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Coverage Report

  1. Project Clover database Wed Nov 13 2024 18:27:33 GMT
  2. Package jalview.analysis

File AAFrequency.java

 

Coverage histogram

../../img/srcFileCovDistChart8.png
20% of files have more coverage

Code metrics

146
310
18
1
957
607
109
0.35
17.22
18
6.06

Classes

Class Line # Actions
AAFrequency 57 310 109
0.748945274.9%
 

Contributing tests

This file is covered by 191 tests. .

Source view

1    /*
2    * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
3    * Copyright (C) $$Year-Rel$$ The Jalview Authors
4    *
5    * This file is part of Jalview.
6    *
7    * Jalview is free software: you can redistribute it and/or
8    * modify it under the terms of the GNU General Public License
9    * as published by the Free Software Foundation, either version 3
10    * of the License, or (at your option) any later version.
11    *
12    * Jalview is distributed in the hope that it will be useful, but
13    * WITHOUT ANY WARRANTY; without even the implied warranty
14    * of MERCHANTABILITY or FITNESS FOR A PARTICULAR
15    * PURPOSE. See the GNU General Public License for more details.
16    *
17    * You should have received a copy of the GNU General Public License
18    * along with Jalview. If not, see <http://www.gnu.org/licenses/>.
19    * The Jalview Authors are detailed in the 'AUTHORS' file.
20    */
21    package jalview.analysis;
22   
23    import jalview.datamodel.AlignedCodonFrame;
24    import jalview.datamodel.AlignmentAnnotation;
25    import jalview.datamodel.AlignmentI;
26    import jalview.datamodel.Annotation;
27    import jalview.datamodel.Profile;
28    import jalview.datamodel.ProfileI;
29    import jalview.datamodel.Profiles;
30    import jalview.datamodel.ProfilesI;
31    import jalview.datamodel.ResidueCount;
32    import jalview.datamodel.ResidueCount.SymbolCounts;
33    import jalview.datamodel.SecondaryStructureCount;
34    import jalview.datamodel.SeqCigar;
35    import jalview.datamodel.SequenceI;
36    import jalview.ext.android.SparseIntArray;
37    import jalview.util.Comparison;
38    import jalview.util.Constants;
39    import jalview.util.Format;
40    import jalview.util.MappingUtils;
41    import jalview.util.QuickSort;
42   
43    import java.awt.Color;
44    import java.util.Arrays;
45    import java.util.Hashtable;
46    import java.util.List;
47   
48    /**
49    * Takes in a vector or array of sequences and column start and column end and
50    * returns a new Hashtable[] of size maxSeqLength, if Hashtable not supplied.
51    * This class is used extensively in calculating alignment colourschemes that
52    * depend on the amount of conservation in each alignment column.
53    *
54    * @author $author$
55    * @version $Revision$
56    */
 
57    public class AAFrequency
58    {
59    public static final String PROFILE = "P";
60   
61    /*
62    * Quick look-up of String value of char 'A' to 'Z'
63    */
64    private static final String[] CHARS = new String['Z' - 'A' + 1];
65   
 
66  50 toggle static
67    {
68  1350 for (char c = 'A'; c <= 'Z'; c++)
69    {
70  1300 CHARS[c - 'A'] = String.valueOf(c);
71    }
72    }
73   
 
74  3 toggle public static final ProfilesI calculate(List<SequenceI> list, int start,
75    int end)
76    {
77  3 return calculate(list, start, end, false);
78    }
79   
 
80  384 toggle public static final ProfilesI calculate(List<SequenceI> sequences,
81    int start, int end, boolean profile)
82    {
83  384 SequenceI[] seqs = new SequenceI[sequences.size()];
84  384 int width = 0;
85  384 synchronized (sequences)
86    {
87  3233 for (int i = 0; i < sequences.size(); i++)
88    {
89  2849 seqs[i] = sequences.get(i);
90  2849 int length = seqs[i].getLength();
91  2849 if (length > width)
92    {
93  383 width = length;
94    }
95    }
96   
97  384 if (end >= width)
98    {
99  213 end = width;
100    }
101   
102  384 ProfilesI reply = calculate(seqs, width, start, end, profile);
103  384 return reply;
104    }
105    }
106   
107    /**
108    * Calculate the consensus symbol(s) for each column in the given range.
109    *
110    * @param sequences
111    * @param width
112    * the full width of the alignment
113    * @param start
114    * start column (inclusive, base zero)
115    * @param end
116    * end column (exclusive)
117    * @param saveFullProfile
118    * if true, store all symbol counts
119    */
 
