-/*\r
- * Jalview - A Sequence Alignment Editor and Viewer\r
- * Copyright (C) 2007 AM Waterhouse, J Procter, G Barton, M Clamp, S Searle\r
- *\r
- * This program is free software; you can redistribute it and/or\r
- * modify it under the terms of the GNU General Public License\r
- * as published by the Free Software Foundation; either version 2\r
- * of the License, or (at your option) any later version.\r
- *\r
- * This program is distributed in the hope that it will be useful,\r
- * but WITHOUT ANY WARRANTY; without even the implied warranty of\r
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r
- * GNU General Public License for more details.\r
- *\r
- * You should have received a copy of the GNU General Public License\r
- * along with this program; if not, write to the Free Software\r
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA\r
- */\r
-package jalview.analysis;\r
-\r
-import java.util.*;\r
-\r
-import jalview.datamodel.*;\r
-\r
-/**\r
- * Takes in a vector or array of sequences and column start and column end\r
- * and returns a new Hashtable[] of size maxSeqLength, if Hashtable not supplied.\r
- * This class is used extensively in calculating alignment colourschemes\r
- * that depend on the amount of conservation in each alignment column.\r
- * @author $author$\r
- * @version $Revision$\r
- */\r
-public class AAFrequency\r
-{\r
- //No need to store 1000s of strings which are not\r
- //visible to the user.\r
- public static final String MAXCOUNT = "C";\r
- public static final String MAXRESIDUE = "R";\r
- public static final String PID_GAPS = "G";\r
- public static final String PID_NOGAPS = "N";\r
-\r
- public static final Hashtable[] calculate(Vector sequences, int start,\r
- int end)\r
- {\r
- SequenceI[] seqs = new SequenceI[sequences.size()];\r
- int width = 0;\r
- for (int i = 0; i < sequences.size(); i++)\r
- {\r
- seqs[i] = (SequenceI) sequences.elementAt(i);\r
- if (seqs[i].getLength() > width)\r
- {\r
- width = seqs[i].getLength();\r
- }\r
- }\r
-\r
- Hashtable[] reply = new Hashtable[width];\r
-\r
- if (end >= width)\r
- {\r
- end = width;\r
- }\r
-\r
- calculate(seqs, start, end, reply);\r
-\r
- return reply;\r
- }\r
-\r
- public static final void calculate(SequenceI[] sequences,\r
- int start, int end,\r
- Hashtable[] result)\r
- {\r
- Hashtable residueHash;\r
- int maxCount, nongap, i, j, v, jSize = sequences.length;\r
- String maxResidue;\r
- char c;\r
- float percentage;\r
-\r
- int[] values = new int[255];\r
-\r
- char[] seq;\r
-\r
- for (i = start; i < end; i++)\r
- {\r
- residueHash = new Hashtable();\r
- maxCount = 0;\r
- maxResidue = "";\r
- nongap = 0;\r
- values = new int[255];\r
-\r
- for (j = 0; j < jSize; j++)\r
- {\r
- seq = sequences[j].getSequence();\r
- if (seq.length > i)\r
- {\r
- c = seq[i];\r
-\r
- if (c == '.' || c == ' ')\r
- {\r
- c = '-';\r
- }\r
-\r
- if (c == '-')\r
- {\r
- values['-']++;\r
- continue;\r
- }\r
- else if ('a' <= c && c <= 'z')\r
- {\r
- c -= 32; //('a' - 'A');\r
- }\r
-\r
- nongap++;\r
- values[c]++;\r
-\r
- }\r
- else\r
- {\r
- values['-']++;\r
- }\r
- }\r
-\r
- for (v = 'A'; v < 'Z'; v++)\r
- {\r
- if (values[v] < 2 || values[v] < maxCount)\r
- {\r
- continue;\r
- }\r
-\r
- if (values[v] > maxCount)\r
- {\r
- maxResidue = String.valueOf( (char) v);\r
- }\r
- else if (values[v] == maxCount)\r
- {\r
- maxResidue += String.valueOf( (char) v);\r
- }\r
- maxCount = values[v];\r
- }\r
-\r
- if (maxResidue.length() == 0)\r
- {\r
- maxResidue = "-";\r
- }\r
-\r
- residueHash.put(MAXCOUNT, new Integer(maxCount));\r
- residueHash.put(MAXRESIDUE, maxResidue);\r
-\r
- percentage = ( (float) maxCount * 100) / (float) jSize;\r
- residueHash.put(PID_GAPS, new Float(percentage));\r
-\r
- percentage = ( (float) maxCount * 100) / (float) nongap;\r
- residueHash.put(PID_NOGAPS, new Float(percentage));\r
- result[i] = residueHash;\r
- }\r
- }\r
-}\r
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ The Jalview Authors
+ *
+ * This file is part of Jalview.
