R6 Class for performing Flowcell centric analyses

This class aims to simplify the handling and exploration of Flowcell based data and contains various presets, designs and visualisation tools required for assessing flowcell performance and metrics.

Super class

floundeR::FloundeR -> Flowcell

Active bindings

platform

Have a guess at the most likely flowcell platform usedThe sequencing summary file contains no information on the sequencing device or flowcell used. For the preparation of channel density maps it is worth considering which flowcell type is most likely to have been used - this can be guessed on the number of channels described within the data

density_data

produce channelMap for spatial plotsprepares a matrix of X, Y coordinates and the corresponding readcount information for the type of flowcell predicted by get_flowcell_platform

Methods

Public methods

• Flowcell$new()

• Flowcell$set_channel_counts()

• Flowcell$as_tibble()

• Flowcell$clone()

Inherited methods

• floundeR::FloundeR$bin_data()

• floundeR::FloundeR$num_scale()

• floundeR::FloundeR$nums_scale()

• floundeR::FloundeR$print()

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Method new()

Creates a new Flowcell object. This initialisation method performs other sanity checking of the defined file(s) and creates the required data structures.

Usage

Flowcell$new()

Returns

A new Flowcell object.

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Method set_channel_counts()

set channel count summary information

This method is used to provide primitive channel count information for the number of total reads that have been observed per channel - this is used for the generation of spatial plots

Usage

Flowcell$set_channel_counts(channel_counts)

Arguments

channel_counts

a tibble of count information

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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

Flowcell$as_tibble()

Returns

A tibble representation of the starting dataset

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Method clone()

The objects of this class are cloneable with this method.

Usage

Flowcell$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.