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Recluster sequences of an object of class physeq or a list of DNA sequences

Source: R/dada_phyloseq.R
postcluster_pq.Rd

lifecycle-maturing

This function use the merge_taxa_vec function to merge taxa into clusters.

Usage

postcluster_pq(
  physeq = NULL,
  dna_seq = NULL,
  nproc = 1,
  method = "clusterize",
  id = 0.97,
  vsearchpath = "vsearch",
  tax_adjust = 0,
  vsearch_cluster_method = "--cluster_size",
  vsearch_args = "--strand both",
  keep_temporary_files = FALSE,
  swarmpath = "swarm",
  d = 1,
  swarm_args = "--fastidious",
  method_clusterize = "overlap",
  ...
)

asv2otu(
  physeq = NULL,
  dna_seq = NULL,
  nproc = 1,
  method = "clusterize",
  id = 0.97,
  vsearchpath = "vsearch",
  tax_adjust = 0,
  vsearch_cluster_method = "--cluster_size",
  vsearch_args = "--strand both",
  keep_temporary_files = FALSE,
  swarmpath = "swarm",
  d = 1,
  swarm_args = "--fastidious",
  method_clusterize = "overlap",
  ...
)

Arguments

physeq

(required): a phyloseq-class object obtained using the phyloseq package.

dna_seq

You may directly use a character vector of DNA sequences in place of physeq args. When physeq is set, dna sequences take the value of physeq@refseq

nproc

(default: 1) Set to number of cpus/processors to use for the clustering

method

(default: clusterize) Set the clustering method.

  • clusterize use the DECIPHER::Clusterize() fonction,

  • vsearch use the vsearch software (https://github.com/torognes/vsearch) with arguments --cluster_size by default (see args vsearch_cluster_method) and -strand both (see args vsearch_args)

  • swarm use the swarm

id

(default: 0.97) level of identity to cluster

vsearchpath

(default: vsearch) path to vsearch

tax_adjust

(Default 0) See the man page of merge_taxa_vec() for more details. To conserved the taxonomic rank of the most abundant taxa (ASV, OTU,...), set tax_adjust to 0 (default). For the moment only tax_adjust = 0 is robust

vsearch_cluster_method

(default: "–cluster_size) See other possible methods in the vsearch manual (e.g. --cluster_size or --cluster_smallmem)

  • --cluster_fast : Clusterize the fasta sequences in filename, automatically sort by decreasing sequence length beforehand.

  • --cluster_size : Clusterize the fasta sequences in filename, automatically sort by decreasing sequence abundance beforehand.

  • --cluster_smallmem : Clusterize the fasta sequences in filename without automatically modifying their order beforehand. Sequence are expected to be sorted by decreasing sequence length, unless –usersort is used. In that case you may set vsearch_args to vsearch_args = "–strand both –usersort"

vsearch_args

(default : "–strand both") a one length character element defining other parameters to passed on to vsearch.

keep_temporary_files

(logical, default: FALSE) Do we keep temporary files

  • temp.fasta (refseq in fasta or dna_seq sequences)

  • cluster.fasta (centroid if method = "vsearch")

  • temp.uc (clusters if method = "vsearch")

swarmpath

(default: swarm) path to swarm

d

(default: 1) maximum number of differences allowed between two amplicons, meaning that two amplicons will be grouped if they have d (or less) differences

swarm_args

(default : "–fastidious") a one length character element defining other parameters to passed on to swarm See other possible methods in the SWARM pdf manual

method_clusterize

(default "overlap") the method for the DECIPHER::Clusterize() method

...

Other arguments passed on to DECIPHER::Clusterize()

Value

A new object of class physeq or a list of cluster if dna_seq args was used.

Details

This function use the merge_taxa_vec function to merge taxa into clusters. By default tax_adjust = 0. See the man page of merge_taxa_vec().

References

VSEARCH can be downloaded from https://github.com/torognes/vsearch. More information in the associated publication https://pubmed.ncbi.nlm.nih.gov/27781170.

See also

vsearch_clustering() and swarm_clustering()

Author

Adrien Taudière

Examples

if (requireNamespace("DECIPHER")) {
  postcluster_pq(data_fungi_mini)
}
#> Partitioning sequences by 3-mer similarity:
#> ================================================================================
#> 
#> Time difference of 0.02 secs
#> 
#> Sorting by relatedness within 11 groups:
#> 
iteration 1 of up to 17 (100.0% stability) 
#> 
#> Time difference of 0.01 secs
#> 
#> Clustering sequences by 9-mer similarity:
#> ================================================================================
#> 
#> Time difference of 0.07 secs
#> 
#> Clusters via relatedness sorting: 100% (0% exclusively)
#> Clusters via rare 3-mers: 100% (0% exclusively)
#> Estimated clustering effectiveness: 100%
#> 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 32 taxa and 137 samples ]
#> sample_data() Sample Data:       [ 137 samples by 7 sample variables ]
#> tax_table()   Taxonomy Table:    [ 32 taxa by 12 taxonomic ranks ]
#> refseq()      DNAStringSet:      [ 32 reference sequences ]
# \donttest{
if (requireNamespace("DECIPHER")) {
  postcluster_pq(data_fungi_mini, method_clusterize = "longest")

  if (MiscMetabar::is_swarm_installed()) {
    d_swarm <- postcluster_pq(data_fungi_mini, method = "swarm")
  }
  if (MiscMetabar::is_vsearch_installed()) {
    d_vs <- postcluster_pq(data_fungi_mini, method = "vsearch")
  }
}
#> Partitioning sequences by 3-mer similarity:
#> ================================================================================
#> 
#> Time difference of 0.02 secs
#> 
#> Sorting by relatedness within 11 groups:
#> 
iteration 1 of up to 17 (100.0% stability) 
#> 
#> Time difference of 0.01 secs
#> 
#> Clustering sequences by 9-mer similarity:
#> ================================================================================
#> 
#> Time difference of 0.07 secs
#> 
#> Clusters via relatedness sorting: 100% (0% exclusively)
#> Clusters via rare 3-mers: 100% (0% exclusively)
#> Estimated clustering effectiveness: 100%
#> 
# }