+ *
+ * Jalview is free software: you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License
+ * as published by the Free Software Foundation, either version 3
+ * of the License, or (at your option) any later version.
+ *
+ * Jalview is distributed in the hope that it will be useful, but
+ * WITHOUT ANY WARRANTY; without even the implied warranty
+ * of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ * PURPOSE. See the GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with Jalview. If not, see <http://www.gnu.org/licenses/>.
+ * The Jalview Authors are detailed in the 'AUTHORS' file.
+ */
+package jalview.analysis;
+
+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;
+
+/**
+ * Takes in a vector or array of sequences and column start and column end and
+ * returns a new Hashtable[] of size maxSeqLength, if Hashtable not supplied.
+ * This class is used extensively in calculating alignment colourschemes that
+ * depend on the amount of conservation in each alignment column.
+ *
+ */
+public class AAFrequency
+{
+ private static final double LOG2 = Math.log(2);
+
+ public static final String PROFILE = "P";
+
+ public static final ProfilesI calculate(List<SequenceI> list, int start,
+ int end)
+ {
+ return calculate(list, start, end, false);
+ }
+
+ public static final ProfilesI calculate(List<SequenceI> sequences,
+ int start, int end, boolean profile)
+ {
+ SequenceI[] seqs = new SequenceI[sequences.size()];
+ int width = 0;
+ synchronized (sequences)
+ {
+ for (int i = 0; i < sequences.size(); i++)
+ {
+ seqs[i] = sequences.get(i);
+ int length = seqs[i].getLength();
+ if (length > width)
+ {
+ width = length;
+ }
+ }
+
+ if (end >= width)
+ {
+ end = width;
+ }
+
+ 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 saveFullProfile
+ * if true, store all symbol counts
+ */
+ public static final ProfilesI calculate(final SequenceI[] sequences,
+ int width, int start, int end, boolean saveFullProfile)
+ {
+ // long now = System.currentTimeMillis();
+ int seqCount = sequences.length;
+ boolean nucleotide = false;
+ int nucleotideCount = 0;
+ int peptideCount = 0;
+
+ ProfileI[] result = new ProfileI[width];
+
+ for (int column = start; column < end; column++)
+ {
+ /*
+ * Apply a heuristic to detect nucleotide data (which can
+ * be counted in more compact arrays); here we test for
+ * more than 90% nucleotide; recheck every 10 columns in case
+ * of misleading data e.g. highly conserved Alanine in peptide!
+ * Mistakenly guessing nucleotide has a small performance cost,
+ * as it will result in counting in sparse arrays.
+ * Mistakenly guessing peptide has a small space cost,
+ * as it will use a larger than necessary array to hold counts.
+ */
+ if (nucleotideCount > 100 && column % 10 == 0)
+ {
+ nucleotide = (9 * peptideCount < nucleotideCount);
+ }
+ ResidueCount residueCounts = new ResidueCount(nucleotide);
+
+ for (int row = 0; row < seqCount; row++)
+ {
+ if (sequences[row] == null)
+ {
+ System.err.println(
+ "WARNING: Consensus skipping null sequence - possible race condition.");
+ continue;
+ }
+ if (sequences[row].getLength() > column)
+ {
+ char c = sequences[row].getCharAt(column);
+ residueCounts.add(c);
+ if (Comparison.isNucleotide(c))
+ {
+ nucleotideCount++;
+ }
+ else if (!Comparison.isGap(c))
+ {
+ peptideCount++;
+ }
+ }
+ else
+ {
+ /*
+ * count a gap if the sequence doesn't reach this column
+ */
+ residueCounts.addGap();
+ }
+ }
+
+ int maxCount = residueCounts.getModalCount();
+ String maxResidue = residueCounts.getResiduesForCount(maxCount);
+ int gapCount = residueCounts.getGapCount();
+ ProfileI profile = new Profile(seqCount, gapCount, maxCount,
+ maxResidue);
+
+ if (saveFullProfile)
+ {
+ profile.setCounts(residueCounts);
+ }
+
+ 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
+ * extend the SparseIntArray holding the profile counts.
