Makes heat maps
Description
Makes heat maps
Usage
proteo_hm(
df = NULL,
id = NULL,
df_meta = NULL,
sample_ids = NULL,
filepath = NULL,
filename = NULL,
complete_cases = FALSE,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = NULL,
xmin = -1,
xmax = 1,
xmargin = 0.1,
...
)
Arguments
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").
|
id |
Character string; one of pep_seq, pep_seq_mod,
prot_acc and gene.
|
df_meta |
A file name of meta data.
|
sample_ids |
A character vector containing the sample IDs for an ascribing analysis.
|
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.
|
complete_cases |
Logical; if TRUE, only cases that are complete with no
missing values will be used. The default is FALSE.
|
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.
|
... |
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
|
Examples
proteo_hm(
df = Protein_delta.txt,
id = gene,
df_meta = hm_meta.xlsx,
filepath = file.path(dat_dir, "Protein/Heatmap"),
filename = "kin_delta.png",
complete_cases = FALSE,
annot_cols = NULL,
annot_colnames = NULL,
annot_rows = c("kin_class"),
cluster_rows = FALSE,
xmin = -1,
xmax = 1,
xmargin = .1,
width = 5,
height = 12,
arrange2_by = exprs(kin_class, gene),
)
