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.
|
label_scheme_sub |
A data frame. Subset entries from label_scheme
for selected samples.
|
anal_type |
Character string; the type of analysis that are preset for
method dispatch in function factories. The value will be determined
automatically. Exemplary values include anal_type = c("PCA",
"Corrplot", "EucDist", "GSPA", "Heatmap", "Histogram", "MDS", "Model",
"NMF", "Purge", "Trend", "LDA", ...).
|
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.
|
center_features |
Logical; if TRUE, adjusts log2FC to center zero by
features (proteins or peptides). The default is TRUE. Note the difference to
data alignment with method_align in standPrn
or standPep where log2FC are aligned by observations
(samples).
|
scale_features |
Logical; if TRUE, adjusts log2FC to the same scale of
variance by features (protein or peptide entries). The default is TRUE. Note
the difference to data scaling with scale_log2r where log2FC are
scaled by observations (samples).
|
choice |
Character string; the PCA method in c("prcomp"). The
default is "prcomp".
|
method |
Character string; the distance measure in one of c("euclidean",
"maximum", "manhattan", "canberra", "binary") for dist.
The default method is "euclidean".
|
type |
Character string indicating the type of PCA by either
observations or features. At the type = obs default,
observations (samples) are in rows and features (peptides or proteins) in
columns for prcomp. The principal components are then
plotted by observations. Alternatively at type = feats, features
(peptides or proteins) are in rows and observations (samples) are in
columns. The principal components are then plotted by features.
|
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.
|
folds |
Not currently used. Integer; the degree of folding data into
subsets. The default is one without data folding.
|
out_file |
A file path object to an output file.
|
... |
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.
Arguments passed to prcomp: rank., tol
etc. At type = obs, argument scale becomes
scale_features and center matches center_features. At
type = feats, the setting of scale_log2r will be applied for
data scaling and data centering be automated by
standPep or standPrn.
Additional arguments for ggsave:
width, the width of plot;
height, the height of plot
...
|
