MultiplexSet: R6 Class for loading, visualising and analysing barcode...
| MultiplexSet | R Documentation |
R6 Class for loading, visualising and analysing barcode information
Description
R6 Class for loading, visualising and analysing barcode information
R6 Class for loading, visualising and analysing barcode information
Super class
floundeR::FloundeR -> MultiplexSet
Active bindings
enumerateprepares a simple
2D Angenieuxenumeration of the provided dataset for quick visualisation of the dataset.
Methods
Public methods
Inherited methods
Method new()
Initialise a new instance of the R6 Class MultiplexSet
Usage
MultiplexSet$new(seqsum = NA, barcoding_summary_file = NA)
Arguments
seqsuma tibble of sequencing summary information
barcoding_summary_fileis a file.path to the corresponding barcoding_summary file that should be merged with the
seqsumcontent.
Method as_tibble()
Export the imported dataset(s) as a tibble
This object consumes a sequencing summary file (and optionally the corresponding barcoding_summary file) and creates an object in memory that can be explored, sliced and filtered. This method dumps out the in-memory object for further exploration and development.
Usage
MultiplexSet$as_tibble()
Returns
A tibble representation of the starting dataset
Method read_length_bins()
bin the sequences in seqsum content into bins of sequence length
The nanopore sequencing run is expected to return a collection of sequences that vary in their length distributions; this variance is a function of the sequencing library prepared, the starting DNA etc. This method is used to bin reads into uniform bins & assess the distribution of sequence lengths.
Usage
MultiplexSet$read_length_bins( qfilt = TRUE, normalised = TRUE, cumulative = FALSE, bins = 20, outliers = 0.025 )
Arguments
qfilt-
specifies how the quality information should be filtered
at the moment this only defines whether reporting is based on PASS or FAIL reads; would make more sense to have filtered by PASS / ALL?
normalised-
should the sequence collection be reported to normalise for the number of sequence bases sequenced or the number of sequence reads - TRUE by default to normalise for sequenced bases.
cumulativedefines whether cumulative sequence bases (reads) are reported per bin (FALSE by default).
binsthe number of sequence bins that should be prepared (20 by default)
outliersdefines the number of outliers (0.025 = 2.5%) that are excluded from the longest reads to prepare a richer distribution visulation - the plots can be bothered by the long tail of mini-whales.
Returns
Angenieux 2D graph object
Method quality_bins()
bin the sequences in seqsum content into bins of quality
The nanopore sequencing run is expected to return a collection of sequences that vary in their quality distributions; this variance is a function of the sequencing library prepared, the starting DNA etc. This method is used to bin reads into uniform quality bins to assess the overall quality of the run and to identify potential issues
Usage
MultiplexSet$quality_bins(bins = 20, outliers = 0)
Arguments
binsthe number of sequence bins that should be prepared (20 by default)
outliersdefines the number of outliers (0 = 0%) that are excluded from the reads - should probably be deprecated for simplicity?
Returns
Angenieux 2D graph object
Method sequencingset()
Prepare a SequencingSet object from a given barcode
This method is used to subset the sequencing_summary information to focus on a single barcode for more detailed analysis.
Usage
MultiplexSet$sequencingset(barcode)
Arguments
barcodethe barcode that should be reported
Returns
SequencingSet object containing information on barcode of
interest.
Method temporalset()
Prepare a TemporalSet object from a given barcode
This method is used to subset the SequencingSummary information to focus on a single barcode for a more detailed analysis of content.
Usage
MultiplexSet$temporalset(barcode)
Arguments
barcodethe barcode that should be reported
Returns
TemporalSet object containing information on barcode of
interest.
Method clone()
The objects of this class are cloneable with this method.
Usage
MultiplexSet$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
