Filter Data Rows and Subset Data Columns — filterInputData • NAIR

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 = NULL,
  verbose = FALSE
)

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: Optional. Specifies the column of data containing a measure of abundance, e.g., clone count or unique molecular identifier (UMI) count. Accepts either the column name as a character string or the numeric column index. If provided, data rows with NA count values will be removed.
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().

Value

A data frame.

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

Author

Brian Neal (Brian.Neal@ucsf.edu)

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
)
#> Input data contains 200 rows.
#> Removing sequences with length fewer than 13 characters...
#>  Done. 136 rows remaining.
#> Removing sequences containing matches to “GGGG”...
#>  Done. 105 rows remaining.
#>          CloneSeq CloneFrequency SampleID
#> 1   TTGAGGAAATTCG    0.007873775  Sample1
#> 2   GGAGATGAATCGG    0.007777102  Sample1
#> 3   GTCGGGTAATTGG    0.009094910  Sample1
#> 4   GCCGGGTAATTCG    0.010160859  Sample1
#> 5   GAAAGAGAATTCG    0.009336593  Sample1
#> 6   AGGTGGGAATTCG    0.010369470  Sample1
#> 7   GCGCAGCAATTGG    0.007939920  Sample1
#> 9   AGGGACAAATTGG    0.008362229  Sample1
#> 10  GAGGAAGAATCGG    0.012432679  Sample1
#> 14  GGTTAGGAATTCG    0.011582972  Sample1
#> 16  CAAGGGAAATTCG    0.012557336  Sample1
#> 19  TCGATGGAATTGG    0.014465359  Sample1
#> 20  TAGAGAGAATCGG    0.011305673  Sample1
#> 21  GGGAAAGAATTGG    0.010964773  Sample1
#> 22  CGGAGAGAATCGG    0.011819567  Sample1
#> 23  AAGGGATAATTGG    0.008166339  Sample1
#> 26  GAGAATAAATTGC    0.008418198  Sample1
#> 27  GGAATAGAATTGG    0.013554596  Sample1
#> 29  GGAAAGAAATTGG    0.011921328  Sample1
#> 30  GGGCGGGAATCGG    0.010537376  Sample1
#> 37  GGGAGGGAATTGG    0.006339725  Sample1
#> 38  GAGGCGGAATCGG    0.007074950  Sample1
#> 39  AGTAGAGAATCGG    0.014307629  Sample1
#> 42  GGGAAGGAATCGG    0.012397062  Sample1
#> 45  GGGCGGGAATTGC    0.006779842  Sample1
#> 46  TGGTCGGAATTGG    0.012854988  Sample1
#> 47  GGGTGAAAATTGG    0.010926612  Sample1
#> 48  ATGGGAGAATTCG    0.008573384  Sample1
#> 50  GGGCGATAATTGG    0.009135615  Sample1
#> 53  GGGTGGGAATTGG    0.013697062  Sample1
#> 54  GAGACGGAATTGG    0.011175927  Sample1
#> 56  AGCGGAGAATTGG    0.011249704  Sample1
#> 57  GGAGCTGAATCGG    0.013335810  Sample1
#> 64  TGAGGGAAATTGG    0.004574167  Sample1
#> 66  GGATAGGAATCGG    0.006746770  Sample1
#> 68  AGGTCGGAATTGG    0.008301173  Sample1
#> 69  GTGAGGAAATTGG    0.012079058  Sample1
#> 70  GTGTGGGAATCGG    0.005960664  Sample1
#> 71  CGAGGGAAATTGG    0.010135419  Sample1
#> 73  GTGGTGGAATTGG    0.008352053  Sample1
#> 75  GGATAGGAATTCG    0.007827983  Sample1
#> 76  AGTGGAGAATTGG    0.008003521  Sample1
