* This class is used extensively in calculating alignment colourschemes that
* depend on the amount of conservation in each alignment column.
*
- * @author $author$
- * @version $Revision$
*/
public class AAFrequency
{
- public static final String PROFILE = "P";
-
- private static final String AMINO = "amino";
-
- private static final String DNA = "DNA";
-
- private static final String RNA = "RNA";
+ private static final double LOG2 = Math.log(2);
- /*
- * Quick look-up of String value of char 'A' to 'Z'
- */
- private static final String[] CHARS = new String['Z' - 'A' + 1];
-
- static
- {
- for (char c = 'A'; c <= 'Z'; c++)
- {
- CHARS[c - 'A'] = String.valueOf(c);
- }
- }
+ public static final String PROFILE = "P";
public static final ProfilesI calculate(List<SequenceI> list, int start,
int end)
}
}
-
-
/**
* Calculate the consensus symbol(s) for each column in the given range.
*
{
if (sequences[row] == null)
{
- System.err
- .println("WARNING: Consensus skipping null sequence - possible race condition.");
+ System.err.println(
+ "WARNING: Consensus skipping null sequence - possible race condition.");
continue;
}
- char[] seq = sequences[row].getSequence();
- if (seq.length > column)
+ if (sequences[row].getLength() > column)
{
- char c = seq[column];
+ char c = sequences[row].getCharAt(column);
residueCounts.add(c);
if (Comparison.isNucleotide(c))
{
// System.out.println(elapsed);
}
- public static ProfilesI calculateInformation(final HiddenMarkovModel hmm,
- int width, int start, int end, boolean saveFullProfile,
- boolean removeBelowBackground)
+ /**
+ * Returns the full set of profiles for a hidden Markov model. The underlying
+ * data is the raw probabilities of a residue being emitted at each node,
+ * however the profiles returned by this function contain the percentage
+ * chance of a residue emission.
+ *
+ * @param hmm
+ * @param width
+ * The width of the Profile array (Profiles) to be returned.
+ * @param start
+ * The alignment column on which the first profile is based.
+ * @param end
+ * The alignment column on which the last profile is based.
+ * @param removeBelowBackground
+ * if true, symbols with a match emission probability less than
+ * background frequency are ignored
+ * @return
+ */
+ public static ProfilesI calculateHMMProfiles(final HiddenMarkovModel hmm,
+ int width, int start, int end, boolean removeBelowBackground,
+ boolean infoLetterHeight)
{
ProfileI[] result = new ProfileI[width];
- int symbolCount = hmm.getNumberOfSymbols();
- String alph = hmm.getAlphabetType();
+ char[] symbols = hmm.getSymbols().toCharArray();
+ int symbolCount = symbols.length;
for (int column = start; column < end; column++)
{
ResidueCount counts = new ResidueCount();
- for (char symbol : hmm.getSymbols())
+ for (char symbol : symbols)
{
- int value = getAnalogueCount(hmm, column, removeBelowBackground,
- alph, symbol);
+ int value = getAnalogueCount(hmm, column, symbol,
+ removeBelowBackground, infoLetterHeight);
counts.put(symbol, value);
}
int maxCount = counts.getModalCount();
int gapCount = counts.getGapCount();
ProfileI profile = new Profile(symbolCount, gapCount, maxCount,
maxResidue);
-
- if (saveFullProfile)
- {
- profile.setCounts(counts);
- }
+ profile.setCounts(counts);
result[column] = profile;
}
* @param endCol
* end column (exclusive)
* @param ignoreGaps
- * if true, normalise residue percentages
+ * if true, normalise residue percentages
* @param showSequenceLogo
* if true include all information symbols, else just show modal
* residue
- * @param nseq
- * number of sequences
*/
- public static void completeInformation(AlignmentAnnotation information,
- ProfilesI profiles, int startCol, int endCol,
- boolean ignoreBelowBackground,
- boolean showSequenceLogo, long nseq)
+ public static float completeInformation(AlignmentAnnotation information,
+ ProfilesI profiles, int startCol, int endCol)
{
// long now = System.currentTimeMillis();
- if (information == null || information.annotations == null