+ *
+ * @param profileSizes
+ * counts of sizes of profiles so far encountered
+ * @return
+ */
+ static int estimateProfileSize(SparseIntArray profileSizes)
+ {
+ if (profileSizes.size() == 0)
+ {
+ return 4;
+ }
+
+ /*
+ * could do a statistical heuristic here e.g. 75%ile
+ * for now just return the largest value
+ */
+ return profileSizes.keyAt(profileSizes.size() - 1);
+ }
+
+ /**
+ * Derive the consensus 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 gaps', which may in turn result in a
+ * change in the derived values.
+ *
+ * @param consensus
+ * the annotation row to add annotations to
+ * @param profiles
+ * the source consensus data
+ * @param startCol
+ * start column (inclusive)
+ * @param endCol
+ * end column (exclusive)
+ * @param ignoreGaps
+ * if true, normalise residue percentages ignoring gaps
+ * @param showSequenceLogo
+ * if true include all consensus symbols, else just show modal
+ * residue
+ * @param nseq
+ * number of sequences
+ */
+ public static void completeConsensus(AlignmentAnnotation consensus,
+ 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 < endCol)
+ {
+ /*
+ * called with a bad alignment annotation row
+ * wait for it to be initialised properly
+ */
+ return;
+ }
+
+ 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
+ */
+ consensus.annotations[i] = null;
+ return;
+ }
+
+ final int dp = getPercentageDp(nseq);
+
+ float value = profile.getPercentageIdentity(ignoreGaps);
+
+ String description = getTooltip(profile, value, showSequenceLogo,
+ ignoreGaps, dp);
+
+ 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>
+ * </ul>
+ * Percentages are as a fraction of all sequence, or only ungapped sequences
+ * if ignoreGaps is true.
+ *
+ * @param profile
+ * @param pid
+ * @param showSequenceLogo
+ * @param ignoreGaps
+ * @param dp
+ * the number of decimal places to format percentages to
+ * @return
+ */
+ 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();
+ description = counts.getTooltip(normaliseBy, dp);
+ }
+ else
+ {
+ StringBuilder sb = new StringBuilder(64);
+ String maxRes = profile.getModalResidue();
+ if (maxRes.length() > 1)
+ {
+ sb.append("[").append(maxRes).append("]");
+ }
+ else
+ {
+ sb.append(maxRes);
+ }
+ if (maxRes.length() > 0)
+ {
+ sb.append(" ");
+ Format.appendPercentage(sb, pid, dp);
+ sb.append("%");
+ }
+ description = sb.toString();
+ }
+ return description;
+ }
+
+ /**
+ * Returns the sorted profile for the given consensus data. The returned array
+ * contains
+ *
+ * <pre>
+ * [profileType, numberOfValues, nonGapCount, charValue1, percentage1, charValue2, percentage2, ...]
