*/
package jalview.analysis;
-import jalview.analysis.ResidueCount.SymbolCounts;
import jalview.datamodel.AlignedCodonFrame;
import jalview.datamodel.AlignmentAnnotation;
import jalview.datamodel.AlignmentI;
import jalview.datamodel.Annotation;
+import jalview.datamodel.HiddenMarkovModel;
+import jalview.datamodel.Profile;
+import jalview.datamodel.ProfileI;
+import jalview.datamodel.Profiles;
+import jalview.datamodel.ProfilesI;
+import jalview.datamodel.ResidueCount;
+import jalview.datamodel.ResidueCount.SymbolCounts;
import jalview.datamodel.SequenceI;
import jalview.ext.android.SparseIntArray;
+import jalview.schemes.ResidueProperties;
import jalview.util.Comparison;
import jalview.util.Format;
import jalview.util.MappingUtils;
import jalview.util.QuickSort;
+import java.awt.Color;
import java.util.Arrays;
import java.util.Hashtable;
import java.util.List;
* 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 MAXCOUNT = "C";
-
- public static final String MAXRESIDUE = "R";
-
- public static final String PID_GAPS = "G";
-
- public static final String PID_NOGAPS = "N";
+ private static final double LOG2 = Math.log(2);
public static final String PROFILE = "P";
- public static final String ENCODED_CHARS = "E";
-
- /*
- * 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 Profile[] calculate(List<SequenceI> list,
- int start, int end)
+ public static final ProfilesI calculate(List<SequenceI> list, int start,
+ int end)
{
return calculate(list, start, end, false);
}
- public static final Profile[] calculate(List<SequenceI> sequences,
+ public static final ProfilesI calculate(List<SequenceI> sequences,
int start, int end, boolean profile)
{
SequenceI[] seqs = new SequenceI[sequences.size()];
for (int i = 0; i < sequences.size(); i++)
{
seqs[i] = sequences.get(i);
- if (seqs[i].getLength() > width)
+ int length = seqs[i].getLength();
+ if (length > width)
{
- width = seqs[i].getLength();
+ width = length;
}
}
- Profile[] reply = new Profile[width];
-
if (end >= width)
{
end = width;
}
- calculate(seqs, start, end, reply, profile);
+ ProfilesI reply = calculate(seqs, width, start, end, profile);
return reply;
}
}
* Calculate the consensus symbol(s) for each column in the given range.
*
* @param sequences
+ * @param width
+ * the full width of the alignment
* @param start
* start column (inclusive, base zero)
* @param end
* end column (exclusive)
- * @param result
- * array in which to store profile per column
* @param saveFullProfile
* if true, store all symbol counts
*/
- public static final void calculate(final SequenceI[] sequences,
- int start, int end, Profile[] result, boolean saveFullProfile)
+ public static final ProfilesI calculate(final SequenceI[] sequences,
+ int width, int start, int end, boolean saveFullProfile)
{
// long now = System.currentTimeMillis();
int seqCount = sequences.length;
int nucleotideCount = 0;
int peptideCount = 0;
+ ProfileI[] result = new ProfileI[width];
+
for (int column = start; column < end; column++)
{
/*
{
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))
{
int maxCount = residueCounts.getModalCount();
String maxResidue = residueCounts.getResiduesForCount(maxCount);
int gapCount = residueCounts.getGapCount();
- Profile profile = new Profile(seqCount, gapCount, maxCount,
+ ProfileI profile = new Profile(seqCount, gapCount, maxCount,
maxResidue);
if (saveFullProfile)
result[column] = profile;
}
+ return new Profiles(result);
// long elapsed = System.currentTimeMillis() - now;
// System.out.println(elapsed);
}
/**
+ * 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];
+ char[] symbols = hmm.getSymbols().toCharArray();
+ int symbolCount = symbols.length;
+ for (int column = start; column < end; column++)
+ {
+ ResidueCount counts = new ResidueCount();
+ for (char symbol : symbols)
+ {
+ int value = getAnalogueCount(hmm, column, symbol,
+ removeBelowBackground, infoLetterHeight);
+ counts.put(symbol, value);
+ }
+ int maxCount = counts.getModalCount();
+ String maxResidue = counts.getResiduesForCount(maxCount);
+ int gapCount = counts.getGapCount();
+ ProfileI profile = new Profile(symbolCount, gapCount, maxCount,
+ maxResidue);
+ profile.setCounts(counts);
+
+ result[column] = profile;
+ }
+ return new Profiles(result);
+ }
+
+ /**
* Make an estimate of the profile size we are going to compute i.e. how many
* different characters may be present in it. Overestimating has a cost of
* using more memory than necessary. Underestimating has a cost of needing to
* the annotation row to add annotations to
* @param profiles
* the source consensus data
- * @param iStart
- * start column
- * @param width
- * end column
+ * @param startCol
+ * start column (inclusive)