- || information.annotations.length < endCol)
+ if (information == null || information.annotations == null)
{
/*
* called with a bad alignment annotation row
* wait for it to be initialised properly
*/
- return;
+ return 0;
}
- Float max = 0f;
+ float max = 0f;
+ SequenceI hmmSeq = information.sequenceRef;
- for (int i = startCol; i < endCol; i++)
+ int seqLength = hmmSeq.getLength();
+ if (information.annotations.length < seqLength)
{
- ProfileI profile = profiles.get(i);
- if (profile == null)
+ return 0;
+ }
+
+ HiddenMarkovModel hmm = hmmSeq.getHMM();
+
+ for (int column = startCol; column < endCol; column++)
+ {
+ if (column >= seqLength)
{
- /*
- * happens if sequences calculated over were
- * shorter than alignment width
- */
- information.annotations[i] = null;
- return;
+ // hmm consensus sequence is shorter than the alignment
+ break;
}
-
- HiddenMarkovModel hmm;
-
- SequenceI hmmSeq = information.sequenceRef;
-
- hmm = hmmSeq.getHMM();
- Float value = getInformationContent(i, hmm);
-
- if (value > max)
+ float value = hmm.getInformationContent(column);
+ boolean isNaN = Float.isNaN(value);
+ if (!isNaN)
{
- max = value;
+ max = Math.max(max, value);
}
- String description = value + " bits";
-
- information.annotations[i] = new Annotation(" ", description,
- ' ', value);
+ String description = isNaN ? null
+ : String.format("%.4f bits", value);
+ information.annotations[column] = new Annotation(
+ Character.toString(
+ Character.toUpperCase(hmmSeq.getCharAt(column))),
+ description, ' ', value);
}
+
information.graphMax = max;
- // long elapsed = System.currentTimeMillis() - now;
- // System.out.println(-elapsed);
+ return max;
}
/**
- * Derive the gap count annotation row.
+ * Derive the occupancy count annotation
*
- * @param gaprow
+ * @param occupancy
* the annotation row to add annotations to
* @param profiles
* the source consensus data
* @param endCol
* end column (exclusive)
*/
- public static void completeGapAnnot(AlignmentAnnotation gaprow,
+ public static void completeGapAnnot(AlignmentAnnotation occupancy,
ProfilesI profiles, int startCol, int endCol, long nseq)
{
- if (gaprow == null || gaprow.annotations == null
- || gaprow.annotations.length < endCol)
+ if (occupancy == null || occupancy.annotations == null
+ || occupancy.annotations.length < endCol)
{
/*
* called with a bad alignment annotation row
return;
}
// always set ranges again
- gaprow.graphMax = nseq;
- gaprow.graphMin = 0;
- double scale = 0.8/nseq;
+ occupancy.graphMax = nseq;
+ occupancy.graphMin = 0;
+ double scale = 0.8 / nseq;
for (int i = startCol; i < endCol; i++)
{
ProfileI profile = profiles.get(i);
* happens if sequences calculated over were
* shorter than alignment width
*/
- gaprow.annotations[i] = null;
+ occupancy.annotations[i] = null;
return;
}
String description = "" + gapped;
- gaprow.annotations[i] = new Annotation("", description,
- '\0', gapped, jalview.util.ColorUtils.bleachColour(
- Color.DARK_GRAY, (float) scale * gapped));
+ occupancy.annotations[i] = new Annotation("", description, '\0',
+ gapped,
+ jalview.util.ColorUtils.bleachColour(Color.DARK_GRAY,
+ (float) scale * gapped));
}
}
* <ul>
* <li>the full profile (percentages of all residues present), if
* showSequenceLogo is true, or</li>
- * <li>just the modal (most common) residue(s), if showSequenceLogo is false</li>
+ * <li>just the modal (most common) residue(s), if showSequenceLogo is
+ * false</li>
* </ul>
* Percentages are as a fraction of all sequence, or only ungapped sequences
* if ignoreGaps is true.
String description = null;
if (counts != null && showSequenceLogo)
{
- int normaliseBy = ignoreGaps ? profile.getNonGapped() : profile
- .getHeight();
+ int normaliseBy = ignoreGaps ? profile.getNonGapped()
+ : profile.getHeight();
description = counts.getTooltip(normaliseBy, dp);
}
else
* contains
*
* <pre>
- * [profileType, numberOfValues, nonGapCount, charValue1, percentage1, charValue2, percentage2, ...]
