Package 'SQMtools' reference manual

Title:	Analyze Results Generated by the 'SqueezeMeta' Pipeline
Description:	'SqueezeMeta' is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading 'SqueezeMeta' results into R, filtering them based on different criteria, and visualizing the results using basic plots. The 'SqueezeMeta' project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as 'vegan' or 'DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.
Authors:	Fernando Puente-Sánchez [aut, cre], Natalia García-García [aut]
Maintainer:	Fernando Puente-Sánchez <[email protected]>
License:	GPL-3
Version:	1.6.3
Built:	2024-12-12 07:14:55 UTC
Source:	CRAN

Combine several SQM objects

Description

Combine an arbitrary number of SQM objects into a single SQM object. The input objects must be subsets of the same original SQM object (i.e. from the same SqueezeMeta run). For combining results from different runs please check combineSQMlite.

Usage

combineSQM(
  ...,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)
combineSQM(
  ...,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)

Arguments

`...`	an arbitrary number of SQM objects. Alternatively, a single list containing an arbitrary number of SQM objects.
`tax_source`	character. Features used for calculating aggregated abundances at the different taxonomic ranks. Either `"orfs"` or `"contigs"` (default `"orfs"`). If the objects being combined contain a subset of taxa or bins, this parameter can be set to `TRUE`.
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object (default `TRUE`).
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object with the highest RecA/RadA coverages. By default it is set to `TRUE`, which means that the returned copy numbers will represent the average copy number per function in the genomes of the selected bins or contigs. If any SQM objects that are being combined contain a functional subset rather than a contig/bins subset, this parameter should be set to `FALSE`.

Value

A SQM object

Examples

data(Hadza)
# Select Carbohydrate metabolism ORFs in Bacteroidetes,
#  and Amino acid metabolism ORFs in Proteobacteria
bact = subsetTax(Hadza, "phylum", "Bacteroidetes")
bact.carb = subsetFun(bact, "Carbohydrate metabolism")
proteo = subsetTax(Hadza, "phylum", "Proteobacteria")
proteo.amins = subsetFun(proteo, "Amino acid metabolism")
bact.carb_proteo.amins = combineSQM(bact.carb, proteo.amins, rescale_copy_number=FALSE)
data(Hadza)
# Select Carbohydrate metabolism ORFs in Bacteroidetes,
#  and Amino acid metabolism ORFs in Proteobacteria
bact = subsetTax(Hadza, "phylum", "Bacteroidetes")
bact.carb = subsetFun(bact, "Carbohydrate metabolism")
proteo = subsetTax(Hadza, "phylum", "Proteobacteria")
proteo.amins = subsetFun(proteo, "Amino acid metabolism")
bact.carb_proteo.amins = combineSQM(bact.carb, proteo.amins, rescale_copy_number=FALSE)

Combine several SQM or SQMlite objects

Description

Combine an arbitrary number of SQM or SQMlite objects into a single SQMlite object. This function accepts objects originating from different projects (i.e. different SqueezeMeta runs).

Usage

combineSQMlite(...)
combineSQMlite(...)

Arguments

...

an arbitrary number of SQM or SQMlite objects. Alternatively, a single list containing an arbitrary number of SQMlite objects.

Value

A SQMlite object

Examples

## Not run: 
data(Hadza)
# Load data coming from a different run
other = loadSQMlite("/path/to/other/project/tables") # e.g. if the project was run using sqm_reads
# (We could also use loadSQM to load the data as long as the data comes from a SqueezeMeta run)
combined = combineSQMlite(Hadza, other)
# Now we can plot together the samples from Hadza and the second project
plotTaxonomy(combined, 'family')

## End(Not run)
## Not run: 
data(Hadza)
# Load data coming from a different run
other = loadSQMlite("/path/to/other/project/tables") # e.g. if the project was run using sqm_reads
# (We could also use loadSQM to load the data as long as the data comes from a SqueezeMeta run)
combined = combineSQMlite(Hadza, other)
# Now we can plot together the samples from Hadza and the second project
plotTaxonomy(combined, 'family')

## End(Not run)

Export the taxonomy of a SQM object into a Krona Chart

Description

Generate a krona chart containing the full taxonomy from a SQM object.

Usage

exportKrona(SQM, output_name = NA)
exportKrona(SQM, output_name = NA)

Arguments

`SQM`	A SQM or SQMlite object.
`output_name`	character. Name of the output file containing the Krona charts in html format (default `"<project_name>.krona.html")`.

Details

Original code was kindly provided by Giuseppe D'Auria ([email protected]).

Value

No return value, but a krona chart is produced in the current working directory.

Examples


data(Hadza)
# Check that kronatools is present.
ecode = system('ktImportText', ignore.stdout = TRUE, ignore.stderr = TRUE)
# If so, run.
if(ecode==0) { exportKrona(Hadza) }

data(Hadza)
# Check that kronatools is present.
ecode = system('ktImportText', ignore.stdout = TRUE, ignore.stderr = TRUE)
# If so, run.
if(ecode==0) { exportKrona(Hadza) }

Export the functions of a SQM object into KEGG pathway maps

Description

This function is a wrapper for the pathview package (Luo et al., 2017. Nucleic acids research, 45:W501-W508). It will generate annotated KEGG pathway maps showing which reactions are present in the different samples. It will also generate legends with the color scales for each sample in separate png files.

