Model: Logistic regression model for the standard curve

In SerolyzeR: Reading, Quality Control and Preprocessing of MBA (Multiplex Bead Assay) Data

Logistic regression model for the standard curve

Description

The Model class is a wrapper around the nplr model. It allows to predict the RAU (Relative Antibody Unit) values directly from the MFI values of a given sample.

The nplr model is fitted using the formula:

y = B + \frac{T - B}{(1 + 10^{b \cdot (x_{mid} - x)})^s},

where:

• y is the predicted value, MFI in our case,

• x is the independent variable, dilution in our case,

• B is the bottom plateau - the right horizontal asymptote,

• T is the top plateau - the left horizontal asymptote,

• b is the slope of the curve at the inflection point,

• x_{mid} is the x-coordinate at the inflection point,

• s is the asymmetric coefficient.

This equation is referred to as the Richards' equation. More information about the model can be found in the nplr package documentation.

After the model is fitted to the data, the RAU values can be predicted using the predict method. The RAU value is simply a predicted dilution value (using the standard curve) for a given MFI multiplied by 1,000 000 to have a more readable value. For more information about the differences between dilution, RAU and MFI values, please see the "Normalisation" section in the "Basic SerolyzeR functionalities" vignette.

Public fields

analyte

(character(1))
Name of the analyte for which the model was fitted

dilutions

(numeric())
Dilutions used to fit the model

mfi

(numeric())
MFI values used to fit the model

mfi_min

(numeric(1))
Minimum MFI used for scaling MFI values to the range [0, 1]

mfi_max

(numeric(1))
Maximum MFI used for scaling MFI values to the range [0, 1]

model

(nplr)
Instance of the nplr model fitted to the data

log_dilution

(logical())
Indicator should the dilutions be transformed using the log10 function

log_mfi

(logical())
Indicator should the MFI values be transformed using the log10 function

scale_mfi

(logical())
Indicator should the MFI values be scaled to the range [0, 1]

Active bindings

top_asymptote

(numeric(1))
The top asymptote of the logistic curve

bottom_asymptote

(numeric(1))
The bottom asymptote of the logistic curve

Methods

Public methods

• Model$new()

• Model$predict()

• Model$get_plot_data()

• Model$print()

• Model$clone()

---

Method new()

Create a new instance of Model R6 class

Usage

Model$new(
  analyte,
  dilutions,
  mfi,
  npars = 5,
  verbose = TRUE,
  log_dilution = TRUE,
  log_mfi = TRUE,
  scale_mfi = TRUE,
  mfi_min = NULL,
  mfi_max = NULL
)

Arguments

analyte

(character(1))
Name of the analyte for which the model was fitted.

dilutions

(numeric())
Dilutions used to fit the model

mfi

MFI (numeric())
values used to fit the model

npars

(numeric(1))
Number of parameters to use in the model

verbose

(logical())
If TRUE prints messages, TRUE by default

log_dilution

(logical())
If TRUE the dilutions are transformed using the log10 function, TRUE by default

log_mfi

(logical())
If TRUE the MFI values are transformed using the log10 function, TRUE by default

scale_mfi

(logical())
If TRUE the MFI values are scaled to the range [0, 1], TRUE by default

mfi_min

(numeric(1))
Enables to set the minimum MFI value used for scaling MFI values to the range [0, 1]. Use values before any transformations (e.g., before the log10 transformation)

mfi_max

(numeric(1))
Enables to set the maximum MFI value used for scaling MFI values to the range [0, 1]. Use values before any transformations (e.g., before the log10 transformation)

---

Method predict()

Predict RAU values from the MFI values

Usage

Model$predict(mfi, over_max_extrapolation = 0, eps = 1e-06)

Arguments

mfi

(numeric())
MFI values for which we want to predict the RAU values

over_max_extrapolation

(numeric(1))
How much we can extrapolate the values above the maximum RAU value seen in standard curve samples \text{RAU}_{max}. Defaults to 0. If the value of the predicted RAU is above RAU_{max} + \text{over\_max\_extrapolation}, the value is censored to the value of that sum.

eps

(numeric(1))
A small value used to avoid numerical issues close to the asymptotes

Warning: High dose hook effect affects which dilution and MFI values are used to fit the logistic model and by extension affects the over_max_extrapolation value. When a high dose hook effect is detected we remove the samples with dilutions above the high dose threshold (which by default is 1/200). Making the highest RAU value lower than the one calculated without handling of the high dose hook effect.

Returns

(data.frame())
Dataframe with the predicted RAU values for given MFI values The columns are named as follows:

• RAU - the Relative Antibody Units (RAU) value

• MFI - the predicted MFI value

---

Method get_plot_data()

Data that can be used to plot the standard curve.

Usage

Model$get_plot_data()

Returns

(data.frame())
Prediction dataframe for scaled MFI (or logMFI) values in the range [0, 1]. Columns are named as in the predict method

---

Method print()

Function prints the basic information about the model such as the number of parameters or samples used

Usage

Model$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Model$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

plate_file <- system.file("extdata", "CovidOISExPONTENT.csv", package = "SerolyzeR")
layout_file <- system.file("extdata", "CovidOISExPONTENT_layout.csv", package = "SerolyzeR")
plate <- read_luminex_data(plate_file, layout_filepath = layout_file)
model <- create_standard_curve_model_analyte(plate, "S2", log_mfi = TRUE)
print(model)

SerolyzeR documentation built on April 12, 2025, 2:11 a.m.