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## de.jstacs.motifDiscovery
Class KMereStatistic

    java.lang.Object
      de.jstacs.motifDiscovery.KMereStatistic

    public final class KMereStatistic

extends Object

This class enables the user to get some statistics of a `Sample` in an easy way.

Author:
  Jens Keilwagen

Constructor Summary
`KMereStatistic(Sample data, int k)` This constructor creates an internal statistic counting all `k`-mers in the `data`.

Method Summary
`static Sample.WeightedSampleFactory` | `getAbsoluteKMereFrequencies(Sample data, int k, boolean bothStrands)` This method enables the user to get a statistic over all `k`-mers in the `data`.
`static Sample.WeightedSampleFactory` | `getAbsoluteKMereFrequencies(Sample data, int k, boolean bothStrands, Sample.WeightedSampleFactory.SortOperation sortOp)` This method enables the user to get a statistic over all `k`-mers in the `data`.
`static Sequence[]` | `getCommonString(Sample data, int motifLength, boolean bothStrands)` This method returns an array of strings of length `motifLength` so that each String is contained in all sequences of the sample respectively in the sample and the reverse complementary sample.
`static LinkedList<Sequence>` | `getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic, int dataSetIndex, int threshold)` This method returns a list of `Sequence`s.
`static Hashtable<Sequence,BitSet[]>` | `getKmereSequenceStatistic(int k, boolean bothStrands, Sample... data)` This method enables the user to get a statistic over all `k`-mers in the sequences.
`double[][]` | `getSmoothedProfile(int window, Sequence... seq)` This method returns an array of smoothed profiles.
`double[][]` | `getSmoothedProfile(int window, String... kmere)` This method returns an array of smoothed profiles.
`static Hashtable<Sequence,BitSet[]>` | `merge(Hashtable<Sequence,BitSet[]> statistic, int maximalMissmatch, boolean bothStrands)` This method allows to merge the statistics of k-mers by allowing mismatches.
`static Hashtable<Sequence,BitSet[]>` | `removeBackground(Hashtable<Sequence,BitSet[]> statistic, int fgIndex, int bgIndex, double fgWeight, double bgWeight)` This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### KMereStatistic

    public KMereStatistic(Sample data,
                          int k)

  This constructor creates an internal statistic counting all `k`-mers in the `data`.

  Parameters:
    `data` - the data
    `k` - the number of symbols in each counted word

Method Detail

### getSmoothedProfile

    public double[][] getSmoothedProfile(int window,
                                         String... kmere)

  This method returns an array of smoothed profiles. For each k-mere it returns one profile. The order of the profile is the same as the order of the k-meres.

  Parameters:
    `window` - the window length, for no smoothing use 1
    `kmere` - the k-mere
  Returns:
    an array of smoothed profiles
  See Also:
    `getSmoothedProfile(int, Sequence...)`, `Sequence.create(AlphabetContainer, String)`

### getSmoothedProfile

    public double[][] getSmoothedProfile(int window,
                                         Sequence... seq)

  This method returns an array of smoothed profiles. For each k-mere it returns one profile. The order of the profile is the same as the order of the k-meres.

  Parameters:
    `window` - the window length, for no smoothing use 1
    `seq` - the `Sequence` instances containing the k-meres
  Returns:
    an array of smoothed profiles

### getCommonString

    public static Sequence[] getCommonString(Sample data,
                                             int motifLength,
                                             boolean bothStrands)
                                      throws Exception

  This method returns an array of strings of length `motifLength` so that each String is contained in all sequences of the sample respectively in the sample and the reverse complementary sample.

  Parameters:
    `data` - the sample of sequences
    `motifLength` - the motif length
    `bothStrands` - the switch for using both strand `true` or only forward strand `false`
  Returns:
    an array of Strings of length `motifLength` so that each String is contained in `data` respectively on one strand of the `data`
  Throws:
    `Exception` - if something went wrong

### getAbsoluteKMereFrequencies

    public static Sample.WeightedSampleFactory getAbsoluteKMereFrequencies(Sample data,
                                                                           int k,
                                                                           boolean bothStrands)
                                                                    throws Exception

  This method enables the user to get a statistic over all `k`-mers in the `data`. That is it counts the outcome of each `k`-mere in the complete `data`.

  Parameters:
    `data` - the sample of sequences
    `k` - the motif length
    `bothStrands` - the switch for using both strand `true` or only forward strand `false`. If `true` for each `k`-mer only this `k`-mere or its reverse complement is contained in the returned WeightedSampleFactory.
  Returns:
    a WeightedSampleFactory containing all `k`-mers and their absolute frequencies in `data` respectively on one strand of the `data`
  Throws:
    `Exception` - if something went wrong
  See Also:
    `getAbsoluteKMereFrequencies(Sample, int, boolean, Sample.WeightedSampleFactory.SortOperation)`, `Sample.WeightedSampleFactory.SortOperation.NO_SORT`

### getAbsoluteKMereFrequencies

    public static Sample.WeightedSampleFactory getAbsoluteKMereFrequencies(Sample data,
                                                                           int k,
                                                                           boolean bothStrands,
                                                                           Sample.WeightedSampleFactory.SortOperation sortOp)
                                                                    throws Exception

  This method enables the user to get a statistic over all `k`-mers in the `data`. That is it counts the outcome of each `k`-mere in the complete `data`.