120  1365 toggle public static final ProfilesI calculate(final SequenceI[] sequences,
121    int width, int start, int end, boolean saveFullProfile)
122    {
123    // long now = System.currentTimeMillis();
124  1365 int seqCount = sequences.length;
125  1365 boolean nucleotide = false;
126  1365 int nucleotideCount = 0;
127  1365 int peptideCount = 0;
128   
129  1365 ProfileI[] result = new ProfileI[width];
130   
131  613829 for (int column = start; column < end; column++)
132    {
133    /*
134    * Apply a heuristic to detect nucleotide data (which can
135    * be counted in more compact arrays); here we test for
136    * more than 90% nucleotide; recheck every 10 columns in case
137    * of misleading data e.g. highly conserved Alanine in peptide!
138    * Mistakenly guessing nucleotide has a small performance cost,
139    * as it will result in counting in sparse arrays.
140    * Mistakenly guessing peptide has a small space cost,
141    * as it will use a larger than necessary array to hold counts.
142    */
143  612470 if (nucleotideCount > 100 && column % 10 == 0)
144    {
145  55011 nucleotide = (9 * peptideCount < nucleotideCount);
146    }
147  612478 ResidueCount residueCounts = new ResidueCount(nucleotide);
148   
149  11336630 for (int row = 0; row < seqCount; row++)
150    {
151  10736609 if (sequences[row] == null)
152    {
153  0 jalview.bin.Console.errPrintln(
154    "WARNING: Consensus skipping null sequence - possible race condition.");
155  0 continue;
156    }
157  10708677 if (sequences[row].getLength() > column)
158    {
159  10679071 char c = sequences[row].getCharAt(column);
160  10677780 residueCounts.add(c);
161  10718833 if (Comparison.isNucleotide(c))
162    {
163  975183 nucleotideCount++;
164    }
165  9741168 else if (!Comparison.isGap(c))
166    {
167  894502 peptideCount++;
168    }
169    }
170    else
171    {
172    /*
173    * count a gap if the sequence doesn't reach this column
174    */
175  30133 residueCounts.addGap();
176    }
177    }
178   
179  612397 int maxCount = residueCounts.getModalCount();
180  612371 String maxResidue = residueCounts.getResiduesForCount(maxCount);
181  612459 int gapCount = residueCounts.getGapCount();
182  612447 ProfileI profile = new Profile(seqCount, gapCount, maxCount,
183    maxResidue);
184   
185  612486 if (saveFullProfile)
186    {
187  594147 profile.setCounts(residueCounts);
188    }
189   
190  612456 result[column] = profile;
191    }
192  1365 return new Profiles(seqCount, result);
193    // long elapsed = System.currentTimeMillis() - now;
194    // jalview.bin.Console.outPrintln(elapsed);
195    }
196   
 
197  0 toggle public static final ProfilesI calculateSS(List<SequenceI> list, int start,
198    int end)
199    {
200  0 return calculateSS(list, start, end, false);
201    }
202   
 
203  381 toggle public static final ProfilesI calculateSS(List<SequenceI> sequences,
204    int start, int end, boolean profile)
205    {
206  381 SequenceI[] seqs = new SequenceI[sequences.size()];
207  381 int width = 0;
208  381 synchronized (sequences)
209    {
210  3227 for (int i = 0; i < sequences.size(); i++)
211    {
212  2846 seqs[i] = sequences.get(i);
213  2846 int length = seqs[i].getLength();
214  2846 if (length > width)
215    {
216  380 width = length;
217    }
218    }
219   
220  381 if (end >= width)
221    {
222  213 end = width;
223    }
224   
225  381 ProfilesI reply = calculateSS(seqs, width, start, end, profile);
226  381 return reply;
227    }
228    }
229   
 