+ * [profileType, numberOfValues, totalPercent, charValue1, percentage1, charValue2, percentage2, ...]
* in descending order of percentage value
* </pre>
*
*/
public static int[] extractProfile(ProfileI profile, boolean ignoreGaps)
{
- int[] rtnval = new int[64];
ResidueCount counts = profile.getCounts();
if (counts == null)
{
char[] symbols = symbolCounts.symbols;
int[] values = symbolCounts.values;
QuickSort.sort(values, symbols);
- int nextArrayPos = 2;
int totalPercentage = 0;
- final int divisor = ignoreGaps ? profile.getNonGapped() : profile
- .getHeight();
+ final int divisor = ignoreGaps ? profile.getNonGapped()
+ : profile.getHeight();
/*
* traverse the arrays in reverse order (highest counts first)
*/
+ int[] result = new int[3 + 2 * symbols.length];
+ int nextArrayPos = 3;
+ int nonZeroCount = 0;
+
for (int i = symbols.length - 1; i >= 0; i--)
{
int theChar = symbols[i];
int charCount = values[i];
-
- rtnval[nextArrayPos++] = theChar;
final int percentage = (charCount * 100) / divisor;
- rtnval[nextArrayPos++] = percentage;
+ if (percentage == 0)
+ {
+ /*
+ * this count (and any remaining) round down to 0% - discard
+ */
+ break;
+ }
+ nonZeroCount++;
+ result[nextArrayPos++] = theChar;
+ result[nextArrayPos++] = percentage;
totalPercentage += percentage;
}
- rtnval[0] = symbols.length;
- rtnval[1] = totalPercentage;
- int[] result = new int[rtnval.length + 1];
+
+ /*
+ * truncate array if any zero values were discarded
+ */
+ if (nonZeroCount < symbols.length)
+ {
+ int[] tmp = new int[3 + 2 * nonZeroCount];
+ System.arraycopy(result, 0, tmp, 0, tmp.length);
+ result = tmp;
+ }
+
+ /*
+ * fill in 'header' values
+ */
result[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
- System.arraycopy(rtnval, 0, result, 1, rtnval.length);
+ result[1] = nonZeroCount;
+ result[2] = totalPercentage;
return result;
}
* contains
*
* <pre>
- * [profileType, numberOfValues, totalCount, charValue1, percentage1, charValue2, percentage2, ...]
+ * [profileType, numberOfValues, totalPercentage, charValue1, percentage1, charValue2, percentage2, ...]
* in descending order of percentage value, where the character values encode codon triplets
* </pre>
*
* @param hashtable
* @return
*/
- public static int[] extractCdnaProfile(Hashtable hashtable,
- boolean ignoreGaps)
+ public static int[] extractCdnaProfile(
+ Hashtable<String, Object> hashtable, boolean ignoreGaps)
{
// this holds #seqs, #ungapped, and then codon count, indexed by encoded
// codon triplet
{
break; // nothing else of interest here
}
+ final int percentage = codonCount * 100 / divisor;
+ if (percentage == 0)
+ {
+ /*
+ * this (and any remaining) values rounded down to 0 - discard
+ */
+ break;
+ }
distinctValuesCount++;
result[j++] = codons[i];
- final int percentage = codonCount * 100 / divisor;
result[j++] = percentage;
totalPercentage += percentage;
}
* the consensus data stores to be populated (one per column)
*/
public static void calculateCdna(AlignmentI alignment,
- Hashtable[] hconsensus)
+ Hashtable<String, Object>[] hconsensus)
{
final char gapCharacter = alignment.getGapCharacter();
List<AlignedCodonFrame> mappings = alignment.getCodonFrames();
for (int col = 0; col < cols; col++)
{
// todo would prefer a Java bean for consensus data
- Hashtable<String, int[]> columnHash = new Hashtable<>();
+ Hashtable<String, Object> columnHash = new Hashtable<>();
// #seqs, #ungapped seqs, counts indexed by (codon encoded + 1)
int[] codonCounts = new int[66];
codonCounts[0] = alignment.getSequences().size();
{
continue;
}
- List<char[]> codons = MappingUtils
- .findCodonsFor(seq, col, mappings);
+ List<char[]> codons = MappingUtils.findCodonsFor(seq, col,
+ mappings);
for (char[] codon : codons)
{
int codonEncoded = CodingUtils.encodeCodon(codon);
{
codonCounts[codonEncoded + 2]++;
ungappedCount++;
+ break;
}
}
}
*/
public static void completeCdnaConsensus(
AlignmentAnnotation consensusAnnotation,
- Hashtable[] consensusData, boolean showProfileLogo, int nseqs)
+ Hashtable<String, Object>[] consensusData,
+ boolean showProfileLogo, int nseqs)
{
if (consensusAnnotation == null
|| consensusAnnotation.annotations == null
consensusAnnotation.scaleColLabel = true;
for (int col = 0; col < consensusData.length; col++)
{
- Hashtable hci = consensusData[col];
+ Hashtable<String, Object> hci = consensusData[col];
if (hci == null)
{
// gapped protein column?
