| Package | Description |
|---|---|
| de.jstacs.clustering.distances | |
| de.jstacs.data |
Provides classes for the representation of data.
The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets,
Sequence and its sub-classes to represent continuous and discrete sequences, and
DataSet to represent data sets comprising a set of sequences. |
| de.jstacs.motifDiscovery |
This package provides the framework including the interface for any de novo motif discoverer.
|
| de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
The package provides all interfaces and classes for a hidden Markov model (HMM).
|
| de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
The package provides different implementations of hidden Markov models based on
AbstractHMM. |
| de.jstacs.utils |
This package contains a bundle of useful classes and interfaces like ...
|
| Modifier and Type | Method and Description |
|---|---|
static Pair<Integer,Double> |
DeBruijnMotifComparison.compare(double[] profile1,
double[] profile2,
int maxShift)
Computes the correlation of the two score profiles with relative shifts of the profiles of up to
maxShift. |
static Pair<double[][],int[][]> |
DeBruijnMotifComparison.getClusterRepresentative(ClusterTree<StatisticalModel> tree,
int n)
Returns a position weight matrix (PWM) representation of the root node of the given cluster tree and
also computed the relative shifts of the motifs such that they align best with the consensus motif at the root.
|
| Modifier and Type | Method and Description |
|---|---|
Pair<DataSet[],double[][]> |
DataSet.partition(double[] sequenceWeights,
DataSet.PartitionMethod method,
double... percentage)
This method partitions the elements, i.e.
|
Pair<DataSet[],double[][]> |
DataSet.partition(double[] sequenceWeights,
DataSet.PartitionMethod method,
int k)
This method partitions the elements, i.e.
|
Pair<DataSet,double[]> |
DataSet.resize(double[] weights,
int subsequenceLength)
Returns modified version of this data set with adjusted subsequence length.
|
Pair<DataSet,double[]> |
DataSet.subSampling(double number,
double[] weights)
Sub-samples sequences and corresponding weights from this
DataSet. |
static Pair<DataSet,double[]> |
DataSet.union(DataSet[] s,
double[][] weights,
boolean[] in)
|
| Modifier and Type | Method and Description |
|---|---|
static Pair<Sequence,BitSet[]>[] |
KMereStatistic.getKmereSequenceStatistic(boolean bothStrands,
int maxMismatch,
HashSet<Sequence> filter,
DataSet... data)
This method enables the user to get a statistic for a set of
k-mers. |
Pair<double[][],double[]> |
SignificantMotifOccurrencesFinder.getPWMAndPosDist(int motif,
DataSet data,
double[] weights,
double[] mean,
int addLeft,
int addRight)
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder
together with standard deviation of binding site positions computed using the provided mean values for each sequence. |
Pair<double[][],double[]> |
SignificantMotifOccurrencesFinder.getPWMAndPosDist(int motif,
DataSet data,
double[] weights,
double[] mean,
int addLeft,
int addRight,
LinkedList<Sequence> bs,
DoubleList bsWeight,
DoubleList bsScores)
Returns the position weight matrix and standard deviation of the position distribution using the given mean.
|
Pair<double[][][],int[][]> |
SignificantMotifOccurrencesFinder.getPWMAndPositions(int motif,
DataSet data,
double[] weights,
int addLeft,
int addRight)
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder
together with the positions of the binding sites within the sequences of data and the corresponding p-values. |
| Modifier and Type | Method and Description |
|---|---|
abstract Pair<IntList,Double> |
AbstractHMM.getViterbiPathFor(int startPos,
int endPos,
Sequence seq) |
Pair<IntList,Double> |
AbstractHMM.getViterbiPathFor(Sequence seq) |
Pair<IntList,Double>[] |
AbstractHMM.getViterbiPathsFor(DataSet data)
This method returns the viterbi paths and scores for all sequences of the data set
data. |
static Pair<AbstractHMM,HomogeneousMMDiffSM> |
HMMFactory.parseProfileHMMFromHMMer(Reader hmmReader,
StringBuffer consensus,
LinkedList<Integer> matchStates,
LinkedList<Integer> silentStates)
Parses a profile HMM from the textual HMMer representation.
|
static Pair<double[][],double[]> |
HMMFactory.propagateESS(double ess,
ArrayList<HMMFactory.PseudoTransitionElement> list)
Propagates the
ess for an HMM with absorbing states. |
| Modifier and Type | Method and Description |
|---|---|
Pair<IntList,Double> |
SamplingHigherOrderHMM.getViterbiPath(int startPos,
int endPos,
Sequence seq,
SamplingHigherOrderHMM.ViterbiComputation compute)
This method returns a viterbi path that is the optimum for the choosen ViterbiComputation method
|
Pair<IntList,Double> |
SamplingHigherOrderHMM.getViterbiPathFor(int startPos,
int endPos,
Sequence seq) |
Pair<IntList,Double> |
HigherOrderHMM.getViterbiPathFor(int startPos,
int endPos,
Sequence seq) |
| Modifier and Type | Method and Description |
|---|---|
static Pair<BufferedImage,Graphics2D> |
SeqLogoPlotter.getBufferedImageAndGraphics(int height,
double[][] ps)
Creates a new
BufferedImage with given height and width chosen automatically according to the number of rows
of ps, and returns this BufferedImage and its Graphics2D object. |
static Pair<IntList,ArrayList<Sequence>> |
LargeSequenceReader.readNextSequences(BufferedReader read,
StringBuffer lastHeader,
int minimumLength)
Returns the next chunk of input sequences.
|