Estimate measurement noise for digital PCR data

This option estimates the unexplained variation in wastewater measurements using a coefficient of variation model specialized for digital PCR (e.g. digital droplet PCR). Specifically, the coefficient of variation is modeled as a function of the expected concentration according to the statistical properties of dPCR.

This model predicts a higher coefficient of variation at smaller concentrations, which often leads to a better model fit, even for measurements from other quantification methods such as qPCR.

If multiple measurements (replicates) per sample are provided, EpiSewer can also explicitly model variation before the replication stage.

Usage

noise_estimate_dPCR(
  replicates = FALSE,
  cv_prior_mu = 0,
  cv_prior_sigma = 1,
  total_partitions_prior_mu = 20000,
  total_partitions_prior_sigma = 5000,
  total_partitions_observe = FALSE,
  partition_variation_prior_mu = 0,
  partition_variation_prior_sigma = 0.05,
  volume_scaled_prior_mu = 1e-05,
  volume_scaled_prior_sigma = 4e-05,
  pre_replicate_cv_prior_mu = 0,
  pre_replicate_cv_prior_sigma = 1,
  prePCR_noise_type = "log-normal",
  use_taylor_approx = TRUE,
  modeldata = modeldata_init()
)

Arguments

replicates: Should replicates be used to explicitly model variation before the replication stage?
cv_prior_mu: Prior (mean) on the coefficient of variation of concentration measurements. Note that in contrast to using noise_estimate(), this does not include the technical noise of the digital PCR. This is because the dPCR noise is explicitly modeled (using the number of partitions and conversion factor). Moreover, when replicates=TRUE, this is only the CV after the replication stage (see details for more explanation).
cv_prior_sigma: Prior (standard deviation) on the coefficient of variation of concentration measurements.
total_partitions_prior_mu: Prior (mean) on the total number of partitions in the dPCR reaction.
total_partitions_prior_sigma: Prior (standard deviation) on the total number of partitions in the dPCR reaction. If this is set to zero, the total number of partitions will be fixed to the prior mean and not estimated.
total_partitions_observe: If TRUE, the total number of partitions is taken from the supplied measurements data.frame. This requires that the argument total_partitions_col is specified in concentrations_observe().
partition_variation_prior_mu: Prior (mean) on the coefficient of variation of the total number of partitions in the dPCR reaction. Usually, the maximum number of partitions possible for a given dPCR chip is not reached, i.e. a certain number of partitions is lost. This loss varies between PCR runs, and is modeled as log-normal distributed in EpiSewer.
partition_variation_prior_sigma: Prior (standard deviation) on the coefficient of variation of the total number of partitions in the dPCR reaction. If this is set to zero, the partition variation will be fixed to the prior mean and not estimated.
volume_scaled_prior_mu: Prior (mean) on the conversion factor (partition volume scaled by the dilution of wastewater in the assay) for the dPCR reaction. See details for further explanation.
volume_scaled_prior_sigma: Prior (standard deviation) on the conversion factor (partition volume scaled by the dilution of wastewater in the assay) for the dPCR reaction. If this is set to zero, the conversion factor will be fixed to the prior mean and not estimated.
pre_replicate_cv_prior_mu: Prior (mean) on the coefficient of variation of concentrations before the replication stage.
pre_replicate_cv_prior_sigma: Prior (standard deviation) on the coefficient of variation of concentrations before the replication stage.
prePCR_noise_type: The parametric distribution to assume for noise before the PCR assay. Currently supported are "log-normal" and "gamma". The choice of the parametric distribution typically makes no relevant difference for the noise model, but can make a relevant difference for the LOD model if LOD_estimate_dPCR() is used.
use_taylor_approx: If TRUE (default), a Taylor expansion approximation is used to estimate the CV of measurements under pre-PCR noise. The approximation is very accurate, unless concentrations are extremely high (so high that the quality of the measurements from dPCR would anyway be questionable).
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).

Details

The conversion factor (see volume_scaled_prior_mu, volume_scaled_prior_sigma) is the partition volume scaled by the dilution of the wastewater in the assay. The dilution accounts for all extraction and preparation steps. For example, if the partition volume is 4.5e-7 mL and the dilution of the wastewater is 100:3 (i.e. 100 gc/mL in the original wastewater sample correspond to 3 gc/mL in the PCR reaction), then the overall conversion factor is 4.5e-7 * 100 / 3 = 1.5e-5.

When replicates=TRUE, two coefficients of variation are estimated:

the CV before the replication stage (see pre_replicate_cv_prior_mu)
the CV after the replication stage (see cv_prior_mu)

The meaning of these CV estimates depends on the type of replicates. If the replicates are biological replicates (i.e. independently processed), then cv estimates the noise in the preprocessing before the PCR, and pre_replicate_cv estimates the noise from anything before preprocessing (e.g. sampling noise and all other unexplained variation). In contrast, if the replicates are technical replicates (i.e. several PCR runs of the same preprocessed sample), then cv estimates only unexplained PCR noise (should be close to zero), and pre_replicate_cv estimates all other noise (including preprocessing noise.)

The priors of this component have the following functional form:

coefficient of variation of concentration measurements (cv): Truncated normal
mean number of partitions in dPCR: Truncated normal
coefficient of variation of number of partitions in dPCR: Truncated normal
conversion factor for dPCR: Truncated normal
coefficient of variation of concentration before the replication stage (pre_replicate_cv): Truncated normal

Usage

Arguments

Value

Details

See also