps and optional sub trees.
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
# ===================================
# Heat map
# ===================================
## !!!require the brief working example in `?load_expts`
## global option
scale_log2r <- TRUE
## proteins
# row clustering
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
filename = "huprns_npep2.png",
)
# rows ordered by kinase classes then by gene names
# (error if `normPSM(annot_kinases = FALSE, ...)`)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("kin_class"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 2,
cellheight = 2,
cellwidth = 14,
width = 22,
height = 22,
filter_kin = exprs(kin_attr, species == "human"),
arrange_kin = exprs(kin_order, gene),
filename = "hukins_rows_by_class.png",
)
# `cutree_rows` ignored at `cluster_rows = FALSE`
prnHM(
scale_log2r = TRUE,
annot_cols = c("Group"),
cluster_rows = FALSE,
clustering_distance_rows = "maximum",
cutree_rows = 6,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 22,
height = 22,
filename = "cutree_overruled.png",
)
# `minkowski` distance and `ward.D2` clustering
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
hc_method_rows = "ward.D2",
hc_method_cols = "ward.D2",
clustering_distance_rows = "minkowski",
clustering_distance_cols = "minkowski",
p_dist_rows = 2,
p_dist_cols = 2,
clustering_distance_cols = "manhattan",
filename = "rowminko2_colman_clustward.D2.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)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6),
filename = "pval_cutoff_at_1e6.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)
prnHM(
impute_na = TRUE,
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_prots_by_sp_npep = exprs(species == "human", prot_n_pep >= 3),
filter_prots_by_pvals = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6),
filename = "huprns_fil_impna.png",
)
## peptides
# under selected protein(s)
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
filename = "ncl_all.png",
)
# rows ordered by gene
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(gene),
filename = "ncl_rows_by_gene.png",
)
# rows ordered by sequence
# (may try alternatively `exprs(pep_seq)` if `pep_seq_mod` not a column key in `Peptide.txt`)
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(pep_seq_mod),
filename = "ncl_rows_by_seq.png",
)
# more options
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene", "W16_vs_W2.pVal (W16-W2)"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
filter_prots_by_pvals = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-5),
arrange_by = exprs(gene, -`W16_vs_W2.pVal (W16-W2)`),
filename = "ncl_more.png",
)
# selected samples
pepHM(
col_select = BI_1,
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(gene),
filename = "ncl_bi1.png",
)
## multiple genes
genes <- c("NCL", "Ncl")
lapply(genes, function (gene) {
gn <- gene
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
arrange_pep = exprs(pep_start, pep_end),
filter_sp = exprs(gene == !!gn),
filename = !!paste0(gene, ".png"),
)
})
## Customer annotation colors
annot_colors_group <- colorRampPalette(brewer.pal(n = 9, "Set1"))(12)
names(annot_colors_group) <- c("W16.BI.TMT1", "W16.BI.TMT2",
"W16.JHU.TMT1", "W16.JHU.TMT2",
"W16.PNNL.TMT1", "W16.PNNL.TMT2",
"W2.BI.TMT1", "W2.BI.TMT2",
"W2.JHU.TMT1", "W2.JHU.TMT2",
"W2.PNNL.TMT1", "W2.PNNL.TMT2")
annot_colors_lab <- brewer.pal(n = 3, "Set2")
names(annot_colors_lab) <- c("BI", "JHU", "PNNL")
annot_colors_batch <- brewer.pal(n = 4, "Set3")[1:2]
names(annot_colors_batch) <- c("TMT1", "TMT2")
annot_colors_whim <- brewer.pal(n = 4, "Set3")[3:4]
names(annot_colors_whim) <- c("W16", "W2")
annot_colors <- list(Group = annot_colors_group,
Lab = annot_colors_lab,
Batch = annot_colors_batch,
WHIM = annot_colors_whim)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
annotation_colors = annot_colors,
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
filter_sp = exprs(species == "human"),
filename = custom.png,
)
qzhang503/proteoQ documentation built on March 16, 2024, 5:27 a.m.
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| pepHM | R Documentation |
Visualization of heat maps
Description
pepHM applies dist and hclust
for the visualization of the heat maps of peptide log2FC via
pheatmap.
prnHM applies dist and hclust
for the visualization of the heat maps of protein log2FC via
pheatmap.
