prepare_PVCA_df {proBatch} | R Documentation |
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 |
sample_annotation |
data frame with:
.
See |
feature_id_col |
name of the column with feature/gene/peptide/protein
ID used in the long format representation |
sample_id_col |
name of the column in |
technical_factors |
vector |
biological_factors |
vector |
fill_the_missing |
numeric value determining how missing values
should be substituted. If |
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)