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Read MIPTools tables

Source: R/read-tbl.R
read-tbl.Rd

The read_tbl_*() family of functions is designed to read data tables generated by the software program MIPtools. Data is read lazily using the vroom package. Data can be filtered, retaining all rows that satisfy the conditions. To be retained, the row in question must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA, the row will be dropped.

Usage

read_tbl_reference(.tbl, ...)

read_tbl_alternate(.tbl, ...)

read_tbl_coverage(.tbl, ...)

read_tbl_genotype(.tbl, ...)

read_tbl_haplotype(.tbl, ..., .col_select = NULL)

read_tbl_ref_alt_cov(
  .tbl_ref,
  .tbl_alt,
  .tbl_cov,
  ...,
  chrom = deprecated(),
  gene = deprecated()
)

Arguments

.tbl

File path to the table.

...

data-masking Expressions that return a logical value and are used to filter the data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

.col_select

One or more selection expressions, like in dplyr::select(). Use c() or list() to use more than one expression. See ?dplyr::select for details on available selection options.

.tbl_ref

File path to the reference table.

.tbl_alt

File path to the alternate table.

.tbl_cov

File path to the coverage table.

chrom

[Deprecated] The chromosome(s) to filter to.

gene

[Deprecated] The gene(s) to filter to.

Value

A tibble(). The first six columns contain the metadata associated with each sample and mutation. The last column contains the information parsed from the table. In some cases, this may be the umi_count and in other cases it may be the coverage of the associated data point.

Data structure

Input data must contain six rows of metadata. The metadata can vary depending on what type of file is read, but typically contains information about the location of a mutation. The remaining rows represent the data for each sample sequenced.

Useful filter functions

The dplyr::filter() function is employed to subset the rows of the data applying the expressions in ... to the column values to determine which rows should be retained.

There are many functions and operators that are useful when constructing the expressions used to filter the data:

See also

vroom::vroom() dplyr::filter()

Examples

# Get path to example file
ref_file <- miplicorn_example("reference_AA_table.csv")
alt_file <- miplicorn_example("alternate_AA_table.csv")
cov_file <- miplicorn_example("coverage_AA_table.csv")
ref_file
#> [1] "/home/runner/work/_temp/Library/miplicorn/extdata/reference_AA_table.csv"

# Input sources -------------------------------------------------------------
# Read from a path
read_tbl_reference(ref_file)
#> # A tibble: 6,344 × 8
#>    sample     gene_id       gene  mutation_name  exonic_func  aa_change targeted
#>    <chr>      <chr>         <chr> <chr>          <chr>        <chr>     <chr>   
#>  1 D10-JJJ-23 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  2 D10-JJJ-43 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  3 D10-JJJ-55 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  4 D10-JJJ-5  PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  5 D10-JJJ-47 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  6 D10-JJJ-15 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  7 D10-JJJ-27 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  8 D10-JJJ-10 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  9 D10-JJJ-28 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> 10 D10-JJJ-52 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> # … with 6,334 more rows, and 1 more variable: ref_umi_count <dbl>

# You can also use paths directly
# read_tbl_alternate("alternate_AA_table.csv")

# Read entire file ----------------------------------------------------------
read_tbl_coverage(cov_file)
#> # A tibble: 6,344 × 8
#>    sample    gene_id gene  mutation_name exonic_func aa_change targeted coverage
#>    <chr>     <chr>   <chr> <chr>         <chr>       <chr>     <chr>       <dbl>
#>  1 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           608
#>  2 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes            20
#>  3 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           158
#>  4 D10-JJJ-5 PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             2
#>  5 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             1
#>  6 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           129
#>  7 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             0
#>  8 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             0
#>  9 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes            90
#> 10 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           175
#> # … with 6,334 more rows

