productiveSeq: Productive sequences
In LymphoSeq: Analyze high-throughput sequencing of T and B cell receptors

Description Usage Arguments Value See Also Examples

Remove unproductive CDR3 sequences from a list of data frames.

1	productiveSeq(file.list, aggregate = "aminoAcid", prevalence = FALSE)

`file.list`	A list of data frames consisting antigen receptor sequencing data imported by the LymphoSeq function readImmunoSeq. "aminoAcid", "count", and "frequencyCount" are required columns.
`aggregate`	Indicates whether the values of "count", "frequencyCount", and "esimatedNumberGenomes" should be aggregated by amino acid or nucleotide sequence. Acceptable values are "aminoAcid" or "nucleotide". If "aminoAcid" is selected, then resulting data frame will have columns corresponding to aminoAcid, count, frequencyCount, and estimatedNumberGenomes (if this column is available). If "nucleotide" is selected then all columns in the original list will be present in the outputted list. The difference in output is due to the fact that the same amino acid CDR3 sequence may be encoded by multiple unique nucleotide sequences with differing V, D, and J genes.
`prevalence`	A Boolean value indicating if a new column should be added to each of the data frames giving the prevalence of each CDR3 amino acid sequence in 55 healthy donor peripheral blood samples. TRUE means the column is added and FALSE means it is not. Values range from 0 to 100% where 100% means the sequence appeared in the blood of all 55 individuals. The prevalenceTRB database is located in a separate package called LymphoSeqDB that should be loaded automatically.

Returns a list of data frames of productive amino acid sequences with recomputed values for "count", "frequencyCount", and "esimatedNumberGenomes". A productive sequences is defined as a sequences that is in frame and does not have an early stop codon.

Refer to the LymphoSeqDB package for details regarding the prevalenceTRB database.

file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")

file.list <- readImmunoSeq(path = file.path)

productive.nt <- productiveSeq(file.list = file.list, 
   aggregate = "nucleotide", prevalence = FALSE)

productive.aa <- productiveSeq(file.list = file.list, 
  aggregate = "aminoAcid", prevalence = TRUE)