Usage

exportPathway(
  SQM,
  pathway_id,
  count = "tpm",
  samples = NULL,
  split_samples = FALSE,
  sample_colors = NULL,
  log_scale = FALSE,
  fold_change_groups = NULL,
  fold_change_colors = NULL,
  max_scale_value = NULL,
  color_bins = 10,
  output_suffix = "pathview"
)
exportPathway(
  SQM,
  pathway_id,
  count = "tpm",
  samples = NULL,
  split_samples = FALSE,
  sample_colors = NULL,
  log_scale = FALSE,
  fold_change_groups = NULL,
  fold_change_colors = NULL,
  max_scale_value = NULL,
  color_bins = 10,
  output_suffix = "pathview"
)

Arguments

`SQM`	A SQM or SQMlite object.
`pathway_id`	character. The five-number KEGG pathway identifier. A list of all pathway identifiers can be found in https://www.genome.jp/kegg/pathway.html.
`count`	character. Either `"abund"` for raw abundances, `"percent"` for percentages, `"bases"` for raw base counts, `"tpm"` for TPM normalized values or `"copy_number"` for copy numbers (default `"tpm"`). Note that a given count type might not available in this object (e.g. TPM or copy number in SQMlite objects originating from a SQM reads project).
`samples`	character. An optional vector with the names of the samples to export. If absent, all samples will be exported (default `NULL`).
`split_samples`	logical. Generate a different output file for each sample (default `FALSE`).
`sample_colors`	character. An optional vector with the plotting colors for each sample (default `NULL`).
`log_scale`	logical. Use a base 10 logarithmic transformation for the color scale. Will have no effect if `fold_change_groups` is provided (default `FALSE`).
`fold_change_groups`	list. An optional list containing two vectors of samples. If provided, the function will generate a single plot displaying the log2 fold-change between the average abundances of both groups of samples ( log(second group / first group) ) (default `NULL`).
`fold_change_colors`	character. An optional vector with the plotting colors of both groups in the fold-change plot. Will be ignored if `fold_change_group` is not provided.
`max_scale_value`	numeric. Maximum value to include in the color scale. By default it is the maximum value in the selected samples (if plotting abundances in samples) or the maximum absolute log2 fold-change (if plotting fold changes) (default `NULL`).
`color_bins`	numeric. Number of bins used to generate the gradient in the color scale (default `10`).
`output_suffix`	character. Suffix to be added to the output files (default `"pathview"`).

Value

No return value, but Pathview figures are produced in the current working directory.

Examples


data(Hadza)
exportPathway(Hadza, "00910", count = 'copy_number', 
              output_suffix = "nitrogen_metabolism", sample_colors = c("red", "blue"))
exportPathway(Hadza, "00250", count = 'tpm', 
              output_suffix = "ala_asp_glu_metabolism_FoldChange", 
              fold_change_groups = list(c("H1"), c("H12")), max_scale_value=2)

data(Hadza)
exportPathway(Hadza, "00910", count = 'copy_number', 
              output_suffix = "nitrogen_metabolism", sample_colors = c("red", "blue"))
exportPathway(Hadza, "00250", count = 'tpm', 
              output_suffix = "ala_asp_glu_metabolism_FoldChange", 
              fold_change_groups = list(c("H1"), c("H12")), max_scale_value=2)

Export results in tabular format

Description

This function is a wrapper for R's write.table function.

Usage

exportTable(table, output_name)
exportTable(table, output_name)

Arguments

`table`	vector, matrix or data.frame. The table to be written.
`output_name`	character. Name of the output file.

Value

No return value, but a table is produced in the current working directory.

Examples


data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
# Write the taxonomic distribution at the genus level of all the genes related to iron.
exportTable(Hadza.iron$taxa$genus$percent, "Hadza.ironGenes.genus.tsv")
# Now write the distribution of the different iron-related COGs
#  (Clusters of Orthologous Groups) across samples.
exportTable(Hadza.iron$functions$COG$tpm, "Hadza.ironGenes.COG.tsv")
# Now write all the information contained in the ORF table.
exportTable(Hadza.iron$orfs$table, "Hadza.ironGenes.orftable.tsv")

data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
# Write the taxonomic distribution at the genus level of all the genes related to iron.
exportTable(Hadza.iron$taxa$genus$percent, "Hadza.ironGenes.genus.tsv")
# Now write the distribution of the different iron-related COGs
#  (Clusters of Orthologous Groups) across samples.
exportTable(Hadza.iron$functions$COG$tpm, "Hadza.ironGenes.COG.tsv")
# Now write all the information contained in the ORF table.
exportTable(Hadza.iron$orfs$table, "Hadza.ironGenes.orftable.tsv")

Hadza hunter-gatherer gut metagenomes

Description

Subset of two bins (and the associated contigs and genes) generated by running SqueezeMeta on two gut metagenomic samples obtained from two hunter-gatherers of the Hadza ethnic group.

Usage

data(Hadza)
data(Hadza)

Format

A SQM object; see loadSQM.

Source

SRR1927149, SRR1929485.

References

Rampelli et al., 2015. Metagenome Sequencing of the Hadza Hunter-Gatherer Gut Microbiota. Curr. biol. 25:1682-93 (PubMed).

Examples

data(Hadza)
plotTaxonomy(Hadza, "genus", rescale=TRUE)
plotFunctions(Hadza, "COG")

data(Hadza)
plotTaxonomy(Hadza, "genus", rescale=TRUE)
plotFunctions(Hadza, "COG")

Load a SqueezeMeta project into R

Description

This function takes the path to a project directory generated by SqueezeMeta (whose name is specified in the -p parameter of the SqueezeMeta.pl script) and parses the results into a SQM object. Alternatively, it can load the project data from a zip file produced by sqm2zip.py.

Usage

loadSQM(
  project_path,
  tax_mode = "prokfilter",
  trusted_functions_only = FALSE,
  engine = "data.table"
)
loadSQM(
  project_path,
  tax_mode = "prokfilter",
  trusted_functions_only = FALSE,
  engine = "data.table"
)

Arguments

`project_path`	character, project directory generated by SqueezeMeta, or zip file generated by `sqm2zip.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, `prokfilter` for applying the threshold to Bacteria and Archaea, but not to Eukaryotes, and `nofilter` for applying no thresholds at all (default `prokfilter`).
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`). Will only have an effect if the `project_dir/results/tables` is not already present.
`engine`	character. Engine used to load the ORFs and contigs tables. Either `data.frame` or `data.table` (significantly faster if your project is large). Default `data.table`.

