plot_peptide_corr_distribution | R Documentation |
Plot distribution of peptide correlations within one protein and between proteins
plot_peptide_corr_distribution(data_matrix, peptide_annotation,
protein_col = "ProteinName", feature_id_col = "peptide_group_label",
filename = NULL, width = NA, height = NA, units = c("cm", "in",
"mm"), plot_title = "Distribution of peptide correlation",
theme = "classic")
plot_peptide_corr_distribution.corrDF(corr_distribution, filename = NULL,
width = NA, height = NA, units = c("cm", "in", "mm"),
plot_title = "Correlation of peptides", theme = "classic",
base_size = 20)
data_matrix |
features (in rows) vs samples (in columns) matrix, with
feature IDs in rownames and file/sample names as colnames.
See "example_proteome_matrix" for more details (to call the description,
use |
peptide_annotation |
long format data frame with peptide ID and their
corresponding protein and/or gene annotations.
See |
protein_col |
column where protein names are specified |
feature_id_col |
name of the column with feature/gene/peptide/protein
ID used in the long format representation |
filename |
path where the results are saved. If null the object is returned to the active window; otherwise, the object is save into the file. Currently only pdf and png format is supported |
width |
option determining the output image width |
height |
option determining the output image width |
units |
units: 'cm', 'in' or 'mm' |
plot_title |
title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc)) |
theme |
ggplot theme, by default |
corr_distribution |
data frame with peptide correlation distribution |
ggplot
object (violin plot of peptide correlation)
calculate_peptide_corr_distr
, ggplot
peptide_corr_distribution <- plot_peptide_corr_distribution(
example_proteome_matrix,
example_peptide_annotation, protein_col = 'Gene')
selected_genes = c('BOVINE_A1ag','BOVINE_FetuinB','Cyfip1')
gene_filter = example_peptide_annotation$Gene %in% selected_genes
peptides_ann = example_peptide_annotation$peptide_group_label
selected_peptides = peptides_ann[gene_filter]
matrix_test = example_proteome_matrix[selected_peptides,]
pep_annotation_sel = example_peptide_annotation[gene_filter, ]
corr_distribution = calculate_peptide_corr_distr(matrix_test,
pep_annotation_sel, protein_col = 'Gene')
peptide_corr_distribution <- plot_peptide_corr_distribution.corrDF(corr_distribution)
## Not run:
peptide_corr_distribution <- plot_peptide_corr_distribution.corrDF(corr_distribution,
filename = 'test_peptide.png',
width = 28, height = 28, units = 'cm')
## End(Not run)
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