ggDNAvis (version 0.3.2)

extract_methylation_from_dataframe: Extract methylation information from dataframe for visualisation

Description

This function takes a dataframe that contains methylation information in the form of locations (indices along the read signifying bases at which modification probability was assessed) and probabilities (the probability of modification at each assessed location, as an integer from 0 to 255).

Each observation/row in the dataframe represents one sequence (e.g. a Nanopore read). In the locations and probabilities column, each sequence (row) has many numbers associated. These are stored as one string per observation e.g. "3,6,9,12", with the column representing a character vector of such strings (e.g. c("3,6,9,12", "1,2,3,4")).

This function calls extract_and_sort_sequences() on each of these three columns and returns a list of vectors stored in $locations, $probabilities, and $lengths. These can then be used as input for visualise_methylation().

Default arguments are set up to work with the included example_many_sequences data.

Usage

extract_methylation_from_dataframe(
  modification_data,
  locations_colname = "methylation_locations",
  probabilities_colname = "methylation_probabilities",
  lengths_colname = "sequence_length",
  grouping_levels = c(family = 8, individual = 2),
  sort_by = "sequence_length",
  desc_sort = TRUE
)

Value

list, containing $locations (character vector), $probabilities (character vector), and $lengths (numeric vector).

Arguments

modification_data

dataframe. A dataframe that must contain columns for methylation locations, methylation probabilities, and sequence length for each read. The former two should be condensed strings as produced by vector_to_string() e.g. "1,2,3,4". The latter should be integer.

See example_many_sequences for an example of a compatible dataframe.

locations_colname

character. The name of the column within the input dataframe that contains methylation/modification location information. Defaults to "methylation_locations".

Values within this column must be a comma-separated string representing a condensed numerical vector (e.g. "3,6,9,12", produced via vector_to_string()) of the indices along the read at which modification was assessed. Indexing starts at 1.

probabilities_colname

character. The name of the column within the input dataframe that contains methylation/modification probability information. Defaults to "methylation_probabilities".

Values within this column must be a comma-separated string representing a condensed numerical vector (e.g. "2,212,128,64", produced via vector_to_string()) of the probability of modification as an 8-bit (0-255) integer for each base where modification was assessed.

lengths_colname

character. The name of the column within the input dataframe that contains the length of each sequence. Defaults to "sequence_length".

Values within this column must be non-negative integers.

grouping_levels

named character vector. What variables should be used to define the groups/chunks, and how large a gap should be left between groups at that level. Set to NA to turn off grouping.

Defaults to c("family" = 8, "individual" = 2), meaning the highest-level groups are defined by the family column, and there is a gap of 8 between each family. Likewise the second-level groups (within each family) are defined by the individual column, and there is a gap of 2 between each individual.

Any number of grouping variables and gaps can be given, as long as each grouping variable is a column within the dataframe. It is recommended that lower-level groups are more granular and subdivide higher-level groups (e.g. first divide into families, then into individuals within families).

To change the order of groups within a level, make that column a factor with the order specified e.g. example_many_sequences$family <- factor(example_many_sequences$family, levels = c("Family 2", "Family 3", "Family 1")) to change the order to Family 2, Family 3, Family 1.

sort_by

character. The name of the column within the dataframe that should be used to sort/order the rows within each lowest-level group. Set to NA to turn off sorting within groups.

Recommended to be the length of the sequence information, as is the case for the default "sequence_length" which was generated via example_many_sequences$sequence_legnth <- nchar(example_many_sequences$sequence).

desc_sort

logical. Boolean specifying whether rows within groups should be sorted by the sort_by variable descending (TRUE, default) or ascending (FALSE).

Examples

## See documentation for extract_and_sort_sequences()
## for more examples of changing sorting/grouping
extract_methylation_from_dataframe(
    example_many_sequences,
    locations_colname = "methylation_locations",
    probabilities_colname = "methylation_probabilities",
    lengths_colname = "sequence_length",
    grouping_levels = c("family" = 8, "individual" = 2),
    sort_by = "sequence_length",
    desc_sort = TRUE
)

extract_methylation_from_dataframe(
    example_many_sequences,
    locations_colname = "hydroxymethylation_locations",
    probabilities_colname = "hydroxymethylation_probabilities",
    lengths_colname = "sequence_length",
    grouping_levels = c("family" = 8, "individual" = 2),
    sort_by = "sequence_length",
    desc_sort = TRUE
)