Value

SQM object containing the parsed project.

Prerequisites

Run SqueezeMeta! An example call for running it would be:

/path/to/SqueezeMeta/scripts/SqueezeMeta.pl
-m coassembly -f fastq_dir -s samples_file -p project_dir

The SQM object structure

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

lvl1	lvl2	lvl3	type	rows/names	columns	data
$orfs	$table		dataframe	orfs	misc. data	misc. data
	$abund		numeric matrix	orfs	samples	abundances (reads)
	$bases		numeric matrix	orfs	samples	abundances (bases)
	$cov		numeric matrix	orfs	samples	coverages
	$cpm		numeric matrix	orfs	samples	covs. / 10^6 reads
	$tpm		numeric matrix	orfs	samples	tpm
	$seqs		character vector	orfs	(n/a)	sequences
	$tax		character matrix	orfs	tax. ranks	taxonomy
$contigs	$table		dataframe	contigs	misc. data	misc. data
	$abund		numeric matrix	contigs	samples	abundances (reads)
	$bases		numeric matrix	contigs	samples	abundances (bases)
	$cov		numeric matrix	contigs	samples	coverages
	$cpm		numeric matrix	contigs	samples	covs. / 10^6 reads
	$tpm		numeric matrix	contigs	samples	tpm
	$seqs		character vector	contigs	(n/a)	sequences
	$tax		character matrix	contigs	tax. ranks	taxonomies
	$bins		character matrix	contigs	bin. methods	bins
$bins	$table		dataframe	bins	misc. data	misc. data
	$length		numeric vector	bins	(n/a)	length
	$abund		numeric matrix	bins	samples	abundances (reads)
	$percent		numeric matrix	bins	samples	abundances (reads)
	$bases		numeric matrix	bins	samples	abundances (bases)
	$cov		numeric matrix	bins	samples	coverages
	$cpm		numeric matrix	bins	samples	covs. / 10^6 reads
	$tax		character matrix	bins	tax. ranks	taxonomy
$taxa	$superkingdom	$abund	numeric matrix	superkingdoms	samples	abundances (reads)
		$percent	numeric matrix	superkingdoms	samples	percentages
	$phylum	$abund	numeric matrix	phyla	samples	abundances (reads)
		$percent	numeric matrix	phyla	samples	percentages
	$class	$abund	numeric matrix	classes	samples	abundances (reads)
		$percent	numeric matrix	classes	samples	percentages
	$order	$abund	numeric matrix	orders	samples	abundances (reads)
		$percent	numeric matrix	orders	samples	percentages
	$family	$abund	numeric matrix	families	samples	abundances (reads)
		$percent	numeric matrix	families	samples	percentages
	$genus	$abund	numeric matrix	genera	samples	abundances (reads)
		$percent	numeric matrix	genera	samples	percentages
	$species	$abund	numeric matrix	species	samples	abundances (reads)
		$percent	numeric matrix	species	samples	percentages
$functions	$KEGG	$abund	numeric matrix	KEGG ids	samples	abundances (reads)
		$bases	numeric matrix	KEGG ids	samples	abundances (bases)
		$cov	numeric matrix	KEGG ids	samples	coverages
		$cpm	numeric matrix	KEGG ids	samples	covs. / 10^6 reads
		$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)
		$cov	numeric matrix	COG ids	samples	coverages
		$cpm	numeric matrix	COG ids	samples	covs. / 10^6 reads
		$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)
		$cov	numeric matrix	PFAM ids	samples	coverages
		$cpm	numeric matrix	PFAM ids	samples	covs. / 10^6 reads
		$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	COG ids	(n/a)	external databases

If external databases for functional classification were provided to SqueezeMeta via the -extdb argument, the corresponding abundance (reads and bases), coverages, 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).

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
## Now go into R.
library(SQMtools)
Hadza = loadSQM("Hadza") # Where Hadza is the path to the SqueezeMeta output directory.

## End(Not run)

data(Hadza) # We will illustrate the structure of the SQM object on the test data
# Which are the ten most abundant KEGG IDs in our data?
topKEGG = names(sort(rowSums(Hadza$functions$KEGG$tpm), decreasing=TRUE))[1:11]
topKEGG = topKEGG[topKEGG!="Unclassified"]
# Which functions do those KEGG IDs represent?
Hadza$misc$KEGG_names[topKEGG]
# What is the relative abundance of the Gammaproteobacteria class across samples?
Hadza$taxa$class$percent["Gammaproteobacteria",]
# Which information is stored in the orf, contig and bin tables?
colnames(Hadza$orfs$table)
colnames(Hadza$contigs$table)
colnames(Hadza$bins$table)
# What is the GC content distribution of my metagenome?
boxplot(Hadza$contigs$table[,"GC perc"]) # Not weighted by contig length or abundance!
## 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
## Now go into R.
library(SQMtools)
Hadza = loadSQM("Hadza") # Where Hadza is the path to the SqueezeMeta output directory.

## End(Not run)

data(Hadza) # We will illustrate the structure of the SQM object on the test data
# Which are the ten most abundant KEGG IDs in our data?
topKEGG = names(sort(rowSums(Hadza$functions$KEGG$tpm), decreasing=TRUE))[1:11]
topKEGG = topKEGG[topKEGG!="Unclassified"]
# Which functions do those KEGG IDs represent?
Hadza$misc$KEGG_names[topKEGG]
# What is the relative abundance of the Gammaproteobacteria class across samples?
Hadza$taxa$class$percent["Gammaproteobacteria",]
# Which information is stored in the orf, contig and bin tables?
colnames(Hadza$orfs$table)
colnames(Hadza$contigs$table)
colnames(Hadza$bins$table)
# What is the GC content distribution of my metagenome?
boxplot(Hadza$contigs$table[,"GC perc"]) # Not weighted by contig length or abundance!

