Package 'demulticoder'

Assign taxonomy functions

Description

Assign taxonomy functions

Usage

assign_tax(
  analysis_setup,
  asv_abund_matrix,
  retrieve_files = FALSE,
  overwrite_existing = FALSE,
  db_rps10 = "oomycetedb.fasta",
  db_its = "fungidb.fasta",
  db_16S = "bacteriadb.fasta",
  db_other1 = "otherdb1.fasta",
  db_other2 = "otherdb2.fasta"
)

Arguments

analysis_setup	An object containing directory paths and data tables, produced by the prepare_reads function
asv_abund_matrix	The final abundance matrix containing amplified sequence variants
retrieve_files	Logical, TRUE/FALSE whether to copy files from the temp directory to the output directory. Default is FALSE.
overwrite_existing	Logical, indicating whether to remove or overwrite existing files and directories from previous runs. Default is FALSE.
db_rps10	The reference database for the rps10 metabarcode
db_its	The reference database for the ITS metabarcode
db_16S	The SILVA 16S-rRNA reference database provided by the user
db_other1	The reference database for other metabarcode 1 (assumes format is like SILVA DB entries)
db_other2	The reference database for other metabarcode 2 (assumes format is like SILVA DB entries)

Details

At this point, 'DADA2' function assignTaxonomy is used to assign taxonomy to the inferred ASVs.

Value

Taxonomic assignments of each unique ASV sequence

Examples

# Assign taxonomies to ASVs on by metabarcode
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files=FALSE,
overwrite_existing = TRUE
)

---

Filter ASV abundance matrix and convert to 'taxmap' and 'phyloseq' objects

Description

Filter ASV abundance matrix and convert to 'taxmap' and 'phyloseq' objects

Usage

convert_asv_matrix_to_objs(
  analysis_setup,
  min_read_depth = 0,
  minimum_bootstrap = 0,
  save_outputs = FALSE
)

Arguments

analysis_setup	An object containing directory paths and data tables, produced by the prepare_reads function
min_read_depth	ASV filter parameter. If mean read depth of across all samples is less than this threshold, ASV will be filtered.
minimum_bootstrap	Set threshold for bootstrap support value for taxonomic assignments. Below designated minimum bootstrap threshold, taxonomic assignments will be set to N/A
save_outputs	Logical, indicating whether to save the resulting phyloseq and 'taxmap' objects. If TRUE, the objects will be saved; if FALSE, they will only be available in the global environment. Default is FALSE.

Value

ASV matrix converted to 'taxmap' object

Examples

# Convert final matrix to 'taxmap' and phyloseq objects for downstream analysis steps
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files=FALSE,
overwrite_existing=TRUE
)
objs<-convert_asv_matrix_to_objs(
analysis_setup
)

---

Main command to trim primers using 'Cutadapt' and core 'DADA2' functions

Description

Main command to trim primers using 'Cutadapt' and core 'DADA2' functions

Usage

cut_trim(analysis_setup, cutadapt_path, overwrite_existing = FALSE)

Arguments

analysis_setup	An object containing directory paths and data tables, produced by the prepare_reads function
cutadapt_path	Path to the 'Cutadapt' program.
overwrite_existing	Logical, indicating whether to remove or overwrite existing files and directories from previous runs. Default is FALSE.

Details

If samples are comprised of two different metabarcodes (like ITS1 and rps10), reads will also be demultiplexed prior to 'DADA2'-specific read trimming steps.

Value

Trimmed reads, primer counts, quality plots, and ASV matrix.

Examples

# Remove remaining primers from raw reads, demultiplex pooled barcoded samples,
# and then trim reads based on specific 'DADA2' parameters
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)

---

Make an amplified sequence variant (ASV) abundance matrix for each of the input barcodes

Description

Make an amplified sequence variant (ASV) abundance matrix for each of the input barcodes

Usage

make_asv_abund_matrix(analysis_setup, overwrite_existing = FALSE)

Arguments

analysis_setup	An object containing directory paths and data tables, produced by the prepare_reads function
overwrite_existing	Logical, indicating whether to overwrite existing results. Default is FALSE.

Details

The function processes data for each unique barcode separately, inferring ASVs, merging reads, and creating an ASV abundance matrix. To do this, the 'DADA2' core denoising alogrithm is used to infer ASVs.

Value

The ASV abundance matrix (asv_abund_matrix)

Examples

# The primary wrapper function for 'DADA2' ASV inference steps
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)

---

Prepare reads for primer trimming using 'Cutadapt'

Description

Prepare reads for primer trimming using 'Cutadapt'

Usage

prepare_reads(
  data_directory = "data",
  output_directory = tempdir(),
  tempdir_path = NULL,
  tempdir_id = "demulticoder_run",
  overwrite_existing = FALSE
)

Arguments

data_directory	Directory path where the user has placed raw FASTQ (forward and reverse reads), metadata.csv, and primerinfo_params.csv files. Default is "data".
output_directory	User-specified directory for outputs. Default is tempdir().
tempdir_path	Path to a temporary directory. If NULL, a temporary directory path will be identified using the tempdir() command.
tempdir_id	ID for temporary directories. The user can provide any helpful ID, whether it be a date or specific name for the run. Default is "demulticoder_run"
overwrite_existing	Logical, indicating whether to remove or overwrite existing files and directories from previous runs. Default is FALSE.

Value

A list containing data tables, including metadata, primer sequences to search for based on orientation, paths for trimming reads, and user-defined parameters for all subsequent steps.

Examples

# Pre-filter raw reads and parse metadata and primer_information to prepare
# for primer trimming and filter
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  overwrite_existing = TRUE
)

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