set_mandatory_IS_vars: Define custom dynamic vars.

In calabrialab/ISAnalytics: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

Define custom dynamic vars.

Description

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.

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:

names	types	transform	flag	tag
⁠<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

Value

NULL

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)
print(mandatory_IS_vars(TRUE))
reset_mandatory_IS_vars()

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))
reset_annotation_IS_vars()

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)
print(association_file_columns(TRUE))
reset_af_columns_def()

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(TRUE)
reset_iss_stats_specs()

calabrialab/ISAnalytics documentation built on Nov. 2, 2023, 8:57 p.m.