int modalCodonEncoded = codons[codons.length - 1];
int modalCodonCount = sortedCodonCounts[codons.length - 1];
- String modalCodon = String.valueOf(CodingUtils
- .decodeCodon(modalCodonEncoded));
- if (sortedCodonCounts.length > 1
- && sortedCodonCounts[codons.length - 2] == sortedCodonCounts[codons.length - 1])
+ String modalCodon = String
+ .valueOf(CodingUtils.decodeCodon(modalCodonEncoded));
+ if (sortedCodonCounts.length > 1 && sortedCodonCounts[codons.length
+ - 2] == sortedCodonCounts[codons.length - 1])
{
/*
* two or more codons share the modal count
{
if (samePercent.length() > 0)
{
- mouseOver.append(samePercent).append(": ")
- .append(lastPercent).append("% ");
+ mouseOver.append(samePercent).append(": ").append(lastPercent)
+ .append("% ");
}
samePercent.setLength(0);
samePercent.append(codon);
}
/**
- * Returns the information content at a specified column.
+ * Returns the sorted HMM profile for the given column of the alignment. The
+ * returned array contains
*
- * @param column
- * Index of the column, starting from 0.
- * @return
- */
- public static float getInformationContent(int column,
- HiddenMarkovModel hmm)
- {
- float informationContent = 0f;
-
- for (char symbol : hmm.getSymbols())
- {
- float freq = 0f;
- if ("amino".equals(hmm.getAlphabetType()))
- {
- freq = ResidueProperties.aminoBackgroundFrequencies.get(symbol);
- }
- if ("DNA".equals(hmm.getAlphabetType()))
- {
- freq = ResidueProperties.dnaBackgroundFrequencies.get(symbol);
- }
- if ("RNA".equals(hmm.getAlphabetType()))
- {
- freq = ResidueProperties.rnaBackgroundFrequencies.get(symbol);
- }
- Double hmmProb = hmm.getMatchEmissionProbability(column, symbol);
- float prob = hmmProb.floatValue();
- informationContent += prob * (Math.log(prob / freq) / Math.log(2));
-
- }
-
- return informationContent;
- }
-
- /**
- * Produces a HMM profile for a column in an alignment
+ * <pre>
+ * [profileType=0, numberOfValues, 100, charValue1, percentage1, charValue2, percentage2, ...]
+ * in descending order of percentage value
+ * </pre>
*
- * @param aa
- * Alignment annotation for which the profile is being calculated.
+ * @param hmm
* @param column
- * Column in the alignment the profile is being made for.
* @param removeBelowBackground
- * Boolean indicating whether to ignore residues with probabilities
- * less than their background frequencies.