230  381 toggle public static final ProfilesI calculateSS(final SequenceI[] sequences,
231    int width, int start, int end, boolean saveFullProfile)
232    {
233   
234  381 int seqCount = sequences.length;
235   
236  381 ProfileI[] result = new ProfileI[width];
237  381 int maxSSannotcount=0;
238  40439 for (int column = start; column < end; column++)
239    {
240   
241  40058 int ssCount = 0;
242   
243  40058 SecondaryStructureCount ssCounts = new SecondaryStructureCount();
244   
245  281450 for (int row = 0; row < seqCount; row++)
246    {
247  241392 if (sequences[row] == null)
248    {
249  0 jalview.bin.Console.errPrintln(
250    "WARNING: Consensus skipping null sequence - possible race condition.");
251  0 continue;
252    }
253   
254  241392 char c = sequences[row].getCharAt(column);
255  241392 AlignmentAnnotation aa = AlignmentUtils
256    .getDisplayedAlignmentAnnotation(sequences[row]);
257  241392 if (aa != null)
258    {
259  0 ssCount++;
260    }
261   
262  241392 if (sequences[row].getLength() > column && !Comparison.isGap(c)
263    && aa != null)
264    {
265   
266  0 int seqPosition = sequences[row].findPosition(column);
267   
268  0 char ss = AlignmentUtils.findSSAnnotationForGivenSeqposition(aa,
269    seqPosition);
270  0 if (ss == '*')
271    {
272  0 continue;
273    }
274  0 ssCounts.add(ss);
275    }
276  241392 else if (Comparison.isGap(c) && aa != null)
277    {
278  0 ssCounts.addGap();
279    }
280    }
281   
282  40058 int maxSSCount = ssCounts.getModalCount();
283  40058 String maxSS = ssCounts.getSSForCount(maxSSCount);
284  40058 int gapCount = ssCounts.getGapCount();
285  40058 ProfileI profile = new Profile(maxSS, ssCount, gapCount, maxSSCount);
286   
287  40058 if (saveFullProfile)
288    {
289  21744 profile.setSSCounts(ssCounts);
290    }
291   
292  40058 result[column] = profile;
293  40058 maxSSannotcount=Math.max(maxSSannotcount, ssCount);
294    }
295  381 return new Profiles(maxSSannotcount,result);
296    }
297   
298    /**
299    * Make an estimate of the profile size we are going to compute i.e. how many
300    * different characters may be present in it. Overestimating has a cost of
301    * using more memory than necessary. Underestimating has a cost of needing to
302    * extend the SparseIntArray holding the profile counts.
303    *
304    * @param profileSizes
305    * counts of sizes of profiles so far encountered
306    * @return
307    */
 
308  0 toggle static int estimateProfileSize(SparseIntArray profileSizes)
309    {
310  0 if (profileSizes.size() == 0)
311    {
312  0 return 4;
313    }
314   
315    /*
316    * could do a statistical heuristic here e.g. 75%ile
317    * for now just return the largest value
318    */
319  0 return profileSizes.keyAt(profileSizes.size() - 1);
320    }
321   
322    /**
323    * Derive the consensus annotations to be added to the alignment for display.
324    * This does not recompute the raw data, but may be called on a change in
325    * display options, such as 'ignore gaps', which may in turn result in a
326    * change in the derived values.
327    *
328    * @param consensus
329    * the annotation row to add annotations to
330    * @param profiles
331    * the source consensus data
332    * @param startCol
333    * start column (inclusive)
334    * @param endCol
335    * end column (exclusive)
336    * @param ignoreGaps
337    * if true, normalise residue percentages ignoring gaps
338    * @param showSequenceLogo
339    * if true include all consensus symbols, else just show modal
340    * residue
341    * @param nseq
342    * number of sequences
343    */
 
