prepare_PVCA_df: prepare the weights of Principal Variance Components
In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description Usage Arguments Value Examples

View source: R/proteome_wide_diagnostics.R

prepare the weights of Principal Variance Components

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
)

`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)

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

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)