Deconvolute a combined TMA spreadsheet — deconvolute • TMAtools

Reads TMA spreadsheet from tma_file which must contain one "TMA map" sheet and one or more biomarker-specific sheets.

Usage

deconvolute(tma_file, metadata = NULL, output_file = NULL, tma_id = NULL)

Arguments

tma_file: Path to input Excel spreadsheet. Both .xlsx and .xls formats are supported. Must contain one "TMA map" sheet and one or more biomarker-specific sheets (eg, output file of combine_tma_spreadsheets()).
metadata: Optional data.frame with at least core_id and accession_id columns, plus any other metadata columns that should be added to the output (eg, patient age, sex, etc.).
output_file: Optional path to output Excel spreadsheet, which can be used as input to translate_scores().
tma_id: Optional TMA identifier (column tma_id will be added to output).

Value

A data frame with the deconvoluted data.

Details

The function will match core IDs from the TMA map sheet with the biomarker-specific sheets and return a data frame with a "core_id" column and as many columns for each biomarker as necessary.

For instance, if core ID 1 has 3 values for biomarker A, the output will contain 3 columns for biomarker A (A.c1, A.c2, A.c3); if another core ID has 2 values for biomarker A, its corresponding A.c1 and A.c2 columns will be filled with the values for that core ID and the A.c3 column will be filled with NA.

If there are no values for a given core ID in a biomarker-specific sheet, the corresponding columns will all be filled with NA.

Examples

library(TMAtools)
# grab folder with example TMA datasets
tma_dir <- system.file("extdata", "tma1", package = "TMAtools")
# define output files
combined_tma_file <- "combined_tma.xlsx"
deconvoluted_tma_file <- "deconvoluted_tma.xlsx"

# combine TMA datasets
combine_tma_spreadsheets(
 tma_dir = tma_dir,
 output_file = combined_tma_file,
 biomarker_sheet_index = 2,
 valid_biomarkers = c("ER", "p53") # optional, but recommended to avoid misspelling errors
)

# deconvolute combined TMA dataset
deconvoluted_data <- deconvolute(
   tma_file = combined_tma_file,
   output_file = deconvoluted_tma_file
)
print(deconvoluted_data)
#> # A tibble: 17 × 7
#>    core_id ER.c1 ER.c2 ER.c3 p53.c1 p53.c2 p53.c3
#>    <chr>   <chr> <chr> <chr> <chr>  <chr>  <chr> 
#>  1 1       9     9     9     3      0      5     
#>  2 4       2     0     0     8      4      4     
#>  3 10      x     9     0     3      5      5     
#>  4 13      9     1     1     5      1      3     
#>  5 3       2     x     1     5      9      9     
#>  6 12      9     x     9     1      2      4     
#>  7 5       2     2     2     3      2      1     
#>  8 14      1     2     1     3      4      5     
#>  9 2       1     2     2     8      9      x     
#> 10 11      2     x     0     8      9      5     
#> 11 6       x     2     2     5      3      3     
#> 12 9       0     1     x     3      3      2     
#> 13 15      1     1     2     3      x      1     
#> 14 8       0     1     x     x      9      3     
#> 15 17      0     1     2     8      3      2     
#> 16 7       x     1     x     4      4      5     
#> 17 16      x     2     NA    8      4      NA