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Define custom dynamic vars.

Source: R/utility-functions.R
set_mandatory_IS_vars.Rd

This set of function allows users to specify custom look-up tables for dynamic variables. For more details, refer to the dedicated vignette vignette("workflow_start", package="ISAnalytics").

  • set_mandatory_IS_vars() sets the look-up table for mandatory IS vars.

  • set_annotation_IS_vars() sets the look-up table for genomic annotation IS vars.

  • set_af_columns_def() sets the look-up table for association file columns vars

  • set_iss_stats_specs() sets the look-up table for VISPA2 pool statistics vars

Usage

set_mandatory_IS_vars(specs)

set_annotation_IS_vars(specs)

set_af_columns_def(specs)

set_iss_stats_specs(specs)

Arguments

specs

Either a named vector or a data frame with specific format. See details.

Value

NULL

Details

The user can supply specifications in the form of a named vector or a data frame.

Named vector

When using a named vector, names should be the names of the columns, values should be the type associated with each column in the form of a string. The vector gets automatically converted into a data frame with the right format (default values for the columns transform and flag are NULL and required respectively). Use of this method is however discouraged: data frame inputs are preferred since they offer more control.

Look-up table structure

The look-up table for dynamic vars should always follow this structure:

namestypestransformflagtag
<name of the column><type><a lambda or NULL><flag><tag>

where

  • names contains the name of the column as a character

  • types contains the type of the column. Type should be expressed as a string and should be in one of the allowed types

  • char for character (strings)

  • int for integers

  • logi for logical values (TRUE / FALSE)

  • numeric for numeric values

  • factor for factors

  • date for generic date format - note that functions that need to read and parse files will try to guess the format and parsing may fail

  • One of the accepted date/datetime formats by lubridate, you can use ISAnalytics::date_formats() to view the accepted formats

  • transform: a purrr-style lambda that is applied immediately after importing. This is useful to operate simple transformations like removing unwanted characters or rounding to a certain precision. Please note that these lambdas need to be functions that accept a vector as input and only operate a transformation, aka they output a vector of the same length as the input. For more complicated applications that may require the value of other columns, appropriate functions should be manually applied post-import.

  • flag: as of now, it should be set either to required or optional - some functions internally check for only required tags presence and if those are missing from inputs they fail, signaling failure to the user

  • tag: a specific tag expressed as a string

Column types:

Type should be expressed as a string and should be in one of the allowed types

  • char for character (strings)

  • int for integers

  • logi for logical values (TRUE / FALSE)

  • numeric for numeric values

  • factor for factors

  • date for generic date format - note that functions that need to read and parse files will try to guess the format and parsing may fail

  • One of the accepted date/datetime formats by lubridate, you can use ISAnalytics::date_formats() to view the accepted formats

See also

Other dynamic vars: inspect_tags(), mandatory_IS_vars(), pcr_id_column(), reset_mandatory_IS_vars(), set_matrix_file_suffixes()

Examples

tmp_mand_vars <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "chrom", "char", ~ stringr::str_replace_all(.x, "chr", ""), "required",
    "chromosome",
    "position", "int", NULL, "required", "locus",
    "strand", "char", NULL, "required", "is_strand",
    "gap", "int", NULL, "required", NA_character_,
    "junction", "int", NULL, "required", NA_character_
)
set_mandatory_IS_vars(tmp_mand_vars)
#> Mandatory IS vars successfully changed
print(mandatory_IS_vars(TRUE))
#> # A tibble: 5 × 5
#>   names    types transform flag     tag       
#>   <chr>    <chr> <list>    <chr>    <chr>     
#> 1 chrom    char  <formula> required chromosome
#> 2 position int   <NULL>    required locus     
#> 3 strand   char  <NULL>    required is_strand 
#> 4 gap      int   <NULL>    required NA        
#> 5 junction int   <NULL>    required NA        
reset_mandatory_IS_vars()
#> Mandatory IS vars reset to default

tmp_annot_vars <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "gene", "char", NULL, "required",
    "gene_symbol",
    "gene_strand", "char", NULL, "required", "gene_strand"
)
print(annotation_IS_vars(TRUE))
#> # A tibble: 2 × 5
#>   names      types transform flag     tag        
#>   <chr>      <chr> <list>    <chr>    <chr>      
#> 1 GeneName   char  <NULL>    required gene_symbol
#> 2 GeneStrand char  <NULL>    required gene_strand
reset_annotation_IS_vars()
#> Annotation IS vars reset to default

temp_af_cols <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "project", "char", NULL, "required",
    "project_id",
    "pcr_id", "char", NULL, "required", "pcr_repl_id",
    "subject", "char", NULL, "required", "subject"
)
set_af_columns_def(temp_af_cols)
#> Warning: Warning: important tags missing
#> ℹ Some tags are required for proper execution of some functions. If these tags are not provided, execution of dependent functions might fail. Review your inputs carefully.
#> ℹ Missing tags: pool_id, fusion_id, tag_seq, vector_id, tissue, tp_days, cell_marker, tag_id, pcr_replicate, vispa_concatenate, proj_folder
#> ℹ To see where these are involved type `inspect_tags(c('pool_id','fusion_id','tag_seq','vector_id','tissue','tp_days','cell_marker','tag_id','pcr_replicate','vispa_concatenate','proj_folder'))`
#> Association file columns specs successfully changed
print(association_file_columns(TRUE))
#> # A tibble: 3 × 5
#>   names   types transform flag     tag        
#>   <chr>   <chr> <list>    <chr>    <chr>      
#> 1 project char  <NULL>    required project_id 
#> 2 pcr_id  char  <NULL>    required pcr_repl_id
#> 3 subject char  <NULL>    required subject    
reset_af_columns_def()
#> Association file columns specs reset to default

tmp_iss_vars <- tibble::tribble(
    ~names, ~types, ~transform, ~flag, ~tag,
    "pool", "char", NULL, "required",
    "vispa_concatenate",
    "tag", "char", NULL, "required", "tag_seq",
    "barcode", "int", NULL, "required", NA_character_
)
set_iss_stats_specs(tmp_iss_vars)
#> ISS stats specs successfully changed
iss_stats_specs(TRUE)
#> # A tibble: 3 × 5
#>   names   types transform flag     tag              
#>   <chr>   <chr> <list>    <chr>    <chr>            
#> 1 pool    char  <NULL>    required vispa_concatenate
#> 2 tag     char  <NULL>    required tag_seq          
#> 3 barcode int   <NULL>    required NA               
reset_iss_stats_specs()
#> ISS stats specs reset to default