Usage
pepHM(
col_select = NULL,
col_order = NULL,
col_benchmark = NULL,
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
rm_allna = TRUE,
df = NULL,
filepath = NULL,
filename = NULL,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = NULL,
xmin = -1,
xmax = 1,
xmargin = 0.1,
hc_method_rows = "complete",
hc_method_cols = "complete",
p_dist_rows = 2,
p_dist_cols = 2,
x = NULL,
p = NULL,
method = NULL,
diag = NULL,
upper = NULL,
annotation_col = NULL,
annotation_row = NULL,
clustering_method = NULL,
...
)
prnHM(
col_select = NULL,
col_order = NULL,
col_benchmark = NULL,
scale_log2r = TRUE,
complete_cases = FALSE,
impute_na = FALSE,
rm_allna = TRUE,
df = NULL,
filepath = NULL,
filename = NULL,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = NULL,
xmin = -1,
xmax = 1,
xmargin = 0.1,
hc_method_rows = "complete",
hc_method_cols = "complete",
p_dist_rows = 2,
p_dist_cols = 2,
x = NULL,
p = NULL,
method = NULL,
diag = NULL,
upper = NULL,
annotation_col = NULL,
annotation_row = NULL,
clustering_method = NULL,
...
)
Arguments
col_select |
Character string to a column key in |
col_order |
Character string to a column key in |
col_benchmark |
Not used. |
scale_log2r |
Logical; if TRUE, adjusts |
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. |
rm_allna |
Logical; if TRUE, removes data rows that are exclusively NA
across ratio columns of |
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
|
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 |
filename |
A representative file name to outputs. By default, the
name(s) will be determined automatically. For text files, a typical file
extension is |
annot_cols |
A character vector of column keys in |
annot_colnames |
A character vector of replacement name(s) to
|
annot_rows |
A character vector of column keys that can be found from
input files of |
xmin |
Numeric; the minimum x at a log2 scale. The default is -1. |
xmax |
Numeric; the maximum x at a log2 scale. The default is 1. |
xmargin |
Numeric; the margin in heat scales. The default is 0.1. |
hc_method_rows |
A character string; the same agglomeration method for
|
hc_method_cols |
A character string; similar to |
p_dist_rows |
Numeric; the power of the Minkowski distance in the measures
of row |
p_dist_cols |
Numeric; similar to |
x |
Dummy argument to avoid incurring the corresponding argument in dist by partial argument matches. |
p |
Dummy argument to avoid incurring the corresponding argument in dist by partial argument matches. |
method |
Dummy argument to avoid incurring the corresponding argument in dist by partial argument matches. |
diag |
Dummy argument to avoid incurring the corresponding argument in dist by partial argument matches. |
upper |
Dummy argument to avoid incurring the corresponding argument in dist by partial argument matches. |
annotation_col |
Dummy argument to avoid incurring the corresponding argument in pheatmap. |
annotation_row |
Dummy argument to avoid incurring the corresponding argument in pheatmap. |
clustering_method |
Dummy argument to avoid incurring the corresponding argument in pheatmap. |
... |
|
Details
Data rows without non-missing pairs will result in NA distances in inter-row
dissimilarities (dist). At complet_cases = TRUE,
the data subset that are complete without missing values will be used. At
impute_na = TRUE, all data rows will be used with NA imputation (see
prnImp). At the default of complet_cases = FALSE and
impute_na = FALSE, NA distances will be arbitrarily replaced with the
mean value of the row-distance matrix for hierarchical row clustering.
Similar to data rows, NA distances in data columns will be replaced with the mean value of the column-distance matrix.
To avoid memory failure, row aggregation using the kmeans_k option
(pheatmap) may be considered for large data sets.
Value
Heat maps and optional sub trees.
