processData {tigre} | R Documentation |
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
.
processData(data, times = NULL, experiments = NULL, do.normalisation = TRUE) processRawData(rawData, times, experiments = NULL, is.logged = TRUE, do.normalisation = ifelse(is.logged, TRUE, FALSE))
data |
The preprocessed data from |
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, |
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. |
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.
An ExpressionTimeSeries
object containing all provided information.
Antti Honkela, Jonatan Ropponen
## 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))