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Assign taxonomy functions

Source: R/assign_taxonomy.R
assign_tax.Rd

Assign taxonomy functions

Usage

assign_tax(
  analysis_setup,
  asv_abund_matrix,
  tryRC = FALSE,
  verbose = FALSE,
  multithread = FALSE,
  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

ASV abundance matrix.

tryRC

Whether to try reverse complementing sequences during taxonomic assignment

verbose

Logical, indicating whether to display verbose output

multithread

Logical, indicating whether to use multithreading

retrieve_files

Specify TRUE/FALSE whether to copy files from the temp directory to the output directory

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 locus

db_its

The reference database for the ITS locus

db_16S

The reference database for the 16S locus

db_other1

The reference database for different locus 1 (assumes format is like SILVA DB entries)

db_other2

The reference database for a different locus 2 (assumes format is like SILVA DB entries)

Value

Taxonomic assignments of each unique ASV sequence

Examples

# Assign taxonomies to ASVs on a per barcode basis
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  tempdir_path = tempdir(),
  tempdir_id = "demulticoder_run_temp",
  overwrite_existing = TRUE
)
#> Rows: 2 Columns: 22
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (3): primer_name, forward, reverse
#> dbl (16): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl  (3): already_trimmed, 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: 22
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (3): primer_name, forward, reverse
#> dbl (16): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl  (3): already_trimmed, 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/Rtmp23cMn7/demulticoder_run_temp/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
)
#> 711900 total bases in 2698 reads from 2 samples will be used for learning the error rates.
#> Initializing error rates to maximum possible estimate.
#> selfConsist step 1 ..
#>    selfConsist step 2
#>    selfConsist step 3
#> Convergence after  3  rounds.
#> Error rate plot for the Forward read of primer pair its 
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 1481 reads in 660 unique sequences.
#> Sample 2 - 1217 reads in 614 unique sequences.
#> 725393 total bases in 2698 reads from 2 samples will be used for learning the error rates.
#> Initializing error rates to maximum possible estimate.
#> selfConsist step 1 ..
#>    selfConsist step 2
#>    selfConsist step 3
#> Convergence after  3  rounds.
#> Error rate plot for the Reverse read of primer pair its 
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 1481 reads in 1021 unique sequences.
#> Sample 2 - 1217 reads in 815 unique sequences.
#> 1316 paired-reads (in 21 unique pairings) successfully merged out of 1418 (in 32 pairings) input.
#> Duplicate sequences in merged output.
#> 1065 paired-reads (in 25 unique pairings) successfully merged out of 1110 (in 28 pairings) input.
#> Duplicate sequences detected and merged.
#> Identified 0 bimeras out of 38 input sequences.

#> 824778 total bases in 2935 reads from 2 samples will be used for learning the error rates.
#> Initializing error rates to maximum possible estimate.
#> selfConsist step 1 ..
#>    selfConsist step 2
#> Convergence after  2  rounds.
#> Error rate plot for the Forward read of primer pair rps10 
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 1429 reads in 933 unique sequences.
#> Sample 2 - 1506 reads in 1018 unique sequences.
#> 821851 total bases in 2935 reads from 2 samples will be used for learning the error rates.
#> Initializing error rates to maximum possible estimate.
#> selfConsist step 1 ..
#>    selfConsist step 2
#>    selfConsist step 3
#> Convergence after  3  rounds.
#> Error rate plot for the Reverse read of primer pair rps10 
#> Warning: log-10 transformation introduced infinite values.
#> Sample 1 - 1429 reads in 1044 unique sequences.
#> Sample 2 - 1506 reads in 1284 unique sequences.
#> 1420 paired-reads (in 2 unique pairings) successfully merged out of 1422 (in 4 pairings) input.
#> 1503 paired-reads (in 5 unique pairings) successfully merged out of 1504 (in 6 pairings) input.

#> Identified 0 bimeras out of 5 input sequences.


#> $its
#> [1] "/tmp/Rtmp23cMn7/demulticoder_run_temp/asvabund_matrixDADA2_its.RData"
#> 
#> $rps10
#> [1] "/tmp/Rtmp23cMn7/demulticoder_run_temp/asvabund_matrixDADA2_rps10.RData"
#> 
assign_tax(
analysis_setup,
asv_abund_matrix, 
retrieve_files=FALSE, 
overwrite_existing = TRUE
)
#> Duplicate sequences detected and merged.
#>   samplename_barcode input filtered denoisedF denoisedR merged nonchim
#> 1             S1_its  2564     1481      1427      1433   1316    1316
#> 2             S2_its  1996     1217      1145      1124   1065    1065
#>   samplename_barcode input filtered denoisedF denoisedR merged nonchim
#> 1           S1_rps10  1830     1429      1429      1422   1420    1420
#> 2           S2_rps10  2090     1506      1505      1505   1503    1503