NAIR (version 1.0.4)

filterInputData: Filter Data Rows and Subset Data Columns

Description

Given a data frame with a column containing receptor sequences, filter data rows by sequence length and sequence content. Keep all data columns or choose which columns to keep.

Usage

filterInputData(
  data,
  seq_col,
  min_seq_length = NULL,
  drop_matches = NULL,
  subset_cols = NULL,
  count_col = deprecated(),
  verbose = FALSE
)

Value

A data frame.

Arguments

data

A data frame.

seq_col

Specifies the column(s) of data containing the receptor sequences. Accepts a character or numeric vector of length 1 or 2, containing either column names or column indices. Each column specified will be coerced to a character vector. Data rows containing a value of NA in any of the specified columns will be dropped.

min_seq_length

Observations whose receptor sequences have fewer than min_seq_length characters are dropped.

drop_matches

Accepts a character string containing a regular expression (see regex). Checks values in the receptor sequence column for a pattern match using grep(). Rows in which a match is found are dropped.

subset_cols

Specifies which columns of the AIRR-Seq data are included in the output. Accepts a character vector of column names or a numeric vector of column indices. The default NULL includes all columns. The receptor sequence column is always included regardless of this argument's value.

count_col

Does nothing.

verbose

Logical. If TRUE, generates messages about the tasks performed and their progress, as well as relevant properties of intermediate outputs. Messages are sent to stderr().

Author

Brian Neal ()

References

Hai Yang, Jason Cham, Brian Neal, Zenghua Fan, Tao He and Li Zhang. (2023). NAIR: Network Analysis of Immune Repertoire. Frontiers in Immunology, vol. 14. doi: 10.3389/fimmu.2023.1181825

Webpage for the NAIR package

Examples

set.seed(42)
raw_data <- simulateToyData()

# Remove sequences shorter than 13 characters,
# as well as sequences containing the subsequence "GGGG".
# Keep variables for clone sequence, clone frequency and sample ID
filterInputData(
  raw_data,
  seq_col = "CloneSeq",
  min_seq_length = 13,
  drop_matches = "GGGG",
  subset_cols =
    c("CloneSeq", "CloneFrequency", "SampleID"),
  verbose = TRUE
)