{OTU_table} R# Documentation

OTU_table

require(GCModeller);

#' Tools for handling OTU table data
imports "OTU_table" from "metagenomics_kit";

Tools for handling OTU table data

Operational taxonomic unit (OTU)

OTU's are used to categorize bacteria based on sequence similarity.

In 16S metagenomics approaches, OTUs are cluster of similar sequence variants of the 16S rDNA marker gene sequence. Each of these cluster is intended to represent a taxonomic unit of a bacteria species or genus depending on the sequence similarity threshold. Typically, OTU cluster are defined by a 97% identity threshold of the 16S gene sequences to distinguish bacteria at the genus level.

Species separation requires a higher threshold Of 98% Or 99% sequence identity, Or even better the use Of exact amplicon sequence variants (ASV) instead Of OTU sequence clusters.

.NET clr type export
OTU_table: OTUTable

OTU table (sequence count table)

A OTU table contains the number of sequences that are observed for each taxonomic unit (OTUs) in each samples. Columns usually represent samples and rows represent genera or species specific taxonomic units (OTUs). OTU tables are often saved as BIOM formatted files.

Limited taxonomic resolution

OTU resolution depends On the 16S approach which has some limits In distinguishing at the species level, For example,

Escherichia coli And Shigella spp. share almost identical 16S rRNA gene sequences.

Alternative approaches are developed To achieve higher resolution up To strain level by considering larger Or complete sets Of genes.


.NET clr function exports
as.data.frame.otutable OTUTable:

OTU table (sequence count table)

A OTU table contains the number of sequences that are observed for each taxonomic unit (OTUs) in each samples. Columns usually represent samples and rows represent genera or species specific taxonomic units (OTUs). OTU tables are often saved as BIOM formatted files.

Limited taxonomic resolution

OTU resolution depends On the 16S approach which has some limits In distinguishing at the species level, For example,

Escherichia coli And Shigella spp. share almost identical 16S rRNA gene sequences.

Alternative approaches are developed To achieve higher resolution up To strain level by considering larger Or complete sets Of genes.
sample_id

get sample id from the otu table
relative_abundance

Transform abundance data in an otu_table to relative abundance, sample-by-sample.

Transform abundance data into relative abundance, i.e. proportional data. This is an alternative method of normalization and may not be appropriate for all datasets, particularly if your sequencing depth varies between samples.
average
median_scale
filter

filter the otu data which has relative abundance greater than the given threshold
read.OTUtable

read 16s OTU table
read.OTUdata
otu_from_matrix

cast the expression matrix to the otu data
as.hts_matrix

Create expression matrix data from a given otu table
read.rankdata
as.OTU_table

convert the mothur rank tree as the OTU table
set_MAG_data
set_taxonomyName
core_microbiome

evaluate the core microbiome species across all sample data
dominant_species

evaluate top dominant species for each sample data
make_otu_table
makeTreeGraph

make OTU tree graph via JSD correlation method
makeUPGMATree
drawUPGMATree
make_repseq_id
merge_phyloseq
batch_combine

combine of two batch data directly via the taxonomy string group
[Document Index]