+ * in descending order of percentage value
+ * </pre>
+ *
+ * @param profile
+ * the data object from which to extract and sort values
+ * @param ignoreGaps
+ * if true, only non-gapped values are included in percentage
+ * calculations
+ * @return
+ */
+ public static int[] extractProfile(ProfileI profile, boolean ignoreGaps)
+ {
+ int[] rtnval = new int[64];
+ ResidueCount counts = profile.getCounts();
+ if (counts == null)
+ {
+ return null;
+ }
+
+ SymbolCounts symbolCounts = counts.getSymbolCounts();
+ 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();
+
+ /*
+ * traverse the arrays in reverse order (highest counts first)
+ */
+ 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;
+ totalPercentage += percentage;
+ }
+ rtnval[0] = symbols.length;
+ rtnval[1] = totalPercentage;
+ int[] result = new int[rtnval.length + 1];
+ result[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
+ System.arraycopy(rtnval, 0, result, 1, rtnval.length);
+
+ return result;
+ }
+
+
+ /**
+ * Extract a sorted extract of cDNA codon profile data. The returned array
+ * contains
+ *
+ * <pre>
+ * [profileType, numberOfValues, totalCount, 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)
+ {
+ // this holds #seqs, #ungapped, and then codon count, indexed by encoded
+ // codon triplet
+ int[] codonCounts = (int[]) hashtable.get(PROFILE);
+ int[] sortedCounts = new int[codonCounts.length - 2];
+ System.arraycopy(codonCounts, 2, sortedCounts, 0,
+ codonCounts.length - 2);
+
+ int[] result = new int[3 + 2 * sortedCounts.length];
+ // first value is just the type of profile data
+ result[0] = AlignmentAnnotation.CDNA_PROFILE;
+
+ char[] codons = new char[sortedCounts.length];
+ for (int i = 0; i < codons.length; i++)
+ {
+ codons[i] = (char) i;
+ }
+ QuickSort.sort(sortedCounts, codons);
+ int totalPercentage = 0;
+ int distinctValuesCount = 0;
+ int j = 3;
+ int divisor = ignoreGaps ? codonCounts[1] : codonCounts[0];
+ for (int i = codons.length - 1; i >= 0; i--)
+ {
+ final int codonCount = sortedCounts[i];
+ if (codonCount == 0)
+ {
+ break; // nothing else of interest here
+ }
+ distinctValuesCount++;
+ result[j++] = codons[i];
+ final int percentage = codonCount * 100 / divisor;
+ result[j++] = percentage;
+ totalPercentage += percentage;
+ }
+ result[2] = totalPercentage;
+
+ /*
+ * Just return the non-zero values
+ */
+ // todo next value is redundant if we limit the array to non-zero counts
+ result[1] = distinctValuesCount;
+ return Arrays.copyOfRange(result, 0, j);
+ }
+
+ /**
+ * Compute a consensus for the cDNA coding for a protein alignment.
+ *
+ * @param alignment
+ * the protein alignment (which should hold mappings to cDNA
+ * sequences)
+ * @param hconsensus
+ * the consensus data stores to be populated (one per column)
+ */
+ public static void calculateCdna(AlignmentI alignment,
+ Hashtable[] hconsensus)
+ {
+ final char gapCharacter = alignment.getGapCharacter();
+ List<AlignedCodonFrame> mappings = alignment.getCodonFrames();
+ if (mappings == null || mappings.isEmpty())
+ {
+ return;
+ }
+
+ int cols = alignment.getWidth();
+ for (int col = 0; col < cols; col++)
+ {
+ // todo would prefer a Java bean for consensus data
+ Hashtable<String, int[]> columnHash = new Hashtable<>();
+ // #seqs, #ungapped seqs, counts indexed by (codon encoded + 1)
+ int[] codonCounts = new int[66];
+ codonCounts[0] = alignment.getSequences().size();
+ int ungappedCount = 0;
+ for (SequenceI seq : alignment.getSequences())
+ {
+ if (seq.getCharAt(col) == gapCharacter)
+ {
+ continue;
+ }
+ List<char[]> codons = MappingUtils.findCodonsFor(seq, col,
+ mappings);
+ for (char[] codon : codons)
+ {
+ int codonEncoded = CodingUtils.encodeCodon(codon);
+ if (codonEncoded >= 0)
+ {
+ codonCounts[codonEncoded + 2]++;
+ ungappedCount++;
+ }
+ }
+ }
+ codonCounts[1] = ungappedCount;
+ // todo: sort values here, save counts and codons?
+ columnHash.put(PROFILE, codonCounts);
+ hconsensus[col] = columnHash;
+ }
+ }
+
+ /**
+ * Derive displayable cDNA consensus annotation from computed consensus data.