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")
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.

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)
## 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)

Single Copy Phylogenetic Marker Genes from Sunagawa's group (KOs)

Description

Lists of Single Copy Phylogenetic Marker Genes. These are useful for transforming coverages or tpms into copy numbers. This is an alternative way of normalizing data in order to be able to compare functional profiles in samples with different sequencing depths.

Usage

data(MGKOs)
data(MGKOs)

Format

Character vector with the KEGG identifiers for 10 Single Copy Phylogenetic Marker Genes.

References

Salazar, G et al. (2019). Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome Cell 179:1068-1083. (PubMed).

Single Copy Phylogenetic Marker Genes from Sunagawa's group (OGs)

Description

Usage

data(MGOGs)
data(MGOGs)

Format

Character vector with the COG identifiers for 10 Single Copy Phylogenetic Marker Genes.

References

Salazar, G et al. (2019). Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome Cell 179:1068-1083. (PubMed).

Get the N most abundant rows (or columns) from a numeric table

Description

Return a subset of an input matrix or data frame, containing only the N most abundant rows (or columns), sorted. Alternatively, a custom set of rows can be returned.

Usage

mostAbundant(
  data,
  N = 10,
  items = NULL,
  others = FALSE,
  rescale = FALSE,
  bycol = FALSE
)
mostAbundant(
  data,
  N = 10,
  items = NULL,
  others = FALSE,
  rescale = FALSE,
  bycol = FALSE
)

Arguments

`data`	numeric matrix or data frame
`N`	integer Number of rows to return (default `10`).
`items`	Character vector. Custom row names to return. If provided, it will override `N` (default `NULL`).
`others`	logical. If `TRUE`, an extra row will be returned containing the aggregated abundances of the elements not selected with `N` or `items` (default `FALSE`).
`rescale`	logical. Scale result to percentages column-wise (default `FALSE`).
`bycol`	logical. Operate on columns instead of rows (default `FALSE`).

Value

A matrix or data frame (same as input) with the selected rows (or columns).

Examples

data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most abundant KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostAbundant(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names.
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function.
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots.
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")
data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most abundant KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostAbundant(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names.
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function.
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots.
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")

Get the N most variable rows (or columns) from a numeric table

Description

Return a subset of an input matrix or data frame, containing only the N most variable rows (or columns), sorted. Variability is calculated as the Coefficient of Variation (sd/mean).

Usage

mostVariable(data, N = 10, bycol = FALSE)
mostVariable(data, N = 10, bycol = FALSE)

Arguments

`data`	numeric matrix or data frame
`N`	integer Number of rows to return (default `10`).
`bycol`	logical. Operate on columns instead of rows (default `FALSE`).

Value

A matrix or data frame (same as input) with the selected rows or columns.

Examples

data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most variable KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostVariable(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")
data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most variable KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostVariable(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")

Plot a barplot using ggplot2

Description

Plot a ggplot2 barplot from a matrix or data frame. The data should be in tabular format (e.g. features in rows and samples in columns).

Usage

plotBars(
  data,
  label_x = "Samples",
  label_y = "Abundances",
  label_fill = "Features",
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)
plotBars(
  data,
  label_x = "Samples",
  label_y = "Abundances",
  label_fill = "Features",
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

`data`	Numeric matrix or data frame.
`label_x`	character Label for the x axis (default `"Samples"`).
`label_y`	character Label for the y axis (default `"Abundances"`).
`label_fill`	character Label for color categories (default `"Features"`).
`color`	Vector with custom colors for the different features. If empty, the default ggplot2 palette will be used (default `NULL`).
`base_size`	numeric. Base font size (default `11`).
`max_scale_value`	numeric. Maximum value to include in the y axis. By default it is handled automatically by ggplot2 (default `NULL`).
`metadata_groups`	list. Split the plot into groups defined by the user: list('G1' = c('sample1', sample2'), 'G2' = c('sample3', 'sample4')) default `NULL`).

Value

a ggplot2 plot object.

Examples

data(Hadza)
sk = Hadza$taxa$superkingdom$abund
plotBars(sk, label_y = "Raw reads", label_fill = "Superkingdom")
data(Hadza)
sk = Hadza$taxa$superkingdom$abund
plotBars(sk, label_y = "Raw reads", label_fill = "Superkingdom")

Barplot of the most abundant bins in a SQM object

Description

This function selects the most abundant bins across all samples in a SQM object and represents their abundances in a barplot. Alternatively, a custom set of bins can be represented.

Usage

plotBins(
  SQM,
  count = "percent",
  N = 15,
  bins = NULL,
  others = TRUE,
  samples = NULL,
  ignore_unmapped = FALSE,
  ignore_nobin = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)
plotBins(
  SQM,
  count = "percent",
  N = 15,
  bins = NULL,
  others = TRUE,
  samples = NULL,
  ignore_unmapped = FALSE,
  ignore_nobin = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

`SQM`	A SQM or a SQMlite object.
`count`	character. Either `"abund"` for raw abundances, `"percent"` for percentages, `"cov"` for coverages, or `"cpm"` for coverages per million reads (default `"percent"`).
`N`	integer Plot the `N` most abundant bins (default `15`).
`bins`	character. Custom bins to plot. If provided, it will override `N` (default `NULL`).
`others`	logical. Collapse the abundances of least abundant bins, and include the result in the plot (default `TRUE`).
`samples`	character. Character vector with the names of the samples to include in the plot. Can also be used to plot the samples in a custom order. If not provided, all samples will be plotted (default `NULL`).
`ignore_unmapped`	logical. Don't include unmapped reads in the plot (default `FALSE`).
`ignore_nobin`	logical. Don't include reads which are not in a bin in the plot (default `FALSE`).
`rescale`	logical. Re-scale results to percentages (default `FALSE`).
`color`	Vector with custom colors for the different features. If empty, we will use our own hand-picked pallete if N<=15, and the default ggplot2 palette otherwise (default `NULL`).
`base_size`	numeric. Base font size (default `11`).
`max_scale_value`	numeric. Maximum value to include in the y axis. By default it is handled automatically by ggplot2 (default `NULL`).
`metadata_groups`	list. Split the plot into groups defined by the user: list('G1' = c('sample1', sample2'), 'G2' = c('sample3', 'sample4')) default `NULL`).

