MultiplexSet.RdR6 Class for loading, visualising and analysing barcode information
R6 Class for loading, visualising and analysing barcode information
floundeR::FloundeR -> MultiplexSet
enumerateprepares a simple 2D Angenieux enumeration of the provided dataset
for quick visualisation of the dataset.
new()Initialise a new instance of the R6 Class MultiplexSet
MultiplexSet$new(seqsum = NA, barcoding_summary_file = NA)
seqsuma tibble of sequencing summary information
barcoding_summary_fileis a file.path to the corresponding
barcoding_summary file that should be merged with the seqsum
content.
as_tibble()Export the imported dataset(s) as a tibble
This object consumes a sequencing summary file (and optionally the corresponding barcoding_summary file) and creates an object in memory that can be explored, sliced and filtered. This method dumps out the in-memory object for further exploration and development.
MultiplexSet$as_tibble()
A tibble representation of the starting dataset
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.
MultiplexSet$read_length_bins( qfilt = TRUE, normalised = TRUE, cumulative = FALSE, bins = 20, outliers = 0.025 )
qfiltspecifies how the quality information should be filtered
at the moment this only defines whether reporting is based on PASS or FAIL reads; would make more sense to have filtered by PASS / ALL?
normalisedshould the sequence collection be reported to normalise for the number of sequence bases sequenced or the number of sequence reads - TRUE by default to normalise for sequenced bases.
cumulativedefines whether cumulative sequence bases (reads) are reported per bin (FALSE by default).
binsthe number of sequence bins that should be prepared (20 by default)
outliersdefines the number of outliers (0.025 = 2.5%) that are excluded from the longest reads to prepare a richer distribution visulation - the plots can be bothered by the long tail of mini-whales.
Angenieux 2D graph object
quality_bins()bin the sequences in seqsum content into bins of quality
The nanopore sequencing run is expected to return a collection of sequences that vary in their quality distributions; this variance is a function of the sequencing library prepared, the starting DNA etc. This method is used to bin reads into uniform quality bins to assess the overall quality of the run and to identify potential issues
MultiplexSet$quality_bins(bins = 20, outliers = 0)
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?
Angenieux 2D graph object
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.
MultiplexSet$sequencingset(barcode)
barcodethe barcode that should be reported
SequencingSet object containing information on barcode of
interest.
temporalset()Prepare a TemporalSet object from a given barcode
This method is used to subset the SequencingSummary information to focus on a single barcode for a more detailed analysis of content.
MultiplexSet$temporalset(barcode)
barcodethe barcode that should be reported
TemporalSet object containing information on barcode of
interest.
clone()The objects of this class are cloneable with this method.
MultiplexSet$clone(deep = FALSE)
deepWhether to make a deep clone.