loadSQMlite: Load tables generated by 'sqm2tables.py',...

In SQMtools: Analyze Results Generated by the 'SqueezeMeta' Pipeline

Load tables generated by sqm2tables.py, sqmreads2tables.py or combine-sqm-tables.py into R.

Description

This function takes the path to the output directory generated by sqm2tables.py, sqmreads2tables.py or combine-sqm-tables.py a SQMlite object. The SQMlite object will contain taxonomic and functional profiles, but no detailed information on ORFs, contigs or bins. However, it will also have a much smaller memory footprint. A SQMlite object can be used for plotting and exporting, but it can not be subsetted.

Usage

loadSQMlite(tables_path, tax_mode = "allfilter")

Arguments

tables_path	character, tables directory generated by sqm2table.py, sqmreads2tables.py or combine-sqm-tables.py.
tax_mode	character, which taxonomic classification should be loaded? SqueezeMeta applies the identity thresholds described in Luo et al., 2014. Use allfilter for applying the minimum identity threshold to all taxa (default), prokfilter for applying the threshold to Bacteria and Archaea, but not to Eukaryotes, and nofilter for applying no thresholds at all.

Value

SQMlite object containing the parsed tables.

The SQMlite object structure

The SQMlite object is a nested list which contains the following information:

lvl1	lvl2	lvl3	type	rows/names	columns	data
$taxa	$superkingdom	$abund	numeric matrix	superkingdoms	samples	abundances
		$percent	numeric matrix	superkingdoms	samples	percentages
	$phylum	$abund	numeric matrix	phyla	samples	abundances
		$percent	numeric matrix	phyla	samples	percentages
	$class	$abund	numeric matrix	classes	samples	abundances
		$percent	numeric matrix	classes	samples	percentages
	$order	$abund	numeric matrix	orders	samples	abundances
		$percent	numeric matrix	orders	samples	percentages
	$family	$abund	numeric matrix	families	samples	abundances
		$percent	numeric matrix	families	samples	percentages
	$genus	$abund	numeric matrix	genera	samples	abundances
		$percent	numeric matrix	genera	samples	percentages
	$species	$abund	numeric matrix	species	samples	abundances
		$percent	numeric matrix	species	samples	percentages
$functions	$KEGG	$abund	numeric matrix	KEGG ids	samples	abundances (reads)
		$bases	numeric matrix	KEGG ids	samples	abundances (bases)
		$tpm	numeric matrix	KEGG ids	samples	tpm
		$copy_number	numeric matrix	KEGG ids	samples	avg. copies
	$COG	$abund	numeric matrix	COG ids	samples	abundances (reads)
		$bases	numeric matrix	COG ids	samples	abundances (bases)
		$tpm	numeric matrix	COG ids	samples	tpm
		$copy_number	numeric matrix	COG ids	samples	avg. copies
	$PFAM	$abund	numeric matrix	PFAM ids	samples	abundances (reads)
		$bases	numeric matrix	PFAM ids	samples	abundances (bases)
		$tpm	numeric matrix	PFAM ids	samples	tpm
		$copy_number	numeric matrix	PFAM ids	samples	avg. copies
$total_reads			numeric vector	samples	(n/a)	total reads
$misc	$project_name		character vector	(empty)	(n/a)	project name
	$samples		character vector	(empty)	(n/a)	samples
	$tax_names_long	$superkingdom	character vector	short names	(n/a)	full names
		$phylum	character vector	short names	(n/a)	full names
		$class	character vector	short names	(n/a)	full names
		$order	character vector	short names	(n/a)	full names
		$family	character vector	short names	(n/a)	full names
		$genus	character vector	short names	(n/a)	full names
		$species	character vector	short names	(n/a)	full names
	$tax_names_short		character vector	full names	(n/a)	short names
	$KEGG_names		character vector	KEGG ids	(n/a)	KEGG names
	$KEGG_paths		character vector	KEGG ids	(n/a)	KEGG hiararchy
	$COG_names		character vector	COG ids	(n/a)	COG names
	$COG_paths		character vector	COG ids	(n/a)	COG hierarchy
	$ext_annot_sources		character vector	(empty)	(n/a)	external databases

If external databases for functional classification were provided to SqueezeMeta or SqueezeMeta_reads via the -extdb argument, the corresponding abundance, tpm and copy number profiles will be present in SQM$functions (e.g. results for the CAZy database would be present in SQM$functions$CAZy). Additionally, the extended names of the features present in the external database will be present in SQM$misc (e.g. SQM$misc$CAZy_names). Note that results generated by SqueezeMeta_reads will contain only read abundances, but not bases, tpm or copy number estimations.

See Also

plotBars and plotFunctions will plot the most abundant taxa and functions in a SQMlite object. exportKrona will generate Krona charts reporting the taxonomy in a SQMlite object.

Examples

## Not run: 
## (outside R)
## Run SqueezeMeta on the test data.
/path/to/SqueezeMeta/scripts/SqueezeMeta.pl -p Hadza -f raw -m coassembly -s test.samples
## Generate the tabular outputs!
/path/to/SqueezeMeta/utils/sqm2tables.py Hadza Hadza/results/tables
## Now go into R.
library(SQMtools)
Hadza = loadSQMlite("Hadza/results/tables")
# Where Hadza is the path to the SqueezeMeta output directory.
# Note that this is not the whole SQM project, just the directory containing the tables.
# It would also work with tables generated by sqmreads2tables.py, or combine-sqm-tables.py
plotTaxonomy(Hadza)
plotFunctions(Hadza)
exportKrona(Hadza, 'myKronaTest.html')

## End(Not run)

SQMtools documentation built on June 18, 2025, 1:08 a.m.