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
# ===================================
# Heat map
# ===================================
## !!!require the brief working example in `?load_expts`
## global option
scale_log2r <- TRUE
## proteins
# row clustering
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
filename = "huprns_npep2.png",
)
# rows ordered by kinase classes then by gene names
# (error if `normPSM(annot_kinases = FALSE, ...)`)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("kin_class"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 2,
cellheight = 2,
cellwidth = 14,
width = 22,
height = 22,
filter_kin = exprs(kin_attr, species == "human"),
arrange_kin = exprs(kin_order, gene),
filename = "hukins_rows_by_class.png",
)
# `cutree_rows` ignored at `cluster_rows = FALSE`
prnHM(
scale_log2r = TRUE,
annot_cols = c("Group"),
cluster_rows = FALSE,
clustering_distance_rows = "maximum",
cutree_rows = 6,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 22,
height = 22,
filename = "cutree_overruled.png",
)
# `minkowski` distance and `ward.D2` clustering
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
hc_method_rows = "ward.D2",
hc_method_cols = "ward.D2",
clustering_distance_rows = "minkowski",
clustering_distance_cols = "minkowski",
p_dist_rows = 2,
p_dist_cols = 2,
clustering_distance_cols = "manhattan",
filename = "rowminko2_colman_clustward.D2.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)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
filter_sp = exprs(species == "human", prot_n_pep >= 2),
filter_by = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6),
filename = "pval_cutoff_at_1e6.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)
prnHM(
impute_na = TRUE,
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
width = 18,
height = 12,
filter_prots_by_sp_npep = exprs(species == "human", prot_n_pep >= 3),
filter_prots_by_pvals = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-6),
filename = "huprns_fil_impna.png",
)
## peptides
# under selected protein(s)
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
filename = "ncl_all.png",
)
# rows ordered by gene
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(gene),
filename = "ncl_rows_by_gene.png",
)
# rows ordered by sequence
# (may try alternatively `exprs(pep_seq)` if `pep_seq_mod` not a column key in `Peptide.txt`)
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(pep_seq_mod),
filename = "ncl_rows_by_seq.png",
)
# more options
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
annot_rows = c("gene", "W16_vs_W2.pVal (W16-W2)"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
filter_prots_by_pvals = exprs(`W16_vs_W2.pVal (W16-W2)` <= 1e-5),
arrange_by = exprs(gene, -`W16_vs_W2.pVal (W16-W2)`),
filename = "ncl_more.png",
)
# selected samples
pepHM(
col_select = BI_1,
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = TRUE,
annot_rows = c("gene"),
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
filter_by = exprs(gene %in% c("NCL", "Ncl")),
arrange_peps_by = exprs(gene),
filename = "ncl_bi1.png",
)
## multiple genes
genes <- c("NCL", "Ncl")
lapply(genes, function (gene) {
gn <- gene
pepHM(
xmin = -2,
xmax = 2,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
cluster_rows = FALSE,
show_rownames = TRUE,
show_colnames = TRUE,
fontsize_row = 10,
cellwidth = 12,
cellheight = 12,
width = 18,
height = 12,
arrange_pep = exprs(pep_start, pep_end),
filter_sp = exprs(gene == !!gn),
filename = !!paste0(gene, ".png"),
)
})
## Customer annotation colors
annot_colors_group <- colorRampPalette(brewer.pal(n = 9, "Set1"))(12)
names(annot_colors_group) <- c("W16.BI.TMT1", "W16.BI.TMT2",
"W16.JHU.TMT1", "W16.JHU.TMT2",
"W16.PNNL.TMT1", "W16.PNNL.TMT2",
"W2.BI.TMT1", "W2.BI.TMT2",
"W2.JHU.TMT1", "W2.JHU.TMT2",
"W2.PNNL.TMT1", "W2.PNNL.TMT2")
annot_colors_lab <- brewer.pal(n = 3, "Set2")
names(annot_colors_lab) <- c("BI", "JHU", "PNNL")
annot_colors_batch <- brewer.pal(n = 4, "Set3")[1:2]
names(annot_colors_batch) <- c("TMT1", "TMT2")
annot_colors_whim <- brewer.pal(n = 4, "Set3")[3:4]
names(annot_colors_whim) <- c("W16", "W2")
annot_colors <- list(Group = annot_colors_group,
Lab = annot_colors_lab,
Batch = annot_colors_batch,
WHIM = annot_colors_whim)
prnHM(
xmin = -1,
xmax = 1,
xmargin = 0.1,
annot_cols = c("Group", "Color", "Alpha", "Shape"),
annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
annotation_colors = annot_colors,
cluster_rows = TRUE,
cutree_rows = 10,
show_rownames = FALSE,
show_colnames = TRUE,
fontsize_row = 3,
cellwidth = 14,
filter_sp = exprs(species == "human"),
filename = custom.png,
)
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