+ * if true, ignores residues with probability less than their
+ * background frequency
+ * @param infoHeight
+ * if true, uses the log ratio 'information' measure to scale the
+ * value
* @return
*/
public static int[] extractHMMProfile(HiddenMarkovModel hmm, int column,
- boolean removeBelowBackground)
+ boolean removeBelowBackground, boolean infoHeight)
{
+ if (hmm == null)
+ {
+ return null;
+ }
+ String alphabet = hmm.getSymbols();
+ int size = alphabet.length();
+ char symbols[] = new char[size];
+ int values[] = new int[size];
+ int totalCount = 0;
- if (hmm != null)
+ for (int i = 0; i < size; i++)
{
- String alph = hmm.getAlphabetType();
- int size = hmm.getNumberOfSymbols();
- char symbols[] = new char[size];
- int values[] = new int[size];
- List<Character> charList = hmm.getSymbols();
- Integer totalCount = 0;
-
- for (int i = 0; i < size; i++)
- {
- char symbol = charList.get(i);
- symbols[i] = symbol;
- int value = getAnalogueCount(hmm, column, removeBelowBackground,
- alph, symbol);
- values[i] = value;
- totalCount += value;
- }
+ char symbol = alphabet.charAt(i);
+ symbols[i] = symbol;
+ int value = getAnalogueCount(hmm, column, symbol,
+ removeBelowBackground, infoHeight);
+ values[i] = value;
+ totalCount += value;
+ }
- QuickSort.sort(values, symbols);
+ /*
+ * sort symbols by increasing emission probability
+ */
+ QuickSort.sort(values, symbols);
- int[] profile = new int[3 + size * 2];
+ int[] profile = new int[3 + size * 2];
- profile[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
- profile[1] = size;
- profile[2] = totalCount / 100;
+ profile[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
+ profile[1] = size;
+ profile[2] = 100;
- if (totalCount != 0)
+ /*
+ * order symbol/count profile by decreasing emission probability
+ */
+ if (totalCount != 0)
+ {
+ int arrayPos = 3;
+ for (int k = size - 1; k >= 0; k--)
{
- int arrayPos = 3;
- for (int k = size - 1; k >= 0; k--)
+ Float percentage;
+ int value = values[k];
+ if (removeBelowBackground)
+ {
+ percentage = ((float) value) / totalCount * 100f;
+ }
+ else
{
- Double percentage;
- Integer value = values[k];
- percentage = (value.doubleValue() / totalCount.doubleValue())
- * 100d;
- profile[arrayPos] = symbols[k];
- profile[arrayPos + 1] = percentage.intValue();
- arrayPos += 2;
+ percentage = value / 100f;
}
+ int intPercent = Math.round(percentage);
+ profile[arrayPos] = symbols[k];
+ profile[arrayPos + 1] = intPercent;
+ arrayPos += 2;
}
- return profile;
}
- return null;
+ return profile;
}
- private static int getAnalogueCount(HiddenMarkovModel hmm, int column,
- boolean removeBelowBackground, String alph, char symbol)
+ /**
+ * Converts the emission probability of a residue at a column in the alignment
+ * to a 'count', suitable for rendering as an annotation value
+ *
+ * @param hmm
+ * @param column
+ * @param symbol
+ * @param removeBelowBackground
+ * if true, returns 0 for any symbol with a match emission
+ * probability less than the background frequency
+ * @infoHeight if true, uses the log ratio 'information content' to scale the
+ * value
+ * @return
+ */
+ static int getAnalogueCount(HiddenMarkovModel hmm, int column,
+ char symbol, boolean removeBelowBackground, boolean infoHeight)
{
- Double value;
-
- value = hmm.getMatchEmissionProbability(column, symbol);
- double freq;
-
- if (AMINO.equals(alph) && removeBelowBackground)
+ double value = hmm.getMatchEmissionProbability(column, symbol);
+ double freq = ResidueProperties.backgroundFrequencies
+ .get(hmm.getAlphabetType()).get(symbol);
+ if (value < freq && removeBelowBackground)
{
- freq = ResidueProperties.aminoBackgroundFrequencies.get(symbol);
- if (value < freq)
- {
- value = 0d;
- }
+ return 0;
}
- else if (DNA.equals(alph) && removeBelowBackground)
- {
- freq = ResidueProperties.dnaBackgroundFrequencies.get(symbol);
- if (value < freq)
- {
- value = 0d;
- }
- }
- else if (RNA.equals(alph) && removeBelowBackground)
+
+ if (infoHeight)
{
- freq = ResidueProperties.rnaBackgroundFrequencies.get(symbol);
- if (value < freq)
- {
- value = 0d;
- }
+ value = value * (Math.log(value / freq) / LOG2);
}
- value = value * 10000;
- return value.intValue();
+
+ value = value * 10000d;
+ return Math.round((float) value);
}
}
git://source.jalview.org / jalview.git / blobdiff