+ * @param endCol
+ * end column (exclusive)
* @param ignoreGaps
* if true, normalise residue percentages ignoring gaps
* @param showSequenceLogo
* number of sequences
*/
public static void completeConsensus(AlignmentAnnotation consensus,
- Profile[] profiles, int iStart, int width, boolean ignoreGaps,
+ ProfilesI profiles, int startCol, int endCol, boolean ignoreGaps,
boolean showSequenceLogo, long nseq)
{
// long now = System.currentTimeMillis();
if (consensus == null || consensus.annotations == null
- || consensus.annotations.length < width)
+ || consensus.annotations.length < endCol)
{
/*
* called with a bad alignment annotation row
return;
}
- final int dp = getPercentageDp(nseq);
-
- for (int i = iStart; i < width; i++)
+ for (int i = startCol; i < endCol; i++)
{
- Profile profile;
- if (i >= profiles.length || ((profile = profiles[i]) == null))
+ ProfileI profile = profiles.get(i);
+ if (profile == null)
{
/*
* happens if sequences calculated over were
* shorter than alignment width
*/
consensus.annotations[i] = null;
- continue;
+ return;
}
+ final int dp = getPercentageDp(nseq);
+
float value = profile.getPercentageIdentity(ignoreGaps);
String description = getTooltip(profile, value, showSequenceLogo,
ignoreGaps, dp);
- consensus.annotations[i] = new Annotation(profile.getModalResidue(),
- description, ' ', value);
+ String modalResidue = profile.getModalResidue();
+ if ("".equals(modalResidue))
+ {
+ modalResidue = "-";
+ }
+ else if (modalResidue.length() > 1)
+ {
+ modalResidue = "+";
+ }
+ consensus.annotations[i] = new Annotation(modalResidue, description,
+ ' ', value);
}
// long elapsed = System.currentTimeMillis() - now;
// System.out.println(-elapsed);
}
/**
+ * Derive the information annotations to be added to the alignment for
+ * display. This does not recompute the raw data, but may be called on a
+ * change in display options, such as 'ignore below background frequency',
+ * which may in turn result in a change in the derived values.
+ *
+ * @param information
+ * the annotation row to add annotations to
+ * @param profiles
+ * the source information data
+ * @param startCol
+ * start column (inclusive)
+ * @param endCol
+ * end column (exclusive)
+ * @param ignoreGaps
+ * if true, normalise residue percentages
+ * @param showSequenceLogo
+ * if true include all information symbols, else just show modal
+ * residue
+ */
+ public static float completeInformation(AlignmentAnnotation information,
+ ProfilesI profiles, int startCol, int endCol)
+ {
+ // long now = System.currentTimeMillis();
+ if (information == null || information.annotations == null)
+ {
+ /*
+ * called with a bad alignment annotation row
+ * wait for it to be initialised properly
+ */
+ return 0;
+ }
+
+ float max = 0f;
+ SequenceI hmmSeq = information.sequenceRef;
+
+ int seqLength = hmmSeq.getLength();
+ if (information.annotations.length < seqLength)
+ {
+ return 0;
+ }
+
+ HiddenMarkovModel hmm = hmmSeq.getHMM();
+
+ for (int column = startCol; column < endCol; column++)
+ {
+ if (column >= seqLength)
+ {
+ // hmm consensus sequence is shorter than the alignment
+ break;
+ }
+
+ float value = hmm.getInformationContent(column);
+ boolean isNaN = Float.isNaN(value);
+ if (!isNaN)
+ {
+ max = Math.max(max, 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;
+ return max;
+ }
+
+ /**
+ * Derive the occupancy count annotation
+ *
+ * @param occupancy
+ * the annotation row to add annotations to
+ * @param profiles
+ * the source consensus data
+ * @param startCol
+ * start column (inclusive)
+ * @param endCol
+ * end column (exclusive)
+ */
+ public static void completeGapAnnot(AlignmentAnnotation occupancy,
+ ProfilesI profiles, int startCol, int endCol, long nseq)
+ {
+ if (occupancy == null || occupancy.annotations == null
+ || occupancy.annotations.length < endCol)
+ {
+ /*
+ * called with a bad alignment annotation row
+ * wait for it to be initialised properly
+ */
+ return;
+ }
+ // always set ranges again
+ occupancy.graphMax = nseq;
+ occupancy.graphMin = 0;
+ double scale = 0.8 / nseq;
+ for (int i = startCol; i < endCol; i++)
+ {
+ ProfileI profile = profiles.get(i);
+ if (profile == null)
+ {
+ /*
+ * happens if sequences calculated over were
+ * shorter than alignment width
+ */
+ occupancy.annotations[i] = null;
+ return;
+ }
+
+ final int gapped = profile.getNonGapped();
+
+ String description = "" + gapped;
+
+ occupancy.annotations[i] = new Annotation("", description, '\0',
+ gapped,
+ jalview.util.ColorUtils.bleachColour(Color.DARK_GRAY,
+ (float) scale * gapped));
+ }
+ }
+
+ /**
* Returns a tooltip showing either
* <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.