#> 79  AGGGTGAAATTCG    0.007204695  Sample1
#> 82  GGAGGCGAATCGG    0.012758315  Sample1
#> 84  TAGGGCCAATTGG    0.009295889  Sample1
#> 85  GAGAGCAAATCGG    0.009331505  Sample1
#> 87  AGGGAGGAATTGG    0.009099998  Sample1
#> 88  GGGAGATAATCGG    0.009171231  Sample1
#> 90  AGAGGAGAATTGG    0.008303717  Sample1
#> 92  GAAGGGAAATTGG    0.012023090  Sample1
#> 93  GAGGCTGAATCGG    0.011318393  Sample1
#> 95  GGATGAGAATCGG    0.011381994  Sample1
#> 96  AGGAGGAAATTGG    0.008306261  Sample1
#> 98  GGAACGAAATTGG    0.009013501  Sample1
#> 99  GGAGGGAAATTGC    0.007718589  Sample1
#> 100 AGAGCTGAATTGG    0.007046965  Sample1
#> 102 AAAATAAAATTGG    0.011245432  Sample2
#> 104 AAAACAAAATTGG    0.009633772  Sample2
#> 106 AAGTAGGAATTGC    0.010566252  Sample2
#> 107 AAAAAAGAATTGC    0.012219664  Sample2
#> 111 GGAGAGAAATTGC    0.012035952  Sample2
#> 113 TAAAGGAAATTGC    0.014154213  Sample2
#> 115 GGAAAAAAATTGG    0.013889778  Sample2
#> 117 CTGGCAAAATTGC    0.012369975  Sample2
#> 119 AAAAACTAATTGC    0.011498732  Sample2
#> 120 TGAAAAGAATTGG    0.007715925  Sample2
#> 121 AAAAGAAAATTCG    0.009733979  Sample2
#> 124 AAGATAAAATTGC    0.003908066  Sample2
#> 127 AGAAGAAAATTGC    0.006833548  Sample2
#> 128 GGAGGAAAATTCG    0.007493243  Sample2
#> 129 AAAAGAGAATTCG    0.007888503  Sample2
#> 130 AAAAGAGAATCGG    0.010104187  Sample2
#> 132 AGAGAAAAATCGG    0.005962306  Sample2
#> 134 AAAAATAAATTGC    0.005619932  Sample2
#> 136 AAGCAGAAATTGG    0.009703360  Sample2
#> 139 TAAAAAAAATTGC    0.008868303  Sample2
#> 140 CAGTAAAAATTCG    0.005539210  Sample2
#> 141 ACAGGACAATTGG    0.004926835  Sample2
#> 142 AGATAGAAATTCG    0.009060366  Sample2
#> 146 AGGATGTAATCGG    0.014257203  Sample2
#> 148 AAGGAAAAATTGC    0.014524421  Sample2
#> 149 AAAAATAAATCGG    0.014911331  Sample2
#> 150 GAAAAAAAATTGC    0.012589873  Sample2
#> 152 AGAAAAAAATCGG    0.007958091  Sample2
#> 153 AAAAAAAAATTGC    0.007571182  Sample2
#> 155 ACAAAAGAATTGC    0.012208530  Sample2
#> 156 GAAATAGAATTCG    0.012308737  Sample2
#> 160 AGAAAAGAATTGC    0.012266984  Sample2
#> 162 AGAGAAGAATTGG    0.010986564  Sample2
#> 163 GAAAAAGAATTCG    0.007799430  Sample2
#> 166 AAAATGGAATTCG    0.013664313  Sample2
#> 168 AAGAAGGAATTGG    0.005739624  Sample2
#> 169 AGAAGAAAATTCG    0.010374189  Sample2
#> 172 AGAGCAAAATTGG    0.007610151  Sample2
#> 175 GAAATTAAATTGC    0.014028954  Sample2
#> 176 GGGTAAAAATTGC    0.006410453  Sample2
#> 179 AAGAAGAAATTGG    0.013230083  Sample2
#> 181 AGAAGAAAATTGC    0.012854308  Sample2
#> 183 AAAAACAAATTGC    0.009338719  Sample2
#> 185 AAATAAAAATCGG    0.013355342  Sample2
#> 189 GAAGAACAATTGC    0.009104903  Sample2
#> 192 CAAAATAAATTCG    0.012854308  Sample2
#> 196 AAAAAGAAATTGC    0.011674094  Sample2
#> 197 AGAAGAAAATTGC    0.012768018  Sample2
#> 200 AAAAGAAAATTGC    0.010546767  Sample2