Analyze Results Generated by the 'SqueezeMeta' Pipeline
Combine several SQM objects
Combine several SQM or SQMlite objects
Create a bin from a vector of contigs
Export the bins of a SQM object
Export the contigs of a SQM object
Export the taxonomy of a SQM object into a Krona Chart
Export the ORFs of a SQM object
Export the functions of a SQM object into KEGG pathway maps
Export results in tabular format
Find redundant contigs within a bin
Load a SqueezeMeta project into R
Load tables generated by sqm2tables.py
, sqmreads2tables.py
or `com...
Get the N most abundant rows (or columns) from a numeric table
Get the N most variable rows (or columns) from a numeric table
Plot a barplot using ggplot2
Barplot of the most abundant bins in a SQM object
Heatmap of the most abundant functions in a SQM object
Plot a heatmap using ggplot2
Barplot of the most abundant taxa in a SQM object
Remove contigs from a given bin
Return a vector with the row-wise maxima of a matrix or dataframe.
Return a vector with the row-wise minima of a matrix or dataframe.
Print a named vector of sequences as a fasta-formatted string
Convert a SQM object into a microtable object from the microeco pa...
Convert a SQM object into a phyloseq object from the phyloseq pack...
Create a SQM object containing only the requested bins, and the contig...
Select contigs
Filter results by function
Select ORFs
Select random ORFs
Filter results by sample
Filter results by taxonomy
summary method for class SQM
summary method for class SQMbunch
summary method for class SQMlite
'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>.