Value

a ggplot2 plot object.

Examples

data(Hadza)
# Bins distribution.
plotBins(Hadza)
data(Hadza)
# Bins distribution.
plotBins(Hadza)

Heatmap of the most abundant functions in a SQM object

Description

This function selects the most abundant functions across all samples in a SQM object and represents their abundances in a heatmap. Alternatively, a custom set of functions can be represented.

Usage

plotFunctions(
  SQM,
  fun_level = "KEGG",
  count = "tpm",
  N = 25,
  fun = NULL,
  samples = NULL,
  ignore_unmapped = TRUE,
  ignore_unclassified = TRUE,
  gradient_col = c("ghostwhite", "dodgerblue4"),
  base_size = 11,
  metadata_groups = NULL
)
plotFunctions(
  SQM,
  fun_level = "KEGG",
  count = "tpm",
  N = 25,
  fun = NULL,
  samples = NULL,
  ignore_unmapped = TRUE,
  ignore_unclassified = TRUE,
  gradient_col = c("ghostwhite", "dodgerblue4"),
  base_size = 11,
  metadata_groups = NULL
)

Arguments

`SQM`	A SQM or SQMlite object.
`fun_level`	character. Either `"KEGG"`, `"COG"`, `"PFAM"` or any other custom database used for annotation (default `"KEGG"`).
`count`	character. Either `"abund"` for raw abundances, `"percent"` for percentages, `"bases"` for raw base counts, `"cpm"` for coverages per million reads, `"tpm"` for TPM normalized values or `"copy_number"` for copy numbers (default `"tpm"`). Note that a given count type might not available in this object (e.g. TPM or copy number in SQMlite objects originating from a SQM reads project).
`N`	integer Plot the `N` most abundant functions (default `25`).
`fun`	character. Custom functions to plot. If provided, it will override `N` (default `NULL`).
`samples`	character. Character vector with the names of the samples to include in the plot. Can also be used to plot the samples in a custom order. If not provided, all samples will be plotted (default `NULL`).
`ignore_unmapped`	logical. Don't include unmapped reads in the plot (default `TRUE`).
`ignore_unclassified`	logical. Don't include unclassified ORFs in the plot (default `TRUE`).
`gradient_col`	A vector of two colors representing the low and high ends of the color gradient (default `c("ghostwhite", "dodgerblue4")`).
`base_size`	numeric. Base font size (default `11`).
`metadata_groups`	list. Split the plot into groups defined by the user: list('G1' = c('sample1', sample2'), 'G2' = c('sample3', 'sample4')) default `NULL`).

Value

a ggplot2 plot object.

Examples

data(Hadza)
plotFunctions(Hadza)
data(Hadza)
plotFunctions(Hadza)

Plot a heatmap using ggplot2

Description

Plot a ggplot2 heatmap from a matrix or data frame. The data should be in tabular format (e.g. features in rows and samples in columns).

Usage

plotHeatmap(
  data,
  label_x = "Samples",
  label_y = "Features",
  label_fill = "Abundance",
  gradient_col = c("ghostwhite", "dodgerblue4"),
  base_size = 11,
  metadata_groups = NULL
)
plotHeatmap(
  data,
  label_x = "Samples",
  label_y = "Features",
  label_fill = "Abundance",
  gradient_col = c("ghostwhite", "dodgerblue4"),
  base_size = 11,
  metadata_groups = NULL
)

Arguments

`data`	numeric matrix or data frame.
`label_x`	character Label for the x axis (default `"Samples"`).
`label_y`	character Label for the y axis (default `"Features"`).
`label_fill`	character Label for color scale (default `"Abundance"`).
`gradient_col`	A vector of two colors representing the low and high ends of the color gradient (default `c("ghostwhite", "dodgerblue4")`).
`base_size`	numeric. Base font size (default `11`).
`metadata_groups`	list. Split the plot into groups defined by the user: list('G1' = c('sample1', sample2'), 'G2' = c('sample3', 'sample4')) default `NULL`).

Value

A ggplot2 plot object.

Examples

data(Hadza)
topPFAM = mostAbundant(Hadza$functions$PFAM$tpm)
topPFAM = topPFAM[rownames(topPFAM) != "Unclassified",] # Take out the Unclassified ORFs.
plotHeatmap(topPFAM, label_x = "Samples", label_y = "PFAMs", label_fill = "TPM")
data(Hadza)
topPFAM = mostAbundant(Hadza$functions$PFAM$tpm)
topPFAM = topPFAM[rownames(topPFAM) != "Unclassified",] # Take out the Unclassified ORFs.
plotHeatmap(topPFAM, label_x = "Samples", label_y = "PFAMs", label_fill = "TPM")

Barplot of the most abundant taxa in a SQM object

Description

This function selects the most abundant taxa across all samples in a SQM object and represents their abundances in a barplot. Alternatively, a custom set of taxa can be represented.

