processData {tigre} | R Documentation

## Processing expression time series

### Description

`processData` further processes time series data preprocessed by `puma` or `lumi`.

`processRawData` performs similar processing for other data.

Both functions return `ExpressionTimeSeries` objects that can be used as input for the functions `GPLearn` and `GPRankTargets`.

### Usage

      processData(data, times = NULL, experiments = NULL,
        do.normalisation = TRUE)
      processRawData(rawData, times, experiments = NULL,
        is.logged = TRUE, do.normalisation = ifelse(is.logged, TRUE, FALSE))

### Arguments

`data` | The preprocessed data from `mmgMOS` in the `puma` package (an `exprReslt` object) or from the `lumi` package (a `LumiBatch` object).
`rawData` | Raw data matrix to be used. Each row corresponds to a gene and each column to a data point.
`times` | Observation times of each data point. If unspecified or NULL, `processData` attempts to infer this from phenoData(data) field containing 'time' in the name.
`experiments` | The replicate structure of the data indicating which expression data points arise from which experiments. This should be an array in integers from 1 to N with length equal to the number of data points. By default all the data points are assumed to be from same replicate.
`is.logged` | Indicates whether the expression values are on log scale or not. Normalisation of non-logged data is unsupported.
`do.normalisation` | Indicates whether to perform the normalisation.

### Details

The expression data (and percentiles, if available) are normalized by equalising the mean of log-expression in each time points. In `processData`, a normal distribution is then fitted into the data with distfit.

### Value

An `ExpressionTimeSeries` object containing all provided information.

### Author(s)

Antti Honkela, Jonatan Ropponen

### See Also

`GPLearn, GPRankTargets`.

### Examples

      ## Load a mmgmos preprocessed fragment of the Drosophila developmental
      ## time series
      data(drosophila_mmgmos_fragment)

      ## Process the data (3 experiments containing 12 time points each)
      drosophila_gpsim_fragment <- processData(drosophila_mmgmos_fragment,
        experiments=rep(1:3, each=12))

[Package tigre version 1.40.0 Index]