consensusSequence

consensusSequence - Construct a consensus sequence

Description¶

Construct a consensus sequence

Usage¶

consensusSequence(
sequences,
db = NULL,
method = c("mostCommon", "thresholdedFreq", "catchAll", "mostMutated", "leastMutated"),
minFreq = NULL,
muFreqColumn = NULL,
lenLimit = NULL,
includeAmbiguous = FALSE,
breakTiesStochastic = FALSE,
breakTiesByColumns = NULL
)

Arguments¶

sequences: character vector of sequences.
db: data.frame containing sequence data for a single clone. Applicable to and required for the "mostMutated" and "leastMutated" methods. Default is NULL.
method: method to calculate consensus sequence. One of "thresholdedFreq", "mostCommon", "catchAll", "mostMutated", or "leastMutated". See “Methods” under collapseClones for details.
minFreq: frequency threshold for calculating input consensus sequence. Applicable to and required for the "thresholdedFreq" method. A canonical choice is 0.6. Default is NULL.
muFreqColumn: character name of the column in db containing mutation frequency. Applicable to and required for the "mostMutated" and "leastMutated" methods. Default is NULL.
lenLimit: limit on consensus length. if NULL then no length limit is set.
includeAmbiguous: whether to use ambiguous characters to represent positions at which there are multiple characters with frequencies that are at least minimumFrequency or that are maximal (i.e. ties). Applicable to and required for the "thresholdedFreq" and "mostCommon" methods. Default is FALSE. See “Choosing ambiguous characters” under collapseClones for rules on choosing ambiguous characters. Note: this argument refers to the use of ambiguous nucleotides in the output consensus sequence. Ambiguous nucleotides in the input sequences are allowed for methods catchAll, mostMutated and leastMutated.
breakTiesStochastic: In case of ties, whether to randomly pick a sequence from sequences that fulfill the criteria as consensus. Applicable to and required for all methods except for "catchAll". Default is FALSE. See “Methods” under collapseClones for details.
breakTiesByColumns: A list of the form list(c(col_1, col_2, ...), c(fun_1, fun_2, ...)), where col_i is a character name of a column in db, and fun_i is a function to be applied on that column. Currently, only max and min are supported. Note that the two c()’s in list() are essential (i.e. if there is only 1 column, the list should be of the form list(c(col_1), c(func_1)). Applicable to and optional for the "mostMutated" and "leastMutated" methods. If supplied, fun_i’s are applied on col_i’s to help break ties. Default is NULL. See “Methods” under collapseClones for details.

Value¶

A list containing cons, which is a character string that is the consensus sequence for sequences; and muFreq, which is the maximal/minimal mutation frequency of the consensus sequence for the "mostMutated" and "leastMutated" methods, or NULL for all other methods.

Details¶

See collapseClones for detailed documentation on methods and additional parameters.

Examples¶

# Subset example data
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call %in% c("IGHA", "IGHG") & sample_id == "+7d")
clone <- subset(db, clone_id == "3192")

# First compute mutation frequency for most/leastMutated methods
clone <- observedMutations(clone, frequency=TRUE, combine=TRUE)

# Manually create a tie
clone <- rbind(clone, clone[which.max(clone$mu_freq), ])

# ThresholdedFreq method. 
# Resolve ties deterministically without using ambiguous characters
cons1 <- consensusSequence(clone$sequence_alignment,
method="thresholdedFreq", minFreq=0.3,
includeAmbiguous=FALSE, 
breakTiesStochastic=FALSE)
cons1$cons

[1] "GAGGTGCAGCTGGTGGTCTCTGGGGGA...GGCTTGGTACAGCCAGGGCGGTCCCTAAGACTCTCCTGTACAGTTTCTGGATTCACCTTT............GGTGATTATGCTATGACGTGGATCCGCCAGGCTCCTGGGAAGGGGCTGGAGTGGGTCGGTTTCATTAGAAGCAAAACTTTTGGTGGGACAGCAGATTACGCCGCGTTTGTGAGA...GGCAGATTCACCATCTCAAGAGATGATTCCAAAAACATCGCCTATCTGCAATTGAACAGCCTGAAAACCGAGGACACAGGCGTCTATTACTGTGGTAGAGATCTCGCCGTAACTGACACAATAGGTGGTACTAACTGGTTCGACCCCTGGGGCCAGGGGACCCCGGTCACCGTCTCCTCAG"