R6 Class for analysing sequence sets with accompanying temporal data

R6 Class for analysing sequence sets with accompanying temporal data

R6 Class for analysing sequence sets with accompanying temporal data

Super class

floundeR::FloundeR -> TemporalSet

Methods

Public methods

• TemporalSet$new()

• TemporalSet$as_tibble()

• TemporalSet$runtime()

• TemporalSet$run_yield()

• TemporalSet$feature_over_time()

• TemporalSet$t50()

• TemporalSet$clone()

Inherited methods

• floundeR::FloundeR$bin_data()

• floundeR::FloundeR$num_scale()

• floundeR::FloundeR$nums_scale()

• floundeR::FloundeR$print()

---

Method new()

Initialise a new instance of the R6 Class TemporalSet

Usage

TemporalSet$new(seqsum = NA)

Arguments

seqsum

a tibble of sequencing summary information

---

Method as_tibble()

Export the imported dataset(s) as a tibble

Usage

TemporalSet$as_tibble()

Returns

A tibble representation of the starting dataset

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Method runtime()

get a rounded runtime from temporal information in sequencing summary

The sequencing summary file contains information on pore dwell time and start time for given sequencing reads. An analysis of the start time information can be used to calculate a canonical sequencing run time that is rounded to key temporal breaks.

Usage

TemporalSet$runtime()

Returns

a numeric describing the rounded runtime in hours

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Method run_yield()

Prepare summary statistics that describe a flowcell run's yield

A flowcell can yield both sequence bases and sequence reads and the acquisition of these data has a temporal element. This method is used to summarise run performance through assessment of yield per unit time.

Usage

TemporalSet$run_yield(resolution = 15, bases = TRUE, cumulative = TRUE)

Arguments

resolution

describes the temporal resolution (in minutes) by which yield will be summarised.

bases

a logical that describes whether the method will summarise sequence reads or sequence bases - (TRUE) for bases by default

cumulative

defines whether the number of reads(bases) per bin is described as actual number or as a temporally cumulative number

Returns

Angenieux object prepared for rendering in reports

---

Method feature_over_time()

Report a binned temporal data facet (such as speed, quality, length)

The temporal sequencing information can be used to summarise other data facets in a time dependent manner to address questions such as whether there is a change in sequencing speed, length or quality over time.

Usage

TemporalSet$feature_over_time(
  resolution = 60,
  passes = TRUE,
  feature = "speed"
)

Arguments

resolution

describes the temporal resolution (in minutes) by which yield will be summarised (60 minutes by default).

passes

is a logical that defines whether the plot should present data that has passed or failed QC - (TRUE) by default to select for only the QC passed sequence reads.

feature

defines the feature to summarise (speed by default) but could include e.g. sequence_length_template or mean_qscore_template.

Returns

Angenieux object prepared for rendering in reports

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Method t50()

calculate T50 timepoint at which 50% of sequence reads are obtained

Temporal data and yield data can be used to identify timepoints at which a given fraction of the data has been obtained.

Usage

TemporalSet$t50(passes = TRUE, t = 0.5)

Arguments

passes

is a logical that defines that we should focus only on the passed QC reads (TRUE by default) - should probably be deprecated since asking for T50 of failed reads is just silly?

t

is the fractional timepoint (0.5 by default) where we are interested in knowing the time at which this fraction of reads was obtained.

Returns

numeric describing timepoint in hours at which fractional data was obtained.

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Method clone()

The objects of this class are cloneable with this method.

Usage

TemporalSet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.