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Estimate measurement noise for digital PCR data

Source: R/model_measurements.R
noise_estimate_dPCR.Rd

This option models concentration measurements using an error model specialized for digital PCR (e.g. digital droplet PCR). This is a fast approximation assuming a fixed number of valid total partitions in the dPCR assay and a known conversion factor. For a model that jointly estimates uncertain assay parameters, see noise_estimate_dPCR_params().

This model predicts more relative 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_observe = FALSE,
  max_partitions = 30000,
  partition_loss_mean = 0.1,
  volume_scaled = 1e-05,
  pre_replicate_cv_prior_mu = 0,
  pre_replicate_cv_prior_sigma = 1,
  prePCR_noise_type = "log-normal",
  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 analyzed concentrations (pre-PCR noise). Note that this does not include the technical noise of the digital PCR. This is because the dPCR noise is explicitly modeled (using the total number of partitions and conversion factor). Moreover, when replicates=TRUE, noise before the replication stage is separately modeled, so cv_prior_mu only describes any remaining pre-PCR noise (see details for more explanation).

cv_prior_sigma

Prior (standard deviation) on the coefficient of variation of analyzed concentrations.

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().

max_partitions

The maximum total number of dPCR partitions. This is usually defined by the manufacturer of the dPCR system/chip used, which supports a certain maximum number of partitions.

partition_loss_mean

The mean relative partition loss. A certain proportion of partitions in a dPCR run is typically invalid and discarded from the concentration estimate.

volume_scaled

The conversion factor (partition volume multiplied with the scaling of concentration in the assay) for the dPCR reaction. See details for further explanation.

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.

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) is the partition volume v multiplied with a scaling factor s. The scaling factor accounts for concentration differences between the sample and the reaction mix, for example due to extraction or adding of reagents. For example, if the partition volume is 4.5e-7 mL and the scaling factor is 100:3 (i.e. 100 gc/mL in the original 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

  • coefficient of variation of concentration before the replication stage (pre_replicate_cv): Truncated normal

See also

LOD_estimate_dPCR for a limit of detection model specialised for dPCR.

noise_estimate_dPCR_params for jointly estimating uncertain parameters of the assay.

Other noise models: noise_estimate(), noise_estimate_constant_var(), noise_estimate_dPCR_params()