Usage

plotTaxonomy(
  SQM,
  rank = "phylum",
  count = "percent",
  N = 15,
  tax = NULL,
  others = TRUE,
  samples = NULL,
  nocds = "treat_separately",
  ignore_unmapped = FALSE,
  ignore_unclassified = FALSE,
  no_partial_classifications = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)
plotTaxonomy(
  SQM,
  rank = "phylum",
  count = "percent",
  N = 15,
  tax = NULL,
  others = TRUE,
  samples = NULL,
  nocds = "treat_separately",
  ignore_unmapped = FALSE,
  ignore_unclassified = FALSE,
  no_partial_classifications = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

`SQM`	A SQM or a SQMlite object.
`rank`	Taxonomic rank to plot (default `phylum`).
`count`	character. Either `"percent"` for percentages, or `"abund"` for raw abundances (default `"percent"`).
`N`	integer Plot the `N` most abundant taxa (default `15`).
`tax`	character. Custom taxa to plot. If provided, it will override `N` (default `NULL`).
`others`	logical. Collapse the abundances of least abundant taxa, and include the result in the plot (default `TRUE`).
`samples`	character. Character vector with the names of the samples to include in the plot. Can also be used to plot the samples in a custom order. If not provided, all samples will be plotted (default `NULL`).
`nocds`	character. Either `"treat_separately"` to treat reads annotated as No CDS separately, `"treat_as_unclassified"` to treat them as Unclassified or `"ignore"` to ignore them in the plot (default `"treat_separately"`).
`ignore_unmapped`	logical. Don't include unmapped reads in the plot (default `FALSE`).
`ignore_unclassified`	logical. Don't include unclassified reads in the plot (default `FALSE`).
`no_partial_classifications`	logical. Treat reads not fully classified at the requested level (e.g. "Unclassified bacteroidetes" at the class level or below) as fully unclassified. This takes effect before `ignore_unclassified`, so if both are `TRUE` the plot will only contain fully classified contigs (default `FALSE`).
`rescale`	logical. Re-scale results to percentages (default `FALSE`).
`color`	Vector with custom colors for the different features. If empty, we will use our own hand-picked pallete if N<=15, and the default ggplot2 palette otherwise (default `NULL`).
`base_size`	numeric. Base font size (default `11`).
`max_scale_value`	numeric. Maximum value to include in the y axis. By default it is handled automatically by ggplot2 (default `NULL`).
`metadata_groups`	list. Split the plot into groups defined by the user: list('G1' = c('sample1', sample2'), 'G2' = c('sample3', 'sample4')) default `NULL`).

Value

a ggplot2 plot object.

Examples

data(Hadza)
Hadza.amin = subsetFun(Hadza, "Amino acid metabolism")
# Taxonomic distribution of amino acid metabolism ORFs at the family level.
plotTaxonomy(Hadza.amin, "family")
data(Hadza)
Hadza.amin = subsetFun(Hadza, "Amino acid metabolism")
# Taxonomic distribution of amino acid metabolism ORFs at the family level.
plotTaxonomy(Hadza.amin, "family")

RecA/RadA recombinase

Description

The recombination protein RecA/RadA is essential for the repair and maintenance of DNA, and has homologs in every bacteria and archaea. By dividing the coverage of functions by the coverage of RecA, abundances can be transformed into copy numbers, which can be used to compare functional profiles in samples with different sequencing depths. RecA-derived copy numbers are available in the SQM object (SQM$functions$<annotation_type>$copy_number).

Usage

data(RecA)
data(RecA)

Format

Character vector with the COG identifier for RecA/RadA.

Source

EggNOG Database.

Examples

data(Hadza)
data(RecA)
### Let's calculate the average copy number of each function in our samples.
# We do it for COG annotations here, but we could also do it for KEGG or PFAMs.
COG.coverage = Hadza$functions$COG$cov
COG.copynumber = t(t(COG.coverage) / COG.coverage[RecA,]) # Sample-wise division by RecA coverage.
data(Hadza)
data(RecA)
### Let's calculate the average copy number of each function in our samples.
# We do it for COG annotations here, but we could also do it for KEGG or PFAMs.
COG.coverage = Hadza$functions$COG$cov
COG.copynumber = t(t(COG.coverage) / COG.coverage[RecA,]) # Sample-wise division by RecA coverage.

Return a vector with the row-wise maxima of a matrix or dataframe.

Description

Return a vector with the row-wise maxima of a matrix or dataframe.

Usage

rowMaxs(table)
rowMaxs(table)

Arguments

table

matrix or dataframe.

Value

a vector with the row-wise maxima.

Return a vector with the row-wise minima of a matrix or dataframe.

Description

Return a vector with the row-wise minima of a matrix or dataframe.

Usage

rowMins(table)
rowMins(table)

Arguments

table

matrix or dataframe.

Value

a vector with the row-wise minima.

Print a named vector of sequences as a fasta-formatted string

Description

Print a named vector of sequences as a fasta-formatted string

Usage

seqvec2fasta(seqvec)
seqvec2fasta(seqvec)

Arguments

seqvec

vector. The vector to be written as a fasta string.

Value

a character string with the sequence/s written in the fasta format.

Examples

data(Hadza)
seqvec2fasta(Hadza$orfs$seqs[1:10])
data(Hadza)
seqvec2fasta(Hadza$orfs$seqs[1:10])

Create a SQM object containing only the requested bins, and the contigs and ORFs contained in them.

Description

Create a SQM object containing only the requested bins, and the contigs and ORFs contained in them.

Usage

subsetBins(
  SQM,
  bins,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)
subsetBins(
  SQM,
  bins,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)

Arguments

`SQM`	SQM object to be subsetted.
`bins`	character. Vector of bins to be selected.
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object. By default it is set to `TRUE`, which means that the returned TPMs will be scaled by million of reads of the selected bins.
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to `TRUE`, which means that the returned copy numbers for each function will represent the average copy number of that function per genome of the selected bins.

Value

SQM object containing only the requested bins.

Examples

data(Hadza)
# Which are the most complete bins?
topBinNames = rownames(Hadza$bins$table)[order(Hadza$bins$table[,"Completeness"],
                                         decreasing=TRUE)][1:2]
# Subset with the most complete bin.
topBin = subsetBins(Hadza, topBinNames[1])
data(Hadza)
# Which are the most complete bins?
topBinNames = rownames(Hadza$bins$table)[order(Hadza$bins$table[,"Completeness"],
                                         decreasing=TRUE)][1:2]
# Subset with the most complete bin.
topBin = subsetBins(Hadza, topBinNames[1])

Select contigs

Description

Create a SQM object containing only the requested contigs, the ORFs contained in them and the bins that contain them.

