prepare_PVCA_df: prepare the weights of Principal Variance Components

In symbioticMe/proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

prepare the weights of Principal Variance Components

Description

prepare the weights of Principal Variance Components

Usage

prepare_PVCA_df(data_matrix, sample_annotation,
  feature_id_col = "peptide_group_label",
  sample_id_col = "FullRunName", technical_factors = c("MS_batch",
  "instrument"), biological_factors = c("cell_line", "drug_dose"),
  fill_the_missing = -1, pca_threshold = 0.6,
  variance_threshold = 0.01)

Arguments

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 help("example_proteome_matrix"))
sample_annotation	data frame with: sample_id_col (this can be repeated as row names) biological covariates technical covariates (batches etc) . See help("example_sample_annotation")
feature_id_col	name of the column with feature/gene/peptide/protein ID used in the long format representation df_long. In the wide formatted representation data_matrix this corresponds to the row names.
sample_id_col	name of the column in sample_annotation table, where the filenames (colnames of the data_matrix are found).
technical_factors	vector sample_annotation column names that are technical covariates
biological_factors	vector sample_annotation column names, that are biologically meaningful covariates
fill_the_missing	numeric value determining how missing values should be substituted. If NULL, features with missing values are excluded. If NULL, features with missing values are excluded.
pca_threshold	the percentile value of the minimum amount of the variabilities that the selected principal components need to explain
variance_threshold	the percentile value of weight each of the covariates needs to explain (the rest will be lumped together)

Value

data frame with weights and factors, combined in a way ready for plotting

Examples

matrix_test <- example_proteome_matrix[1:150, ]
pvca_df_res <- prepare_PVCA_df(matrix_test, example_sample_annotation, 
technical_factors = c('MS_batch', 'digestion_batch'),
biological_factors = c("Diet", "Sex", "Strain"), 
pca_threshold = .6, variance_threshold = .01, fill_the_missing = -1)

symbioticMe/proBatch documentation built on April 9, 2023, 11:59 a.m.