344  1114 toggle public static void completeConsensus(AlignmentAnnotation consensus,
345    ProfilesI profiles, int startCol, int endCol, boolean ignoreGaps,
346    boolean showSequenceLogo, long nseq)
347    {
348    // long now = System.currentTimeMillis();
349  1114 if (consensus == null || consensus.annotations == null
350    || consensus.annotations.length < endCol)
351    {
352    /*
353    * called with a bad alignment annotation row
354    * wait for it to be initialised properly
355    */
356  0 return;
357    }
358   
359  183813 for (int i = startCol; i < endCol; i++)
360    {
361  182699 ProfileI profile = profiles.get(i);
362  182702 if (profile == null)
363    {
364    /*
365    * happens if sequences calculated over were
366    * shorter than alignment width
367    */
368  0 consensus.annotations[i] = null;
369  0 return;
370    }
371   
372  182702 final int dp = getPercentageDp(nseq);
373   
374  182701 float value = profile.getPercentageIdentity(ignoreGaps);
375   
376  182703 String description = getTooltip(profile, value, showSequenceLogo,
377    ignoreGaps, dp);
378   
379  182698 String modalResidue = profile.getModalResidue();
380  182697 if ("".equals(modalResidue))
381    {
382  6203 modalResidue = "-";
383    }
384  176498 else if (modalResidue.length() > 1)
385    {
386  7887 modalResidue = "+";
387    }
388  182699 consensus.annotations[i] = new Annotation(modalResidue, description,
389    ' ', value);
390    }
391    // long elapsed = System.currentTimeMillis() - now;
392    // jalview.bin.Console.outPrintln(-elapsed);
393    }
394   
 
395  0 toggle public static void completeSSConsensus(AlignmentAnnotation ssConsensus,
396    ProfilesI profiles, int startCol, int endCol, boolean ignoreGaps,
397    boolean showSequenceLogo, long nseq)
398    {
399    // long now = System.currentTimeMillis();
400  0 if (ssConsensus == null || ssConsensus.annotations == null
401    || ssConsensus.annotations.length < endCol)
402    {
403    /*
404    * called with a bad alignment annotation row
405    * wait for it to be initialised properly
406    */
407  0 return;
408    }
409   
410  0 for (int i = startCol; i < endCol; i++)
411    {
412  0 ProfileI profile = profiles.get(i);
413  0 if (profile == null)
414    {
415    /*
416    * happens if sequences calculated over were
417    * shorter than alignment width
418    */
419  0 ssConsensus.annotations[i] = null;
420  0 return;
421    }
422   
423  0 final int dp = getPercentageDp(nseq);
424   
425  0 float value = profile.getSSPercentageIdentity(ignoreGaps);
426   
427  0 String description = getSSTooltip(profile, value, showSequenceLogo,
428    ignoreGaps, dp);
429   
430  0 String modalSS = profile.getModalSS();
431  0 if ("".equals(modalSS))
432    {
433  0 modalSS = "-";
434    }
435  0 else if (modalSS.length() > 1)
436    {
437  0 modalSS = "+";
438    }
439  0 ssConsensus.annotations[i] = new Annotation(modalSS, description, ' ',
440    value);
441    }
442    // long elapsed = System.currentTimeMillis() - now;
443    // jalview.bin.Console.outPrintln(-elapsed);
444    }
445   
446    /**
447    * Derive the gap count annotation row.
448    *
449    * @param gaprow
450    * the annotation row to add annotations to
451    * @param profiles
452    * the source consensus data
453    * @param startCol
454    * start column (inclusive)
455    * @param endCol
456    * end column (exclusive)
457    */
 
