alignME: Align time-course data based on an Mixed-Effects alignment...
In dkaschek/blotIt2: Align Time-Course Data by a Mixed-Effects Alignment Model

Description Usage Arguments Details Value See Also Examples

The function deals primarily with time-course data of different targets which have been measured under different experimental conditions and whose measured values might be on a different scale, e.g. because of different amplification. The algorithm determines the different scaling and estimates the time-course on a common scale.

alignME(
  data,
  model = "ys/sj",
  errmodel = "value*sigmaR",
  fixed = ys ~ Condition,
  latent = sj ~ Experiment,
  error = sigmaR ~ 1,
  log = TRUE,
  normalize = TRUE,
  reduce = FALSE,
  verbose = FALSE,
  normalize_input = T
)

`data`	data frame with obligatory columns "name", "time" and "value". Additionally, `data` should have further columns, e.g. characterizing experimental conditions (the fixed effects) and sources of variance between data of the same experimental condition (the latent variables). Alternatively, object of class `aligned`.
`model`	character defining the model by which the values in `data` can be described, e.g. "ys/sj"
`errmodel`	character defining a model for the standard deviation of a value, e.g. "sigma0 + value * sigmaR". This model can contain parameters, e.g. "sigma0" and "sigmaR", or numeric variables from `data`, e.g. "value" or "time".
`fixed`	two-sided formula of the form `par1+par2+... ~ name1+name2+...` where "par1, par2, ..." are parameters contained in `model`, e.g. "ys", and "name1, ..." refers to variables in `data`, e.g. "Condition". The parameters "par1, ..." are determined specific to the levels of "name1, ...".
`latent`	two-sided formula of the form `par1+par2+... ~ name1+name2+...` where "par1, par2, ..." are parameters contained in `model`, e.g. "sj", and "name1, ..." refers to variables in `data`, e.g. "Experiment".
`error`	two-sided formula of the form `par1+par2+... ~ name1+name2+...` where "par1, par2, ..." are parameter containd in `error`, e.g. "sigma0" and "sigmaR", and "name1, ..." refers to variables in `data`. If the same values of "par1, ..." should be assumed for all data, "name1" can be "1".
`log`	logical indicating whether all parameters are fitted on log-scale.
`normalize`	logical indicating whether the fixed effect parameter should be normalized to unit mean.
`verbose`	logical, print out information about each fit
`normalize_input`	logical, if TRUE the input will be normalized before scaling. see splitData.

Alignment of time-course data is achieved by an alignment model which explains the observed data by a function mixing fixed effects, usually parameters reflecting the "underlying" time-course, and latent variables, e.g. scaling parameters taking account for effects like different amplification or loading, etc. Depending on the measurement technique, the data has constant relative error, or constant absolute error or even a combination of those. This error is described by an error function. The error parameters are usually global, i.e. the same parameter values are assumed for all data points.

Object of class aligned, i.e. a data frame of the alignment result containing an attribute "outputs": a list of data frames

prediction: original data with value and sigma replaced by the predicted values and sigmas
scaled: original data with the values transformed according to the inverse model, i.e. model solved for the first parameter in fixed, e.g. "ys". Sigma values are computed by error propagation from the inverse model equation.
aligned: the reduced data with the fixed effects and their uncertainty, only. The result of the alignment algorithm.
original: the original data
parameter: original data augmented by parameter columns. Parameters in each row correspond to the levels of fixed, latent or error as passed to alignME(). Used for initialization or parameter values when refitting with modified model.

The estimated parameters are returned by the attribute "parameters".

read.wide to read data in a wide column format and get it in the right format for alignME(). plot1, plot2, plot3, plot4 to plot the result of alignME.

## Not run: 

data(MAPK)

## Run alignME with standard arguments
out <- alignME(data = MAPK,
               model = "ys/sj",
               errmodel = "sigmaR*value",
               fixed = ys~Condition,
               latent = sj~Experiment,
               error = sigmaR~1,
               log = TRUE)
plot1(out)
plot2(out)
plot3(out)

## Assume equal variance on all gels
out <- alignME(data = MAPK,
               model = "ys/sj",
               errmodel = "sigma0",
               fixed = ys~Condition,
               latent = sj~Experiment,
               error = sigma0~1,
               log = TRUE)
plot2(out)

## Estimate with offset
out <- alignME(data = MAPK,
               model = "ys/sj+bj",
               errmodel = "sigmaR*value",
               fixed = ys~Condition,
               latent = sj+bj~Experiment,
               error = sigmaR~1,
               log = TRUE)
plot2(out)

## Align data on log-scale
logMAPK <- MAPK
logMAPK$value <- log(MAPK$value)
out <- alignME(data = logMAPK,
               model = "log(ys/sj)",
               errmodel = "sigmaR",
               fixed = ys~Condition,
               latent = sj~Experiment,
               error = sigmaR~1,
               log = TRUE)
plot1(out)

## Align data on log-scale with mixed error model
logMAPK <- MAPK
logMAPK$value <- log(MAPK$value)
out <- alignME(data = logMAPK,
               model = "log(ys/sj)",
               errmodel = "sigmaR + sigma0*exp(-value)",
               fixed = ys~Condition,
               latent = sj~Experiment,
               error = sigmaR+sigma0~1,
               log = TRUE)
plot1(out)


## End(Not run)