+ *
+ * @param consensusAnnotation
+ * the annotation row to be populated for display
+ * @param consensusData
+ * the computed consensus data
+ * @param showProfileLogo
+ * if true show all symbols present at each position, else only the
+ * modal value
+ * @param nseqs
+ * the number of sequences in the alignment
+ */
+ public static void completeCdnaConsensus(
+ AlignmentAnnotation consensusAnnotation,
+ Hashtable[] consensusData, boolean showProfileLogo, int nseqs)
+ {
+ if (consensusAnnotation == null
+ || consensusAnnotation.annotations == null
+ || consensusAnnotation.annotations.length < consensusData.length)
+ {
+ // called with a bad alignment annotation row - wait for it to be
+ // initialised properly
+ return;
+ }
+
+ // ensure codon triplet scales with font size
+ consensusAnnotation.scaleColLabel = true;
+ for (int col = 0; col < consensusData.length; col++)
+ {
+ Hashtable hci = consensusData[col];
+ if (hci == null)
+ {
+ // gapped protein column?
+ continue;
+ }
+ // array holds #seqs, #ungapped, then codon counts indexed by codon
+ final int[] codonCounts = (int[]) hci.get(PROFILE);
+ int totalCount = 0;
+
+ /*
+ * First pass - get total count and find the highest
+ */
+ final char[] codons = new char[codonCounts.length - 2];
+ for (int j = 2; j < codonCounts.length; j++)
+ {
+ final int codonCount = codonCounts[j];
+ codons[j - 2] = (char) (j - 2);
+ totalCount += codonCount;
+ }
+
+ /*
+ * Sort array of encoded codons by count ascending - so the modal value
+ * goes to the end; start by copying the count (dropping the first value)
+ */
+ int[] sortedCodonCounts = new int[codonCounts.length - 2];
+ System.arraycopy(codonCounts, 2, sortedCodonCounts, 0,
+ codonCounts.length - 2);
+ QuickSort.sort(sortedCodonCounts, codons);
+
+ 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])
+ {
+ /*
+ * two or more codons share the modal count
+ */
+ modalCodon = "+";
+ }
+ float pid = sortedCodonCounts[sortedCodonCounts.length - 1] * 100
+ / (float) totalCount;
+
+ /*
+ * todo ? Replace consensus hashtable with sorted arrays of codons and
+ * counts (non-zero only). Include total count in count array [0].
+ */
+
+ /*
+ * Scan sorted array backwards for most frequent values first. Show
+ * repeated values compactly.
+ */
+ StringBuilder mouseOver = new StringBuilder(32);
+ StringBuilder samePercent = new StringBuilder();
+ String percent = null;
+ String lastPercent = null;
+ int percentDecPl = getPercentageDp(nseqs);
+
+ for (int j = codons.length - 1; j >= 0; j--)
+ {
+ int codonCount = sortedCodonCounts[j];
+ if (codonCount == 0)
+ {
+ /*
+ * remaining codons are 0% - ignore, but finish off the last one if
+ * necessary
+ */
+ if (samePercent.length() > 0)
+ {
+ mouseOver.append(samePercent).append(": ").append(percent)
+ .append("% ");
+ }
+ break;
+ }
+ int codonEncoded = codons[j];
+ final int pct = codonCount * 100 / totalCount;
+ String codon = String
+ .valueOf(CodingUtils.decodeCodon(codonEncoded));
+ StringBuilder sb = new StringBuilder();
+ Format.appendPercentage(sb, pct, percentDecPl);
+ percent = sb.toString();
+ if (showProfileLogo || codonCount == modalCodonCount)
+ {
+ if (percent.equals(lastPercent) && j > 0)
+ {
+ samePercent.append(samePercent.length() == 0 ? "" : ", ");
+ samePercent.append(codon);
+ }
+ else
+ {
+ if (samePercent.length() > 0)
+ {
+ mouseOver.append(samePercent).append(": ").append(lastPercent)
+ .append("% ");
+ }
+ samePercent.setLength(0);
+ samePercent.append(codon);
+ }
+ lastPercent = percent;
+ }
+ }
+
+ consensusAnnotation.annotations[col] = new Annotation(modalCodon,
+ mouseOver.toString(), ' ', pid);
+ }
+ }
+
+ /**
+ * Returns the number of decimal places to show for profile percentages. For
+ * less than 100 sequences, returns zero (the integer percentage value will be
+ * displayed). For 100-999 sequences, returns 1, for 1000-9999 returns 2, etc.
+ *
+ * @param nseq
+ * @return
+ */
+ protected static int getPercentageDp(long nseq)
+ {
+ int scale = 0;
+ while (nseq >= 100)
+ {
+ scale++;
+ nseq /= 10;
+ }
+ 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