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Observe positive dPCR partition counts

Source: R/model_measurements.R
concentrations_observe_partitions.Rd

This option fits the EpiSewer model to positive partition counts in digital PCR (dPCR), e.g. positive droplets in ddPCR. This allows the use of a dPCR-specific likelihood using a Binomial model for the number of positive partitions observed. For a more generic likelihood, see concentrations_observe().

Usage

concentrations_observe_partitions(
  measurements = NULL,
  composite_window = 1,
  date_col = "date",
  concentration_col = "concentration",
  positive_partitions_col = "positive_partitions",
  replicate_col = NULL,
  n_averaged = 1,
  n_averaged_col = NULL,
  total_partitions_col,
  modeldata = modeldata_init()
)

Arguments

measurements

A data.frame with each row representing one measurement. Must have at least a column with dates and a column with concentration measurements.

composite_window

Over how many days has each measured sample been collected? If 1 (default), samples represent single days. If larger than 1, samples are assumed to be equivolumetric composites over several dates. In this case, the supplied dates represent the last day included in each sample.

date_col

Name of the column containing the dates.

concentration_col

Name of the column containing the measured concentrations.

positive_partitions_col

Name of the column in the measurements data.frame containing the number of positive partitions (e.g. positive droplets for ddPCR) in the dPCR reaction of each measurement. If several technical replicates are used, this should be the AVERAGE number of positive partitions per replicate.

replicate_col

Name of the column containing the replicate ID of each measurement. This is used to identify multiple measurements made of a sample from the same date. Should be NULL if only one measurement per date was made.

n_averaged

The number of technical replicates (i.e. repeated PCR runs) used for each sample or biological replicate. The concentration provided in the measurements data.frame is assumed to be the average or pooled estimate from several technical replicates. Can be either a single number (it is then assumed that the number of averaged replicates is the same for each observation) or a vector (one value for each observation).

n_averaged_col

Name of the column in the measurements data.frame containing the number of technical replicates over which the measurements have been averaged/pooled. This is an alternative to specifying n_averaged.

total_partitions_col

Name of the column in the measurements data.frame containing the number of total partitions (e.g. droplets for ddPCR) in the dPCR reaction of each measurement. If several technical replicates are used, this should be the AVERAGE number of valid partitions per replicate. Only applies when modeling concentration measurements via the dPCR-specific noise model. Can be used by the noise_estimate_dPCR() and LOD_estimate_dPCR() modeling components. Note that this is really the number of valid partitions, not the number of positive partitions.

modeldata

A modeldata object to which the above model specifications should be added. Default is an empty model given by modeldata_init(). Can also be an already partly specified model returned by other EpiSewer modeling functions.

Value

A modeldata object containing data and specifications of the model to be fitted. Can be passed on to other EpiSewer modeling functions to add further data and model specifications.

The modeldata object also includes information about parameter initialization (.init), meta data (.metainfo), and checks to be performed before model fitting (.checks).

See also

Other observation types: concentrations_observe()