convert_animalcules_silva: Create a multi-assay experiment from MetaScope output for...

In compbiomed/MetaScope: Tools and functions for preprocessing 16S and metagenomic sequencing microbiome data

Create a multi-assay experiment from MetaScope output for usage with animalcules with the SILVA 13_8 database

Description

Upon completion of the MetaScope pipeline, users can analyze and visualize abundances in their samples using the animalcules package. This function allows interoperability of metascope_id output with both animalcules and QIIME. After running this function, the user should save the returned MAE to an RDS file using a function like saveRDS to upload the output into the animalcules package. NOTE: This function is for outputs that were generated with the SILVA 13_8 database.

Usage

convert_animalcules_silva(
  meta_counts,
  annot_path,
  which_annot_col,
  end_string = ".metascope_id.csv",
  qiime_biom_out = FALSE,
  path_to_write = ".",
  caching = TRUE
)

Arguments

meta_counts	A vector of filepaths to the counts ID CSVs output by metascope_id() created with the SILVA database.
annot_path	The filepath to the CSV annotation file for the samples.
which_annot_col	The name of the column of the annotation file containing the sample IDs. These should be the same as the meta_counts root filenames.
end_string	The end string used at the end of the metascope_id files. Default is ".metascope_id.csv".
qiime_biom_out	Would you also like a qiime-compatible biom file output? If yes, two files will be saved: one is a biom file of the counts table, and the other is a specifically formatted mapping file of metadata information. Default is FALSE.
path_to_write	If qiime_biom_out = TRUE, where should output QIIME files be written? Should be a character string of the folder path. Default is '.', i.e. the current working directory.
caching	Whether to use BiocFileCache when downloading genomes. Default is FALSE.

Value

Returns a MultiAssay Experiment file of combined sample counts data and/or saved biom file and mapping file for analysis with QIIME. The MultiAssayExperiment will have a counts assay ("MGX").

Examples

tempfolder <- tempfile()
dir.create(tempfolder)

# Create three different samples
samp_names <- c("X123", "X456", "X789")
all_files <- file.path(tempfolder,
                       paste0(samp_names, ".csv"))

create_IDcsv <- function (out_file) {
  final_taxids <- c("AY846380.1.2583", "AY909584.1.2313", "HG531388.1.1375")
  final_genomes <- rep("Genome name", 3)
  best_hit <- sample(seq(100, 1050), 3)
  proportion <- best_hit/sum(best_hit) |> round(2)
  EMreads <- best_hit + round(runif(3), 1)
  EMprop <- proportion + 0.003
  dplyr::tibble("TaxonomyID" = final_taxids,
                "Genome" = final_genomes,
                "read_count" = best_hit, "Proportion" = proportion,
                "EMreads" = EMreads, "EMProportion" = EMprop) |>
    dplyr::arrange(dplyr::desc(.data$read_count)) |>
    utils::write.csv(file = out_file, row.names = FALSE)
  message("Done!")
  return(out_file)
}
out_files <- vapply(all_files, create_IDcsv, FUN.VALUE = character(1))

# Create annotation data for samples
annot_dat <- file.path(tempfolder, "annot.csv")
dplyr::tibble(Sample = samp_names, RSV = c("pos", "neg", "pos"),
              month = c("March", "July", "Aug"),
              yrsold = c(0.5, 0.6, 0.2)) |>
  utils::write.csv(file = annot_dat,
                   row.names = FALSE)

# Convert samples to MAE
outMAE <- convert_animalcules_silva(meta_counts = out_files,
                              annot_path = annot_dat,
                              which_annot_col = "Sample",
                              end_string = ".metascope_id.csv",
                              qiime_biom_out = FALSE,
                              caching = TRUE)

unlink(tempfolder, recursive = TRUE)

compbiomed/MetaScope documentation built on April 1, 2024, 5:35 p.m.