# Data filtering ------------------------------------------------------------
# Filtering by one criterion
read_tbl_reference(ref_file, gene == "atp6")
#> # A tibble: 260 × 8
#>    sample     gene_id       gene  mutation_name  exonic_func  aa_change targeted
#>    <chr>      <chr>         <chr> <chr>          <chr>        <chr>     <chr>   
#>  1 D10-JJJ-23 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  2 D10-JJJ-43 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  3 D10-JJJ-55 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  4 D10-JJJ-5  PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  5 D10-JJJ-47 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  6 D10-JJJ-15 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  7 D10-JJJ-27 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  8 D10-JJJ-10 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  9 D10-JJJ-28 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> 10 D10-JJJ-52 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> # … with 250 more rows, and 1 more variable: ref_umi_count <dbl>

# Filtering by multiple criteria within a single logical expression
read_tbl_alternate(alt_file, gene == "atp6" & targeted == "Yes")
#> # A tibble: 156 × 8
#>    sample     gene_id       gene  mutation_name  exonic_func  aa_change targeted
#>    <chr>      <chr>         <chr> <chr>          <chr>        <chr>     <chr>   
#>  1 D10-JJJ-23 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  2 D10-JJJ-43 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  3 D10-JJJ-55 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  4 D10-JJJ-5  PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  5 D10-JJJ-47 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  6 D10-JJJ-15 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  7 D10-JJJ-27 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  8 D10-JJJ-10 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  9 D10-JJJ-28 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> 10 D10-JJJ-52 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> # … with 146 more rows, and 1 more variable: alt_umi_count <dbl>
read_tbl_coverage(cov_file, gene == "atp6" | targeted == "Yes")
#> # A tibble: 2,496 × 8
#>    sample    gene_id gene  mutation_name exonic_func aa_change targeted coverage
#>    <chr>     <chr>   <chr> <chr>         <chr>       <chr>     <chr>       <dbl>
#>  1 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           608
#>  2 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes            20
#>  3 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           158
#>  4 D10-JJJ-5 PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             2
#>  5 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             1
#>  6 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           129
#>  7 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             0
#>  8 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes             0
#>  9 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes            90
#> 10 D10-JJJ-… PF3D7_… atp6  atp6-Ala623G… missense_v… Ala623Glu Yes           175
#> # … with 2,486 more rows

# When multiple expressions are used, they are combined using &
read_tbl_reference(ref_file, gene == "atp6", targeted == "Yes")
#> # A tibble: 156 × 8
#>    sample     gene_id       gene  mutation_name  exonic_func  aa_change targeted
#>    <chr>      <chr>         <chr> <chr>          <chr>        <chr>     <chr>   
#>  1 D10-JJJ-23 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  2 D10-JJJ-43 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  3 D10-JJJ-55 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  4 D10-JJJ-5  PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  5 D10-JJJ-47 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  6 D10-JJJ-15 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  7 D10-JJJ-27 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  8 D10-JJJ-10 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  9 D10-JJJ-28 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> 10 D10-JJJ-52 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> # … with 146 more rows, and 1 more variable: ref_umi_count <dbl>

# Read multiple files together ----------------------------------------------
read_tbl_ref_alt_cov(ref_file, alt_file, cov_file)
#> # A tibble: 6,344 × 10
#>    sample     gene_id       gene  mutation_name  exonic_func  aa_change targeted
#>    <chr>      <chr>         <chr> <chr>          <chr>        <chr>     <chr>   
#>  1 D10-JJJ-23 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  2 D10-JJJ-43 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  3 D10-JJJ-55 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  4 D10-JJJ-5  PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  5 D10-JJJ-47 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  6 D10-JJJ-15 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  7 D10-JJJ-27 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  8 D10-JJJ-10 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#>  9 D10-JJJ-28 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> 10 D10-JJJ-52 PF3D7_0106300 atp6  atp6-Ala623Glu missense_va… Ala623Glu Yes     
#> # … with 6,334 more rows, and 3 more variables: ref_umi_count <dbl>,
#> #   alt_umi_count <dbl>, coverage <dbl>