  Parameters:
    `data` - the sample of sequences
    `k` - the motif length
    `bothStrands` - the switch for using both strand `true` or only forward strand `false`. If `true` for each `k`-mer only this `k`-mere or its reverse complement is contained in the returned WeightedSampleFactory.
    `sortOp` - the way how the result should be sorted
  Returns:
    a WeightedSampleFactory containing all `k`-mers and their absolute frequencies in `data` respectively on one strand of the `data`
  Throws:
    `Exception` - if something went wrong

### getKmereSequenceStatistic

    public static Hashtable<Sequence,BitSet[]> getKmereSequenceStatistic(int k,
                                                                         boolean bothStrands,
                                                                         Sample... data)
                                                                  throws WrongAlphabetException,
                                                                         OperationNotSupportedException

  This method enables the user to get a statistic over all `k`-mers in the sequences. That is, it creates for each occurring `k`-mer an array of `BitSet`s indicating for each data set and each sequence whether it contains the `k`-mer (or its reverse complement) or not.

  Parameters:
    `data` - the `Sample`s of `Sequence`s
    `k` - the motif length
    `bothStrands` - the switch for using both strand `true` or only forward strand `false`. If `true` for each `k`-mer only this `k`-mere or its reverse complement is contained in the returned WeightedSampleFactory.
  Returns:
    a `Hashtable` on `Sequence`s and arrays of `BitSet`s; each entry encodes a `k`-mer and the occurrence of this `k`-mer in each data set and sequence; if a `k`-mer occurs in data set `d` in sequence `n` the the `n`-th bit of the `d`-th `BitSet` is true.
  Throws:
    `WrongAlphabetException` - if the `AlphabetContainer`s of the `Sample`s do not match or if they are not simple and discrete
    `OperationNotSupportedException` - if the `bothStrands==true` but the reverse complement could not be computed
  See Also:
    `Hashtable`, `merge(Hashtable, int, boolean)`

### merge

    public static Hashtable<Sequence,BitSet[]> merge(Hashtable<Sequence,BitSet[]> statistic,
                                                     int maximalMissmatch,
                                                     boolean bothStrands)
                                              throws OperationNotSupportedException,
                                                     CloneNotSupportedException,
                                                     WrongLengthException,
                                                     WrongAlphabetException

  This method allows to merge the statistics of k-mers by allowing mismatches.

  Parameters:
    `statistic` - a statistic as obtained from `getKmereSequenceStatistic(int, boolean, Sample...)`
    `maximalMissmatch` - the maximal number of allowed mismatches
    `bothStrands` - the switch for using both strand `true` or only forward strand `false`.
  Returns:
    a merged statistic
  Throws:
    `OperationNotSupportedException` - if the `bothStrands==true` but the reverse complement could not be computed
    `CloneNotSupportedException` - if an array of `BitSet` can not be cloned
    `WrongAlphabetException` - see `Sequence.getHammingDistance(Sequence)`
    `WrongLengthException` - see `Sequence.getHammingDistance(Sequence)`
  See Also:
    `Sequence.getHammingDistance(Sequence)`, `getKmereSequenceStatistic(int, boolean, Sample...)`

### getConservedPatterns

    public static LinkedList<Sequence> getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic,
                                                            int dataSetIndex,
                                                            int threshold)

  This method returns a list of `Sequence`s. Each entry corresponds to a sequence or a set of sequences (depending on the input of the `statistic`) that occurs in more than `threshold` `Sequence`s of the data set.

  Parameters:
    `statistic` - a statistic as obtained from `getKmereSequenceStatistic(int, boolean, Sample...)` or `merge(Hashtable, int, boolean)`
    `dataSetIndex` - the index of the `BitSet` to be used
    `threshold` - a threshold that has to be exceeded by `BitSet.cardinality()` to be declared as a conserved pattern
  Returns:
    a list of conserved patterns
  See Also:
    `getKmereSequenceStatistic(int, boolean, Sample...)`, `merge(Hashtable, int, boolean)`

### removeBackground

    public static Hashtable<Sequence,BitSet[]> removeBackground(Hashtable<Sequence,BitSet[]> statistic,
                                                                int fgIndex,
                                                                int bgIndex,
                                                                double fgWeight,
                                                                double bgWeight)

  This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

  Parameters:
    `statistic` - a statistic as obtained from `getKmereSequenceStatistic(int, boolean, Sample...)` or `merge(Hashtable, int, boolean)`
    `fgIndex` - the foreground index of the `BitSet` to be used
    `bgIndex` - the background index of the `BitSet` to be used
    `fgWeight` - the weight used to weight the foreground cardinality
    `bgWeight` - the weight used to weight the background cardinality
  Returns:
    a `Hashtable` containing only the positive entries

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