458  986 toggle public static void completeGapAnnot(AlignmentAnnotation gaprow,
459    ProfilesI profiles, int startCol, int endCol, long nseq)
460    {
461  986 if (gaprow == null || gaprow.annotations == null
462    || gaprow.annotations.length < endCol)
463    {
464    /*
465    * called with a bad alignment annotation row
466    * wait for it to be initialised properly
467    */
468  0 return;
469    }
470    // always set ranges again
471  986 gaprow.graphMax = nseq;
472  986 gaprow.graphMin = 0;
473  986 double scale = 0.8 / nseq;
474  163898 for (int i = startCol; i < endCol; i++)
475    {
476  162912 ProfileI profile = profiles.get(i);
477  162912 if (profile == null)
478    {
479    /*
480    * happens if sequences calculated over were
481    * shorter than alignment width
482    */
483  0 gaprow.annotations[i] = null;
484  0 return;
485    }
486   
487  162912 final int gapped = profile.getNonGapped();
488   
489  162912 String description = "" + gapped;
490   
491  162912 gaprow.annotations[i] = new Annotation("", description, '\0', gapped,
492    jalview.util.ColorUtils.bleachColour(Color.DARK_GRAY,
493    (float) scale * gapped));
494    }
495    }
496   
497    /**
498    * Returns a tooltip showing either
499    * <ul>
500    * <li>the full profile (percentages of all residues present), if
501    * showSequenceLogo is true, or</li>
502    * <li>just the modal (most common) residue(s), if showSequenceLogo is
503    * false</li>
504    * </ul>
505    * Percentages are as a fraction of all sequence, or only ungapped sequences
506    * if ignoreGaps is true.
507    *
508    * @param profile
509    * @param pid
510    * @param showSequenceLogo
511    * @param ignoreGaps
512    * @param dp
513    * the number of decimal places to format percentages to
514    * @return
515    */
 
516  182701 toggle static String getTooltip(ProfileI profile, float pid,
517    boolean showSequenceLogo, boolean ignoreGaps, int dp)
518    {
519  182704 ResidueCount counts = profile.getCounts();
520   
521  182704 String description = null;
522  182704 if (counts != null && showSequenceLogo)
523    {
524  64157 int normaliseBy = ignoreGaps ? profile.getNonGapped()
525    : profile.getHeight();
526  64157 description = counts.getTooltip(normaliseBy, dp);
527    }
528    else
529    {
530  118547 StringBuilder sb = new StringBuilder(64);
531  118547 String maxRes = profile.getModalResidue();
532  118547 if (maxRes.length() > 1)
533    {
534  2930 sb.append("[").append(maxRes).append("]");
535    }
536    else
537    {
538  115617 sb.append(maxRes);
539    }
540  118547 if (maxRes.length() > 0)
541    {
542  115497 sb.append(" ");
543  115497 Format.appendPercentage(sb, pid, dp);
544  115497 sb.append("%");
545    }
546  118547 description = sb.toString();
547    }
548  182698 return description;
549    }
550   
 
551  0 toggle static String getSSTooltip(ProfileI profile, float pid,
552    boolean showSequenceLogo, boolean ignoreGaps, int dp)
553    {
554  0 SecondaryStructureCount counts = profile.getSSCounts();
555   
556  0 String description = null;
557  0 if (counts != null && showSequenceLogo)
558    {
559  0 int normaliseBy = ignoreGaps ? profile.getNonGapped()
560    : profile.getHeight();
561  0 description = counts.getTooltip(normaliseBy, dp);
562    }
563    else
564    {
565  0 StringBuilder sb = new StringBuilder(64);
566  0 String maxSS = profile.getModalSS();
567  0 if (maxSS.length() > 1)
568    {
569  0 sb.append("[").append(maxSS).append("]");
570    }
571    else
572    {
573  0 sb.append(maxSS);
574    }
575  0 if (maxSS.length() > 0)
576    {
577  0 sb.append(" ");
578  0 Format.appendPercentage(sb, pid, dp);
579  0 sb.append("%");
580    }
581  0 description = sb.toString();
582    }
583  0 return description;
584    }
585   
586    /**
587    * Returns the sorted profile for the given consensus data. The returned array
588    * contains
589    *
590    * <pre>
591    * [profileType, numberOfValues, totalPercent, charValue1, percentage1, charValue2, percentage2, ...]
592    * in descending order of percentage value
593    * </pre>
594    *
595    * @param profile
596    * the data object from which to extract and sort values
597    * @param ignoreGaps
598    * if true, only non-gapped values are included in percentage
599    * calculations
600    * @return
601    */
 
