4898794edd81be5285ea6e544acbedeaeb31bf78 max Tue Nov 23 08:10:57 2021 -0800 Fixing pointers to README file for license in all source code files. refs #27614 diff --git src/hg/txCds/txCdsSvmInput/txCdsSvmInput.c src/hg/txCds/txCdsSvmInput/txCdsSvmInput.c index 394f21f..dc325c1 100644 --- src/hg/txCds/txCdsSvmInput/txCdsSvmInput.c +++ src/hg/txCds/txCdsSvmInput/txCdsSvmInput.c @@ -1,336 +1,336 @@ /* txCdsSvmInput - Create input for svm_light, a nice support vector machine classifier.. */ /* Copyright (C) 2011 The Regents of the University of California - * See README in this or parent directory for licensing information. */ + * See kent/LICENSE or http://genome.ucsc.edu/license/ for licensing information. */ #include "common.h" #include "linefile.h" #include "hash.h" #include "options.h" #include "localmem.h" #include "cdsEvidence.h" #include "cdsOrtho.h" #include "txInfo.h" boolean good = FALSE; boolean bad = FALSE; void usage() /* Explain usage and exit. */ { errAbort( "txCdsSvmInput - Create input for svm_light, a nice support vector machine classifier.\n" "usage:\n" " txCdsSvmInput in.bed in.tce in.ortho in.info out.lst out.vector\n" "options:\n" " -good - consider this the positive training set\n" " -bad - consider this the negative training set\n" ); } static struct optionSpec options[] = { {"good", OPTION_BOOLEAN}, {"bad", OPTION_BOOLEAN}, {NULL, 0}, }; int cdsEvidenceCmp(const void *va, const void *vb) /* Compare to sort based on name,start,end. */ { const struct cdsEvidence *a = *((struct cdsEvidence **)va); const struct cdsEvidence *b = *((struct cdsEvidence **)vb); int dif = strcmp(a->name, b->name); if (dif == 0) { dif = a->start - b->start; if (dif == 0) dif = a->end - b->end; } return dif; } char *orthoHashKey(char *name, int start, int end) /* Make key for orthoHash. */ { static char buf[256]; safef(buf, sizeof(buf), "%s_%X_%X", name, start, end); return buf; } boolean mostlyTrue() /* Return TRUE 3/4 of the time. */ { static int i; ++i; return (i&3) != 0; } struct idVal /* An id (index into feature vector) and it's value */ { struct idVal *next; int id; double val; }; int idValCmp(const void *va, const void *vb) /* Compare to sort based on id. */ { const struct idVal *a = *((struct idVal **)va); const struct idVal *b = *((struct idVal **)vb); int diff = a->id - b->id; if (diff == 0) { double valDiff = b->val - a->val; if (valDiff < 0) diff = -1; else if (valDiff > 0) diff = 1; } return diff; } void idValAdd(struct lm *lm, struct idVal **pList, int id, double val) /* Make up new idVal and add it to head of list. */ { struct idVal *iv; AllocVar(iv); iv->id = id; iv->val = val; slAddHead(pList, iv); } struct idVal *skipDupes(struct idVal *list) /* Remove from list all but first occurence of id, assuming that * list is sorted by ID. */ { struct idVal *newList = NULL, *iv, *nextIv; int lastId = -1; for (iv = list; iv != NULL; iv = nextIv) { nextIv = iv->next; if (iv->id != lastId) { slAddHead(&newList, iv); lastId = iv->id; } } slReverse(&newList); return newList; } void outEvidence(struct lm *lm, int featureId, struct cdsEvidence *cds, struct idVal **pList) /* Output evidence specific to one cdsEvidence */ { idValAdd(lm, pList, featureId, 1); idValAdd(lm, pList, featureId+1, cds->score); idValAdd(lm, pList, featureId+2, cds->cdsCount); } void outOrtho(struct lm *lm, int