prepare_PVCA_df {proBatch} | R Documentation

## 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)

[Package proBatch version 1.3.0 Index]