602  101848 toggle public static int[] extractProfile(ProfileI profile, boolean ignoreGaps)
603    {
604  101848 char[] symbols;
605  101848 int[] values;
606   
607  101848 if (profile.getCounts() != null)
608    {
609  101848 ResidueCount counts = profile.getCounts();
610  101848 SymbolCounts symbolCounts = counts.getSymbolCounts();
611  101848 symbols = symbolCounts.symbols;
612  101848 values = symbolCounts.values;
613   
614    }
615  0 else if (profile.getSSCounts() != null)
616    {
617  0 SecondaryStructureCount counts = profile.getSSCounts();
618    // to do
619  0 SecondaryStructureCount.SymbolCounts symbolCounts = counts
620    .getSymbolCounts();
621  0 symbols = symbolCounts.symbols;
622  0 values = symbolCounts.values;
623    }
624    else
625    {
626  0 return null;
627    }
628   
629  101848 QuickSort.sort(values, symbols);
630  101848 int totalPercentage = 0;
631  101848 final int divisor = ignoreGaps ? profile.getNonGapped()
632    : profile.getHeight();
633   
634    /*
635    * traverse the arrays in reverse order (highest counts first)
636    */
637  101848 int[] result = new int[3 + 2 * symbols.length];
638  101848 int nextArrayPos = 3;
639  101848 int nonZeroCount = 0;
640   
641  295945 for (int i = symbols.length - 1; i >= 0; i--)
642    {
643  194099 int theChar = symbols[i];
644  194099 int charCount = values[i];
645  194099 final int percentage = (charCount * 100) / divisor;
646  194099 if (percentage == 0)
647    {
648    /*
649    * this count (and any remaining) round down to 0% - discard
650    */
651  2 break;
652    }
653  194097 nonZeroCount++;
654  194097 result[nextArrayPos++] = theChar;
655  194097 result[nextArrayPos++] = percentage;
656  194097 totalPercentage += percentage;
657    }
658   
659    /*
660    * truncate array if any zero values were discarded
661    */
662  101848 if (nonZeroCount < symbols.length)
663    {
664  2 int[] tmp = new int[3 + 2 * nonZeroCount];
665  2 System.arraycopy(result, 0, tmp, 0, tmp.length);
666  2 result = tmp;
667    }
668   
669    /*
670    * fill in 'header' values
671    */
672  101848 result[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
673  101848 result[1] = nonZeroCount;
674  101848 result[2] = totalPercentage;
675   
676  101848 return result;
677    }
678   
679    /**
680    * Extract a sorted extract of cDNA codon profile data. The returned array
681    * contains
682    *
683    * <pre>
684    * [profileType, numberOfValues, totalPercentage, charValue1, percentage1, charValue2, percentage2, ...]
685    * in descending order of percentage value, where the character values encode codon triplets
686    * </pre>
687    *
688    * @param hashtable
689    * @return
690    */
 
691  2 toggle public static int[] extractCdnaProfile(
692    Hashtable<String, Object> hashtable, boolean ignoreGaps)
693    {
694    // this holds #seqs, #ungapped, and then codon count, indexed by encoded
695    // codon triplet
696  2 int[] codonCounts = (int[]) hashtable.get(PROFILE);
697  2 int[] sortedCounts = new int[codonCounts.length - 2];
698  2 System.arraycopy(codonCounts, 2, sortedCounts, 0,
699    codonCounts.length - 2);
700   
701  2 int[] result = new int[3 + 2 * sortedCounts.length];
702    // first value is just the type of profile data
703  2 result[0] = AlignmentAnnotation.CDNA_PROFILE;
704   
705  2 char[] codons = new char[sortedCounts.length];
706  130 for (int i = 0; i < codons.length; i++)
707    {
708  128 codons[i] = (char) i;
709    }
710  2 QuickSort.sort(sortedCounts, codons);
711  2 int totalPercentage = 0;
712  2 int distinctValuesCount = 0;
713  2 int j = 3;
714  2 int divisor = ignoreGaps ? codonCounts[1] : codonCounts[0];
715  8 for (int i = codons.length - 1; i >= 0; i--)
716    {
717  8 final int codonCount = sortedCounts[i];
718  8 if (codonCount == 0)
719    {
720  0 break; // nothing else of interest here
721    }
722  8 final int percentage = codonCount * 100 / divisor;
723  8 if (percentage == 0)
724    {
725    /*
726    * this (and any remaining) values rounded down to 0 - discard
727    */
728  2 break;
729    }
730  6 distinctValuesCount++;
731  6 result[j++] = codons[i];
732  6 result[j++] = percentage;
733  6 totalPercentage += percentage;
734    }
735  2 result[2] = totalPercentage;
736   
737    /*
738    * Just return the non-zero values
739    */
740    // todo next value is redundant if we limit the array to non-zero counts
741  2 result[1] = distinctValuesCount;
742  2 return Arrays.copyOfRange(result, 0, j);
743    }
744   
745    /**
746    * Compute a consensus for the cDNA coding for a protein alignment.
747    *
748    * @param alignment
749    * the protein alignment (which should hold mappings to cDNA
750    * sequences)
751    * @param hconsensus
752    * the consensus data stores to be populated (one per column)
753    */
 