Usage

subsetContigs(
  SQM,
  contigs,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE
)
subsetContigs(
  SQM,
  contigs,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE
)

Arguments

`SQM`	SQM object to be subsetted.
`contigs`	character. Vector of contigs to be selected.
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object (default `FALSE`).
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to `FALSE`, which means that the returned copy numbers for each function will represent the average copy number of that function per genome in the parent object.

Value

SQM object containing only the selected contigs.

Examples

data(Hadza)
# Which contigs have a GC content below 40?
lowGCcontigNames = rownames(Hadza$contigs$table[Hadza$contigs$table[,"GC perc"]<40,])
lowGCcontigs = subsetContigs(Hadza, lowGCcontigNames)
hist(lowGCcontigs$contigs$table[,"GC perc"])
data(Hadza)
# Which contigs have a GC content below 40?
lowGCcontigNames = rownames(Hadza$contigs$table[Hadza$contigs$table[,"GC perc"]<40,])
lowGCcontigs = subsetContigs(Hadza, lowGCcontigNames)
hist(lowGCcontigs$contigs$table[,"GC perc"])

Filter results by function

Description

Create a SQM object containing only the ORFs with a given function, and the contigs and bins that contain them.

Usage

subsetFun(
  SQM,
  fun,
  columns = NULL,
  ignore_case = TRUE,
  fixed = FALSE,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE
)
subsetFun(
  SQM,
  fun,
  columns = NULL,
  ignore_case = TRUE,
  fixed = FALSE,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE
)

Arguments

`SQM`	SQM object to be subsetted.
`fun`	character. Pattern to search for in the different functional classifications.
`columns`	character. Restrict the search to the provided column names from `SQM$orfs$table`. If not provided the search will be performed in all the columns containing functional information (default `NULL`).
`ignore_case`	logical Make pattern matching case-insensitive (default `TRUE`).
`fixed`	logical. If `TRUE`, pattern is a string to be matched as is. If `FALSE` the pattern is treated as a regular expression (default `FALSE`).
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object (default `FALSE`).
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to `FALSE`, which means that the returned copy numbers for each function will represent the average copy number of that function per genome in the parent object.

Value

SQM object containing only the requested function.

Examples

data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Search for multiple patterns using regular expressions
Hadza.twoKOs = subsetFun(Hadza, "K00812|K00813", fixed=FALSE)
data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Search for multiple patterns using regular expressions
Hadza.twoKOs = subsetFun(Hadza, "K00812|K00813", fixed=FALSE)

Select ORFs

Description

Create a SQM object containing only the requested ORFs, and the contigs and bins that contain them. Internally, all the other subset functions in this package end up calling subsetORFs to do the work for them.

Usage

subsetORFs(
  SQM,
  orfs,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  contigs_override = NULL
)
subsetORFs(
  SQM,
  orfs,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  contigs_override = NULL
)

Arguments

`SQM`	SQM object to be subsetted.
`orfs`	character. Vector of ORFs to be selected.
`tax_source`	character. Features used for calculating aggregated abundances at the different taxonomic ranks. Either `"orfs"` or `"contigs"` (default `"orfs"`).
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object (default `FALSE`).
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to `FALSE`, which means that the returned copy numbers for each function will represent the average copy number of that function per genome in the parent object.
`contigs_override`	character. Optional vector of contigs to be included in the subsetted object.

Value

SQM object containing the requested ORFs.

A note on contig/bins subsetting

While this function selects the contigs and bins that contain the desired orfs, it DOES NOT recalculate contig/bin abundance and statistics based on the selected ORFs only. This means that the abundances presented in tables such as SQM$contig$abund or SQM$bins$tpm will still refer to the complete contigs and bins, regardless of whether only a fraction of their ORFs are actually present in the returned SQM object. This is also true for the statistics presented in SQM$contigs$table and SQM$bins$table.

Examples

data(Hadza)
# Select the 100 most abundant ORFs in our dataset.
mostAbundantORFnames = names(sort(rowSums(Hadza$orfs$tpm), decreasing=TRUE))[1:100]
mostAbundantORFs = subsetORFs(Hadza, mostAbundantORFnames)
data(Hadza)
# Select the 100 most abundant ORFs in our dataset.
mostAbundantORFnames = names(sort(rowSums(Hadza$orfs$tpm), decreasing=TRUE))[1:100]
mostAbundantORFs = subsetORFs(Hadza, mostAbundantORFnames)

Select random ORFs

Description

Create a random subset of a SQM object.

Usage

subsetRand(SQM, N)
subsetRand(SQM, N)

Arguments

`SQM`	SQM object to be subsetted.
`N`	numeric. number of random ORFs to select.

Value

SQM object containing a random subset of ORFs.

Filter results by sample

Description

Create a SQM object containing only samples specified by the user, and the ORFs, contigs, bins, taxa and functions present in those samples.

Usage

subsetSamples(SQM, samples, remove_missing = TRUE)
subsetSamples(SQM, samples, remove_missing = TRUE)

Arguments

`SQM`	SQM object to be subsetted.
`samples`	character. Samples to be included in the subset.
`remove_missing`	bool. If `TRUE`, ORFs, contigs, bins, taxa and functions absent from the selected samples will be removed from the subsetted object (default `TRUE`).

Value

SQM object containing only the requested samples.

Filter results by taxonomy

Description

Create a SQM object containing only the contigs with a given consensus taxonomy, the ORFs contained in them and the bins that contain them.

