export_expvec_to_XL: Exports counts and featuredata in a SummarizedExperiment...

In johnmcculloch/JAMS_BW: Just A Microbiology System

Exports counts and featuredata in a SummarizedExperiment vector into a single spreadsheet.

Description

Exports counts and featuredata in a SummarizedExperiment vector into a single spreadsheet.

Usage

export_expvec_to_XL(
  expvec = NULL,
  usefulexp = NULL,
  filename = NULL,
  featcutoff = NULL,
  GenomeCompletenessCutoff = NULL,
  applyfilters = NULL,
  asPPM = TRUE,
  PPM_normalize_to_bases_sequenced = FALSE,
  includemetadata = TRUE,
  returncounts = FALSE,
  ...
)

Arguments

expvec	list of JAMS-style SummarizedExperiment objects.
usefulexp	String specifying the name(s) of the JAMS-style SummarizedExperiment object(s) to be exported into Excel. If NULL, will include all analysis contained in the expvec SummarizedExperiment list. Default is NULL.
filename	String specifying the Excel file name. If NULL (the default), the filename will be automatically assigned.
featcutoff	Requires a numeric vector of length 2 for specifying how to filter out features by relative abundance. The first value of the vector specifies the minimum relative abundance in Parts per Million (PPM) and the second value is the percentage of samples which must have at least that relative abundance. Thus, passing c(250, 10) to featcutoff would filter out any feature which does not have at least 250 PPM (= 0.025 percent) of relative abundance in at least 10 percent of all samples being plot. Please note that when using the subsetby option (q.v.) to automatically plot multiple plots of sample subsets, the featcutoff parameters are applied within the subset. The default is c(0, 0), meaning no feature is filtered. If NULL is passed, then the value defaults to c(0, 0). See also applyfilters for a shorthand way of applying multiple filtration settings.
GenomeCompletenessCutoff	Requires a numeric vector of length 2 for specifying how to filter out features by genome completeness. This is, of course, only applicble for taxonomic shotgun SummarizedExperiment objects. When passed to non-taxonomic shotgun SummarizedExperiment objects, GenomeCompletenessCutoff will be ignored. The first value of the vector specifies the minimum genome completeness in percentage and the second value is the percentage of samples which must have at least that genome completeness. Thus, passing c(50, 5) to GenomeCompletenessCutoff would filter out any taxonomic feature which does not have at least 50 percent of genome completeness in at least 5 percent of all samples being plot. Please note that when using the subsetby option (q.v.) to automatically plot multiple plots of sample subsets, the GenomeCompletenessCutoff parameters are applied within the subset. The default is c(0, 0), meaning no feature is filtered. If NULL is passed, then the value defaults to c(0, 0). See also applyfilters for a shorthand way of applying multiple filtration settings.
applyfilters	Optional string specifying filtration setting "combos", used as a shorthand for setting the featcutoff, GenomeCompletenessCutoff, minl2fc and minabscorrcoeff arguments in JAMS plotting functions. If NULL, none of these arguments are set if not specified. Permissible values for applyfilters are "light", "moderate" or "stringent". The actual values vary whether the SummarizedExperiment object is taxonomical (LKT) or not. For a taxonomical SummarizedExperiment object, using "light" will set featcutoff=c(50, 5), GenomeCompletenessCutoff=c(5, 5), minl2fc=1, minabscorrcoeff=0.4; using "moderate" will set featcutoff=c(250, 15), GenomeCompletenessCutoff=c(10, 5), minl2fc=1, minabscorrcoeff=0.6; and using "stringent" will set featcutoff=c(2000, 15), GenomeCompletenessCutoff=c(30, 10), minl2fc=2, minabscorrcoeff=0.8. For non-taxonomical (i.e. functional) SummarizedExperiment objects, using "light" will set featcutoff=c(0, 0), minl2fc=1, minabscorrcoeff=0.4; using "moderate" will set featcutoff=c(5, 5), minl2fc=1, minabscorrcoeff=0.6; and using "stringent" will set featcutoff=c(50, 15), minl2fc=2.5, minabscorrcoeff=0.8. When using applyfilters, one can still set one or more of featcutoff, GenomeCompletenessCutoff, minl2fc and minabscorrcoeff, which will then take the user set value in lieu of those set by the applyfilters shorthand. Default is light.
PPM_normalize_to_bases_sequenced	Requires a logical value. Non-filtered JAMS feature counts tables (the BaseCounts assay within SummarizedExperiment objects) always includes unclassified taxonomical features (for taxonomical SummarizedExperiment objects) or unknown/unattributed functional features (for non-taxonomical SummarizedExperiment objects), so the relative abundance for each feature (see normalization) will be calculated in Parts per Million (PPM) by dividing the number of bases covering each feature by the sum of each sample column **previous to any filtration**. Relative abundances are thus representative of the entirety of the genomic content for taxonomical objects, whereas for non-taxonomical objects, strictly speaking, it is the abundance of each feature relative to only the coding regions present in the metagenome, even if these are annotationally unatributed. In other words, intergenic regions are not taken into account. In order to relative-abundance-normalize a **non-taxonomical** SummarizedExperiment object with the total genomic sequencing content, including non-coding regions, set PPM_normalize_to_bases_sequenced = TRUE. Default is FALSE.
includemetadata	Requires a logical value. If TRUE, information within colData of your inputted expvec, which contains all information pertaining to the samples, will be exported into a "Metadata" tab within the Excel sheet. If FALSE, will omit this information in the Excel. Default is TRUE.
returncounts	Requires a logical value. If TRUE, will print the information to be exported to Excel onto the terminal (which can be directed into a variable). If the analysis is taxonomical, this information will include metadata (if desired), the relative abundance of each feature for each taxonomic feature in Parts Per Million(PPM) for each of the samples, the Genome Completeness for each taxonomic feature in each of the samples, the Percentage from Contigs for each taxonomic feature in each of the samples, and a featuretable containing the feature rownames and any associated taxonomic information. If the analysis is functional, this will include metadata (if desired), the relative abundance of each functional feature in the analysis in Parts Per Million(PPM) for each of the samples, the Genecounts consisting of the number of features annotated in the analysis, and a featuretable containing the feature rownames, an accession, and any addtional information on the feature. If FALSE (the default), the information described above will be exported to Excel only.

johnmcculloch/JAMS_BW documentation built on April 30, 2024, 8:09 p.m.