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Reshape a wide sample table into a long-format manifest

Source: R/io.R
manifest_from_wide.Rd

Many sample sheets start wide: one row per subject, with one column per assay-and-role combination (e.g. wes_tumor_id, wes_normal_id, scrna_id). This turns such a table into the long format validate_manifest() expects, one row per non-missing sample id.

Usage

manifest_from_wide(x, id_cols, subject_id = "subject_id")

Arguments

x

A data.frame or tibble, one row per subject.

id_cols

A data.frame or tibble describing the columns of x that hold sample ids, with columns:

  • column: name of a column in x.

  • assay: the assay that column's ids belong to.

  • role: optional; the role of that column's samples. Defaults to NA for every row when absent.

subject_id

Character scalar naming the subject id column in x. Default "subject_id".

Value

A long-format tibble: one row per non-missing sample id in any id_cols$column, with subject_id, assay, sample_id, role, and every column of x that is not in id_cols$column. Pass it to validate_manifest() next.

Details

A blank or NA value in an id column contributes no row, so a subject missing one assay is not stamped with an empty sample id.

See also

read_manifest(), validate_manifest()

Examples

wide <- data.frame(
  subject_id = c("R1", "R2"),
  species = "rat",
  wes_tumor_id = c("WES_T1", "WES_T2"),
  wes_normal_id = c("WES_N1", NA),
  scrna_id = c("SC_1", "SC_2")
)
id_cols <- data.frame(
  column = c("wes_tumor_id", "wes_normal_id", "scrna_id"),
  assay = c("wes", "wes", "scrna"),
  role = c("tumor", "normal", NA)
)

long <- manifest_from_wide(wide, id_cols)
long
#> # A tibble: 5 × 5
#>   subject_id species assay sample_id role  
#>   <chr>      <chr>   <chr> <chr>     <chr> 
#> 1 R1         rat     wes   WES_T1    tumor 
#> 2 R2         rat     wes   WES_T2    tumor 
#> 3 R1         rat     wes   WES_N1    normal
#> 4 R1         rat     scrna SC_1      NA    
#> 5 R2         rat     scrna SC_2      NA    
validate_manifest(long)
#> $subject_tbl
#> # A tibble: 2 × 2
#>   subject_id species
#>   <chr>      <chr>  
#> 1 R1         rat    
#> 2 R2         rat    
#> 
#> $sample_map
#> # A tibble: 5 × 4
#>   subject_id assay sample_id role  
#>   <chr>      <chr> <chr>     <chr> 
#> 1 R1         wes   WES_T1    tumor 
#> 2 R2         wes   WES_T2    tumor 
#> 3 R1         wes   WES_N1    normal
#> 4 R1         scrna SC_1      NA    
#> 5 R2         scrna SC_2      NA    
#> 
#> $completeness_tbl
#> # A tibble: 4 × 3
#>   subject_id assay n_samples
#>   <chr>      <chr>     <int>
#> 1 R1         scrna         1
#> 2 R1         wes           2
#> 3 R2         scrna         1
#> 4 R2         wes           1
#>