Usage

subsetTax(
  SQM,
  rank,
  tax,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)
subsetTax(
  SQM,
  rank,
  tax,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE
)

Arguments

`SQM`	SQM object to be subsetted.
`rank`	character. The taxonomic rank from which to select the desired taxa (`superkingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`)
`tax`	character. The taxon to select.
`trusted_functions_only`	logical. If `TRUE`, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If `FALSE`, best hit annotations will be used (default `FALSE`).
`ignore_unclassified_functions`	logical. If `FALSE`, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If `TRUE`, they will be ignored (default `FALSE`).
`rescale_tpm`	logical. If `TRUE`, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object. By default it is set to `TRUE`, which means that the returned TPMs will be scaled by million of reads of the selected taxon.
`rescale_copy_number`	logical. If `TRUE`, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to `TRUE`, which means that the returned copy numbers for each function will represent the average copy number of that function per genome of the selected taxon.

Value

SQM object containing only the requested taxon.

Examples

data(Hadza)
Hadza.Prevotella = subsetTax(Hadza, "genus", "Prevotella")
Hadza.Proteobacteria = subsetTax(Hadza, "phylum", "Proteobacteria")
data(Hadza)
Hadza.Prevotella = subsetTax(Hadza, "genus", "Prevotella")
Hadza.Proteobacteria = subsetTax(Hadza, "phylum", "Proteobacteria")

summary method for class SQM

Description

Computes different statistics of the data contained in the SQM object.

Usage

## S3 method for class 'SQM'
summary(object, ...)
## S3 method for class 'SQM'
summary(object, ...)

Arguments

`object`	SQM object to be summarized.
`...`	Additional parameters (ignored).

Value

A list of summary statistics.

summary method for class SQMlite

Description

Computes different statistics of the data contained in the SQMlite object.

Usage

## S3 method for class 'SQMlite'
summary(object, ...)
## S3 method for class 'SQMlite'
summary(object, ...)

Arguments

`object`	SQMlite object to be summarized.
`...`	Additional parameters (ignored).

Value

A list of summary statistics.

Universal Single-Copy Genes

Description

Lists of Universal Single Copy Genes for Bacteria and Archaea. These are useful for transforming coverages or tpms into copy numbers. This is an alternative way of normalizing data in order to be able to compare functional profiles in samples with different sequencing depths.

Usage

data(USiCGs)
data(USiCGs)

Format

Character vector with the KEGG identifiers for 15 Universal Single Copy Genes.

Source

Carr et al., 2013. Table S1.

References

Carr, Shen-Orr & Borenstein (2013). Reconstructing the Genomic Content of Microbiome Taxa through Shotgun Metagenomic Deconvolution PLoS Comput. Biol. 9:e1003292. (PubMed).

Examples

data(Hadza)
data(USiCGs)
### Let's look at the Universal Single Copy Gene distribution in our samples.
KEGG.tpm = Hadza$functions$KEGG$tpm
all(USiCGs %in% rownames(KEGG.tpm)) # Are all the USiCGs present in our dataset?
# Plot a boxplot of USiCGs tpms and calculate median USiCGs tpm.
# This looks weird in the test dataset because it contains only a small subset of the metagenomes.
# In a set of complete metagenomes USiCGs should have fairly similar TPM averages
# and low dispersion across samples.
boxplot(t(KEGG.tpm[USiCGs,]), names=USiCGs, ylab="TPM", col="slateblue2")
 
### Now let's calculate the average copy numbers of each function.
# We do it for KEGG annotations here, but we could also do it for COGs or PFAMs.
USiCGs.cov = apply(Hadza$functions$KEGG$cov[USiCGs,], 2, median)
# Sample-wise division by the median USiCG coverage.
KEGG.copynumber = t(t(Hadza$functions$KEGG$cov) / USiCGs.cov)
data(Hadza)
data(USiCGs)
### Let's look at the Universal Single Copy Gene distribution in our samples.
KEGG.tpm = Hadza$functions$KEGG$tpm
all(USiCGs %in% rownames(KEGG.tpm)) # Are all the USiCGs present in our dataset?
# Plot a boxplot of USiCGs tpms and calculate median USiCGs tpm.
# This looks weird in the test dataset because it contains only a small subset of the metagenomes.
# In a set of complete metagenomes USiCGs should have fairly similar TPM averages
# and low dispersion across samples.
boxplot(t(KEGG.tpm[USiCGs,]), names=USiCGs, ylab="TPM", col="slateblue2")
 
### Now let's calculate the average copy numbers of each function.
# We do it for KEGG annotations here, but we could also do it for COGs or PFAMs.
USiCGs.cov = apply(Hadza$functions$KEGG$cov[USiCGs,], 2, median)
# Sample-wise division by the median USiCG coverage.
KEGG.copynumber = t(t(Hadza$functions$KEGG$cov) / USiCGs.cov)

Package 'SQMtools'

Help Index

Combine several SQM objects

Description

Usage

Arguments

Value

See Also

Examples

Combine several SQM or SQMlite objects

Description

Usage

Arguments

Value

See Also

Examples

Export the taxonomy of a SQM object into a Krona Chart

Description

Usage

Arguments

Details

Value

See Also

Examples

Export the functions of a SQM object into KEGG pathway maps

Description

Usage

Arguments

Value

See Also

Examples

Export results in tabular format

Description

Usage

Arguments

Value

Examples

Hadza hunter-gatherer gut metagenomes

Description

Usage

Format

Source

References

Examples

Load a SqueezeMeta project into R

Description

Usage

Arguments

Value

Prerequisites

The SQM object structure

Examples

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

Description

Usage

Arguments

Value

The SQMlite object structure

See Also

Examples

Single Copy Phylogenetic Marker Genes from Sunagawa's group (KOs)

Description

Usage

Format

References

See Also

Single Copy Phylogenetic Marker Genes from Sunagawa's group (OGs)

Description

Usage

Format

References

See Also

Get the N most abundant rows (or columns) from a numeric table

Description

Usage

Arguments

Value

Examples

Get the N most variable rows (or columns) from a numeric table

Description

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