754  4 toggle public static void calculateCdna(AlignmentI alignment,
755    Hashtable<String, Object>[] hconsensus)
756    {
757  4 final char gapCharacter = alignment.getGapCharacter();
758  4 List<AlignedCodonFrame> mappings = alignment.getCodonFrames();
759  4 if (mappings == null || mappings.isEmpty())
760    {
761  0 return;
762    }
763   
764  4 int cols = alignment.getWidth();
765  1928 for (int col = 0; col < cols; col++)
766    {
767    // todo would prefer a Java bean for consensus data
768  1924 Hashtable<String, Object> columnHash = new Hashtable<>();
769    // #seqs, #ungapped seqs, counts indexed by (codon encoded + 1)
770  1924 int[] codonCounts = new int[66];
771  1924 codonCounts[0] = alignment.getSequences().size();
772  1924 int ungappedCount = 0;
773  1924 for (SequenceI seq : alignment.getSequences())
774    {
775  20870 if (seq.getCharAt(col) == gapCharacter)
776    {
777  10166 continue;
778    }
779  10704 List<char[]> codons = MappingUtils.findCodonsFor(seq, col,
780    mappings);
781  10704 for (char[] codon : codons)
782    {
783  10657 int codonEncoded = CodingUtils.encodeCodon(codon);
784  10657 if (codonEncoded >= 0)
785    {
786  10657 codonCounts[codonEncoded + 2]++;
787  10657 ungappedCount++;
788  10657 break;
789    }
790    }
791    }
792  1924 codonCounts[1] = ungappedCount;
793    // todo: sort values here, save counts and codons?
794  1924 columnHash.put(PROFILE, codonCounts);
795  1924 hconsensus[col] = columnHash;
796    }
797    }
798   
799    /**
800    * Derive displayable cDNA consensus annotation from computed consensus data.
801    *
802    * @param consensusAnnotation
803    * the annotation row to be populated for display
804    * @param consensusData
805    * the computed consensus data
806    * @param showProfileLogo
807    * if true show all symbols present at each position, else only the
808    * modal value
809    * @param nseqs
810    * the number of sequences in the alignment
811    */
 