* the number of decimal places to format percentages to
* @return
*/
- static String getTooltip(Profile profile, float pid,
+ static String getTooltip(ProfileI profile, float pid,
boolean showSequenceLogo, boolean ignoreGaps, int dp)
{
ResidueCount counts = profile.getCounts();
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
String maxRes = profile.getModalResidue();
if (maxRes.length() > 1)
{
- sb.append("[").append(maxRes).append("] ");
- maxRes = "+";
+ sb.append("[").append(maxRes).append("]");
}
else
{
- sb.append(maxRes).append(" ");
+ sb.append(maxRes);
+ }
+ if (maxRes.length() > 0)
+ {
+ sb.append(" ");
+ Format.appendPercentage(sb, pid, dp);
+ sb.append("%");
}
- Format.appendPercentage(sb, pid, dp);
- sb.append("%");
description = sb.toString();
}
return description;
* contains
*
* <pre>
- * [profileType, numberOfValues, nonGapCount, charValue1, percentage1, charValue2, percentage2, ...]
+ * [profileType, numberOfValues, totalPercent, charValue1, percentage1, charValue2, percentage2, ...]
* in descending order of percentage value
* </pre>
*
* calculations
* @return
*/
- public static int[] extractProfile(Profile profile,
- boolean ignoreGaps)
+ 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;
}
+
/**
* Extract a sorted extract of cDNA codon profile data. The returned array
* 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<String, int[]>();
+ 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);
}
return scale;
}
+
+ /**
+ * Returns the sorted HMM profile for the given column of the alignment. The
+ * returned array contains
+ *
+ * <pre>
+ * [profileType=0, numberOfValues, 100, charValue1, percentage1, charValue2, percentage2, ...]
+ * in descending order of percentage value
+ * </pre>
+ *
+ * @param hmm
+ * @param column
+ * @param removeBelowBackground
+ * 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 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;
+
+ for (int i = 0; i < size; i++)
+ {
+ char symbol = alphabet.charAt(i);
+ symbols[i] = symbol;
+ int value = getAnalogueCount(hmm, column, symbol,
+ removeBelowBackground, infoHeight);
+ values[i] = value;
+ totalCount += value;
+ }
+
+ /*
+ * sort symbols by increasing emission probability
+ */
+ QuickSort.sort(values, symbols);
+
+ int[] profile = new int[3 + size * 2];
+
+ profile[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
+ profile[1] = size;
+ profile[2] = 100;
+
+ /*
+ * order symbol/count profile by decreasing emission probability
+ */
+ if (totalCount != 0)
+ {
+ 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
+ {
+ percentage = value / 100f;
+ }
+ int intPercent = Math.round(percentage);
+ profile[arrayPos] = symbols[k];
+ profile[arrayPos + 1] = intPercent;
+ arrayPos += 2;
+ }
+ }
+ return profile;
+ }
+
+ /**
+ * 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 = hmm.getMatchEmissionProbability(column, symbol);
+ double freq = ResidueProperties.backgroundFrequencies
+ .get(hmm.getAlphabetType()).get(symbol);
+ if (value < freq && removeBelowBackground)
+ {
+ return 0;
+ }
+
+ if (infoHeight)
+ {
+ value = value * (Math.log(value / freq) / LOG2);
+ }
+
+ value = value * 10000d;
+ return Math.round((float) value);
+ }
}
git://source.jalview.org / jalview.git / blobdiff