readLearnTS trains an SVM classifier using cell features and
a training cell set. predictCellLabels predicts cell labels.
readLearnTS(x, featurePar, trainingSet, access='cache', cost, gamma)
predictCellLabels(x, uname, access='cache')getUnames for details.local, server and cache,
the default. See fileHTS for details.c(0.1, 1, 10, 20).c(0.0001, 0.001, 0.01, 0.1).classifier, the
trained classifier obtained by tune.svm and cft, the
features that were used to train the classifier.
readLearnTS trains an SVM classifier using cell features and
a training cell set. Features enumerated in the
remove.classification.features field of the feature parameters
are not considered for classification. The training set, pointed by trainingSet,
is a tab-separated file containing the rows uname, spot,
id and label. Each row designates a cell. This file is
constructed by using the output of the cellPicker module, see
popCellPicker. After completion, readLearnTS writes the
a RDA file \'data/classifier.rda\' in the local project
directory. This file contains the list returned by readLearnTS. predictCellLabels uses the trained classifier located in the
file \'data/classifier.rda\' and cell features to predict cell labels
of wells indicated by uname. For each well, the function
writes the file clabels, which contains the predicted cell
labels.
If present, popCellPicker shows the predicted cell
labels. Several iterations of readLearnTS,
predictCellLabels and popCellPicker calls are useful to
build an efficient cell classifier.
popCellPicker