qpcR (version 1.3-7.1)

modlist: Create nonlinear models from a dataframe and coerce them into a list

Description

Essential function to create a list of nonlinear models from the columns (runs) of a qPCR dataframe. This function houses different methods for curve transformation prior to fitting, such as normalization in [0, 1], smoothing, baseline subtraction etc. Runs that failed to fit or that have been identified as kinetic outliers (by default: lack of sigmoidal structure) can be removed automatically as well as their entries in an optionally supplied label vector.

Usage

modlist(x, cyc = 1, fluo = NULL, model = l4, check = "uni2", 
        checkPAR = parKOD(), remove = c("none", "fit", "KOD"), 
        exclude = NULL, labels = NULL, norm = FALSE, baseline = FALSE,
        basefac = 1, smooth = c("none", "smooth", "lowess", "supsmu", "spline"), 
        smoothPAR = list(span = 0.1), factor = 1, opt = FALSE,
        optPAR = list(sig.level = 0.05, crit = "ftest"), verbose = TRUE, ...)

Arguments

x

a dataframe containing the qPCR data or a single qPCR run of class 'pcrfit'.

cyc

the column containing the cycle data. Defaults to first column.

fluo

the column(s) (runs) to be analyzed. If NULL, all runs will be considered.

model

the model to be used for all runs.

check

the method for kinetic outlier detection. Default is check for sigmoidal structure, see KOD. To turn off, use NULL.

checkPAR

parameters to be supplied to the check method, see KOD.

remove

which runs to remove. Either "none", those which failed to "fit" or from the "KOD" outlier method.

exclude

either "" for samples with missing column names or a regular expression defining columns (samples) to be excluded from modlist. See 'Details'.

labels

a vector containing labels, i.e. for defining replicate groups prior to ratiobatch.

norm

logical. Should the raw data be normalized within [0, 1] before model fitting?

baseline

type of baseline subtraction. Same as in efficiency.

basefac

a factor when using averaged baseline cycles, such as 0.95.

smooth

which curve smoothing method to use. See 'Details'.

smoothPAR

parameters to be supplied to the smoothing functions. See 'Details'.

factor

a multiplication factor for the fluorescence response values (barely useful, but who knows...).

opt

logical. Should model selection be applied to each model?

optPAR

parameters to be supplied to mselect.

verbose

logical. If TRUE, fitting and tagging results will be displayed in the console.

...

other parameters to be passed to pcrfit.

Value

• A list with each item containing the model from each column. A names item (which is tagged by *NAME*, if fitting failed) containing the column name is attached to each model as well as an item isFitted with either TRUE (fitting converged) or FALSE (a fitting error occured). This information is useful when ratiocalc is to be applied and unsuccessful fits should automatically removed from the given group definition. If kinetic outlier detection is selected, an item isOutlier is attached, defining the run as an outlier (TRUE) or not (FALSE).

Details

The following smoothing methods are available for the fluorescence values: smooth, lowess, supsmu and spline. See documentation there. The author of this package favorizes "supsmu" with span = 0.1. In case of unsuccessful model fitting and if remove = "none" (default), the original data is included in the output, albeit with no fitting information. This is useful since using plot.pcrfit on the 'modlist' shows the non-fitted runs. If remove = "fit", the non-fitted runs are automatically removed and will thus not be displayed. If remove = "KOD", by default all runs without sigmoidal structure are removed likewise. If a labels vector lab is supplied, the labels from the failed fits are removed and a new label vector lab_mod is written to the global environment. This way, an initial labeling vector for all samples can be supplied, bad runs and their labels automatically removed and these transfered to downstream analysis (i.e. to ratiobatch) without giving errors. exclude offers an option to exclude samples from the modlist by some regular expression or by using "" for samples with empty column names. See 'Examples'.

See Also

pcrbatch for batch analysis using different methods.

Examples

## calculate efficiencies and ct values 
## for each run in the 'reps' data, 
## subtract baseline using the 
## first 8 cycles.
ml1 <- modlist(reps, model = l5, baseline = 1:8)
getPar(ml1, type = "curve")

## 'crossing points' for the first 3 runs (normalized)
##  and using best model from Akaike weights
ml2 <- modlist(reps, 1, 2:5, model = l5, norm = TRUE, 
               opt = TRUE, optPAR = list(crit = "weights"))
sapply(ml2, function(x) efficiency(x, plot = FALSE)$cpD2)

## convert a single run to a 'modlist'
m <- pcrfit(reps, 1, 2, l5)
ml3 <- modlist(m)

## using the 'testdat' set
## include failed fits
ml4 <- modlist(testdat, 1, 2:9,  model = l5)
plot(ml4, which = "single")

## remove failed fits and update a label vector
GROUP <- c("g1s1", "g1s2", "g1s3", "g1s4", "g1c1", "g1c2", "g1c3", "g1c4") 
ml5 <- modlist(testdat, 1, 2:9,  model = l5, labels = GROUP, remove = "KOD")
plot(ml5, which = "single")
GROUP_mod

## use one of the mechanistic models
## get D0 values
ml6 <- modlist(reps, model = mak3)
sapply(ml6, function(x) coef(x)[1])

## exclude first sample in each 
## replicate group of dataset 'reps'
ml7 <- modlist(reps, exclude = ".1")
plot(ml7, which = "single")

## using weighted fitting:
## weighted by inverse residuals
ml8 <- modlist(reps, weights = "1/abs(resid)")
plot(ml8, which = "single")