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.

Value

The ASV abundance matrix (asv_abund_matrix)

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.

Examples

# \donttest{
# 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
)
#> Existing files found in the output directory. Overwriting existing files.
#> Rows: 2 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (3): primer_name, forward, reverse
#> dbl (18): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl  (4): already_trimmed, count_all_samples, multithread, verbose
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Rows: 2 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (3): primer_name, forward, reverse
#> dbl (18): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl  (4): already_trimmed, count_all_samples, multithread, verbose
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Rows: 4 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): sample_name, primer_name, organism
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Creating output directory: /tmp/RtmpBJNmaI/demulticoder_run/prefiltered_sequences

cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
#> Running cutadapt 3.5 for its sequence data

#> Running cutadapt 3.5 for rps10 sequence data

make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
#> 80608 total bases in 307 reads from 2 samples will be used for learning the error rates.
#> Error rate plot for the  Forward  read of primer pair  its
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 163 reads in 84 unique sequences.
#> Sample 2 - 144 reads in 96 unique sequences.
#> 82114 total bases in 307 reads from 2 samples will be used for learning the error rates.
#> Error rate plot for the  Reverse  read of primer pair  its
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 163 reads in 128 unique sequences.
#> Sample 2 - 144 reads in 119 unique sequences.


#> 91897 total bases in 327 reads from 2 samples will be used for learning the error rates.
#> Error rate plot for the  Forward  read of primer pair  rps10
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 145 reads in 107 unique sequences.
#> Sample 2 - 182 reads in 133 unique sequences.
#> 91567 total bases in 327 reads from 2 samples will be used for learning the error rates.
#> Error rate plot for the  Reverse  read of primer pair  rps10
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 145 reads in 114 unique sequences.
#> Sample 2 - 182 reads in 170 unique sequences.


#> $its
#> [1] "/tmp/RtmpBJNmaI/demulticoder_run/asvabund_matrixDADA2_its.RData"
#> 
#> $rps10
#> [1] "/tmp/RtmpBJNmaI/demulticoder_run/asvabund_matrixDADA2_rps10.RData"
#> 
# }