812  4 toggle public static void completeCdnaConsensus(
813    AlignmentAnnotation consensusAnnotation,
814    Hashtable<String, Object>[] consensusData,
815    boolean showProfileLogo, int nseqs)
816    {
817  4 if (consensusAnnotation == null
818    || consensusAnnotation.annotations == null
819    || consensusAnnotation.annotations.length < consensusData.length)
820    {
821    // called with a bad alignment annotation row - wait for it to be
822    // initialised properly
823  0 return;
824    }
825   
826    // ensure codon triplet scales with font size
827  4 consensusAnnotation.scaleColLabel = true;
828  1928 for (int col = 0; col < consensusData.length; col++)
829    {
830  1924 Hashtable<String, Object> hci = consensusData[col];
831  1924 if (hci == null)
832    {
833    // gapped protein column?
834  0 continue;
835    }
836    // array holds #seqs, #ungapped, then codon counts indexed by codon
837  1924 final int[] codonCounts = (int[]) hci.get(PROFILE);
838  1924 int totalCount = 0;
839   
840    /*
841    * First pass - get total count and find the highest
842    */
843  1924 final char[] codons = new char[codonCounts.length - 2];
844  125060 for (int j = 2; j < codonCounts.length; j++)
845    {
846  123136 final int codonCount = codonCounts[j];
847  123136 codons[j - 2] = (char) (j - 2);
848  123136 totalCount += codonCount;
849    }
850   
851    /*
852    * Sort array of encoded codons by count ascending - so the modal value
853    * goes to the end; start by copying the count (dropping the first value)
854    */
855  1924 int[] sortedCodonCounts = new int[codonCounts.length - 2];
856  1924 System.arraycopy(codonCounts, 2, sortedCodonCounts, 0,
857    codonCounts.length - 2);
858  1924 QuickSort.sort(sortedCodonCounts, codons);
859   
860  1924 int modalCodonEncoded = codons[codons.length - 1];
861  1924 int modalCodonCount = sortedCodonCounts[codons.length - 1];
862  1924 String modalCodon = String
863    .valueOf(CodingUtils.decodeCodon(modalCodonEncoded));
864  1924 if (sortedCodonCounts.length > 1 && sortedCodonCounts[codons.length
865    - 2] == sortedCodonCounts[codons.length - 1])
866    {
867    /*
868    * two or more codons share the modal count
869    */
870  25 modalCodon = "+";
871    }
872  1924 float pid = sortedCodonCounts[sortedCodonCounts.length - 1] * 100
873    / (float) totalCount;
874   
875    /*
876    * todo ? Replace consensus hashtable with sorted arrays of codons and
877    * counts (non-zero only). Include total count in count array [0].
878    */
879   
880    /*
881    * Scan sorted array backwards for most frequent values first. Show
882    * repeated values compactly.
883    */
884  1924 StringBuilder mouseOver = new StringBuilder(32);
885  1924 StringBuilder samePercent = new StringBuilder();
886  1924 String percent = null;
887  1924 String lastPercent = null;
888  1924 int percentDecPl = getPercentageDp(nseqs);
889   
890  3823 for (int j = codons.length - 1; j >= 0; j--)
891    {
892  3823 int codonCount = sortedCodonCounts[j];
893  3823 if (codonCount == 0)
894    {
895    /*
896    * remaining codons are 0% - ignore, but finish off the last one if
897    * necessary
898    */
899  1924 if (samePercent.length() > 0)
900    {
901  1899 mouseOver.append(samePercent).append(": ").append(percent)
902    .append("% ");
903    }
904  1924 break;
905    }
906  1899 int codonEncoded = codons[j];
907  1899 final int pct = codonCount * 100 / totalCount;
908  1899 String codon = String
909    .valueOf(CodingUtils.decodeCodon(codonEncoded));
910  1899 StringBuilder sb = new StringBuilder();
911  1899 Format.appendPercentage(sb, pct, percentDecPl);
912  1899 percent = sb.toString();
913  1899 if (showProfileLogo || codonCount == modalCodonCount)
914    {
915  1899 if (percent.equals(lastPercent) && j > 0)
916    {
917  0 samePercent.append(samePercent.length() == 0 ? "" : ", ");
918  0 samePercent.append(codon);
919    }
920    else
921    {
922  1899 if (samePercent.length() > 0)
923    {
924  0 mouseOver.append(samePercent).append(": ").append(lastPercent)
925    .append("% ");
926    }
927  1899 samePercent.setLength(0);
928  1899 samePercent.append(codon);
929    }
930  1899 lastPercent = percent;
931    }
932    }
933   
934  1924 consensusAnnotation.annotations[col] = new Annotation(modalCodon,
935    mouseOver.toString(), ' ', pid);
936    }
937    }
938   
939    /**
940    * Returns the number of decimal places to show for profile percentages. For
941    * less than 100 sequences, returns zero (the integer percentage value will be
942    * displayed). For 100-999 sequences, returns 1, for 1000-9999 returns 2, etc.
943    *
944    * @param nseq
945    * @return
946    */
 
947  184625 toggle protected static int getPercentageDp(long nseq)
948    {
949  184625 int scale = 0;
950  184626 while (nseq >= 100)
951    {
952  0 scale++;
953  0 nseq /= 10;
954    }
955  184626 return scale;
956    }
957    }
  • Report generated by OpenClover v 4.4.1 on Wed Nov 13 2024 18:35:04 GMT using coverage data from Wed Nov 13 2024 18:34:45 GMT.
  • OpenClover is free and open-source software.