featureId, struct cdsOrtho *ortho, int orfSize, struct idVal **pList) /* Output features associated with cdsOrtho. */ { idValAdd(lm, pList, featureId, (double)ortho->missing/orfSize); idValAdd(lm, pList, featureId+1, ortho->ratio); idValAdd(lm, pList, featureId+2, ortho->ratio * (1.0 - (double)ortho->missing/orfSize)); idValAdd(lm, pList, featureId+3, ortho->orthoSize); idValAdd(lm, pList, featureId+4, ortho->ratio * orfSize); } void outputOneCds(struct cdsEvidence *start, struct cdsEvidence *end, struct bed *bed, struct txInfo *info, struct hashEl *orthoEl, FILE *f) /* Assemble together all of the evidence and write it as line to svm_light input file. * This line is of the format: * train id:val id:val ... id:val # transcript:start-end * where train is +1 for positive training examples, -1 for negative training * examples and 0 if not training. The id's are integers representing the type of * data. The val are floating point numbers. */ { struct lm *lm = lmInit(0); boolean outputAll = (!good || !mostlyTrue()); struct idVal *iv, *list = NULL; /* Deal with training status. */ if (good) fprintf(f, "+1 "); else if (bad) fprintf(f, "-1 "); else fprintf(f, "0 "); /* Output features we always have: txSize, orfSize, start, distance from end, start/stop complete. */ int txSize = bedTotalBlockSize(bed); int orfSize = start->end - start->start; fprintf(f, "1:%d ", txSize); fprintf(f, "2:%d ", orfSize); fprintf(f, "3:%d ", start->start); fprintf(f, "4:%d ", txSize - start->end); fprintf(f, "5:%f ", (double)orfSize/txSize); /* To avoid paying attention to things that are always true in training set, * only output cdsStart/cdsEnd sometimes. */ if (outputAll) { fprintf(f, "6:%d ", start->startComplete); fprintf(f, "7:%d ", start->endComplete); } /* Loop through all of the cdsEvidence we have, outputting features from it. */ struct cdsEvidence *cds; for (cds = start; cds != end; cds = cds->next) { char *source = cds->source; if (outputAll) { if (sameString(source, "RefSeqReviewed")) outEvidence(lm, 10, cds, &list); else if (sameString(source, "RefPepReviewed")) outEvidence(lm, 20, cds, &list); } if (sameString(source, "RefSeqValidated")) outEvidence(lm, 15, cds, &list); else if (sameString(source, "RefPepValidated")) outEvidence(lm, 25, cds, &list); else if (sameString(source, "swissProt")) outEvidence(lm, 30, cds, &list); else if (sameString(source, "RefSeqProvisional")) outEvidence(lm, 35, cds, &list); else if (sameString(source, "RefPepProvisional")) outEvidence(lm, 40, cds, &list); else if (sameString(source, "MGCfullLength")) outEvidence(lm, 45, cds, &list); else if (sameString(source, "MGCfullLengthSynthetic")) outEvidence(lm, 50, cds, &list); else if (sameString(source, "RefSeqPredicted")) outEvidence(lm, 55, cds, &list); else if (sameString(source, "RefPepPredicted")) outEvidence(lm, 60, cds, &list); else if (sameString(source, "RefSeqInferred")) outEvidence(lm, 65, cds, &list); else if (sameString(source, "RefPepInferred")) outEvidence(lm, 70, cds, &list); else if (sameString(source, "genbankCds")) outEvidence(lm, 75, cds, &list); else if (sameString(source, "trembl")) outEvidence(lm, 80, cds, &list); else if (sameString(source, "bestorf")) outEvidence(lm, 85, cds, &list); else warn("Unknown source %s\n", source); #ifdef SOON #endif /* SOON */ } /* Output orthology stuff. */ struct hashEl *el; for (el = orthoEl; el != NULL; el = hashLookupNext(el)) { struct cdsOrtho *ortho = el->val; char *db = ortho->species; if (startsWith("panTro", db)) outOrtho(lm, 100, ortho, orfSize, &list); else if (startsWith("rheMac", db)) outOrtho(lm, 105, ortho, orfSize, &list); else if (startsWith("mm", db)) outOrtho(lm, 110, ortho, orfSize, &list); else if (startsWith("canFam", db)) outOrtho(lm, 120, ortho, orfSize, &list); else if (startsWith("rn", db)) outOrtho(lm, 125, ortho, orfSize, &list); else if (startsWith("hg", db)) outOrtho(lm, 130, ortho, orfSize, &list); } /* Output some info stuff. */ idValAdd(lm, &list, 140, info->nonsenseMediatedDecay); idValAdd(lm, &list, 141, info->retainedIntron); idValAdd(lm, &list, 142, info->bleedIntoIntron); idValAdd(lm, &list, 143, info->strangeSplice); idValAdd(lm, &list, 144, info->cdsSingleInIntron); idValAdd(lm, &list, 145, info->cdsSingleInUtr3); /* Sort list, because svm_learn insists on it, and output. */ slSort(&list, idValCmp); list = skipDupes(list); for (iv = list; iv != NULL; iv = iv->next) fprintf(f, "%d:%f ", iv->id, iv->val); /* Output end bit. */ fprintf(f, "# %s:%d-%d\n", start->name, start->start, start->end); lmCleanup(&lm); } void txCdsSvmInput(char *inBed, char *inTce, char *inOrtho, char *inInfo, char *outList, char *outSvm) /* txCdsSvmInput - Create input for svm_light, a nice support vector machine classifier.. */ { /* Load up hash full of beds. */ struct bed *bed, *bedList = bedLoadNAll(inBed, 12); struct hash *bedHash = hashNew(18); for (bed = bedList; bed != NULL; bed = bed->next) hashAdd(bedHash, bed->name, bed); /* Load up hash full of info */ struct txInfo *info, *infoList = txInfoLoadAll(inInfo); struct hash *infoHash = hashNew(18); for (info = infoList; info != NULL; info = info->next) hashAdd(infoHash, info->name, info); /* Load orthology information and put in hash. */ struct cdsOrtho *ortho, *orthoList = cdsOrthoLoadAll(inOrtho); struct hash *orthoHash = hashNew(18); for (ortho = orthoList; ortho != NULL; ortho = ortho->next) hashAdd(orthoHash, orthoHashKey(ortho->name, ortho->start, ortho->end), ortho); /* Load in other evidence and sort. */ struct cdsEvidence *cdsList = cdsEvidenceLoadAll(inTce); slSort(&cdsList, cdsEvidenceCmp); /* Open output files. */ FILE *fList = mustOpen(outList, "w"); FILE *fSvm = mustOpen(outSvm, "w"); /* Stream through evidence, processing all the records on the same orf in the * same transcript at once. */ struct cdsEvidence *start, *end; for (start = cdsList; start != NULL; start = end) { for (end = start->next; end != NULL; end = end->next) { if (end == NULL) break; if (cdsEvidenceCmp(&start,&end) != 0) break; } fprintf(fList, "%s\t%d\t%d\n", start->name, start->start, start->end); struct hashEl *orthoEl = hashLookup(orthoHash, orthoHashKey(start->name, start->start, start->end)); if (orthoEl == NULL) errAbort("%s is in %s but not %s", start->name, inTce, inOrtho); bed = hashFindVal(bedHash, start->name); if (bed == NULL) errAbort("%s is in %s but not %s", start->name, inTce, inBed); info = hashFindVal(infoHash, start->name); if (info == NULL) errAbort("%s is in %s but not %s", start->name, inTce, inInfo); outputOneCds(start, end, bed, info, orthoEl, fSvm); } /* Clean up and go home. */ carefulClose(&fList); carefulClose(&fSvm); } int main(int argc, char *argv[]) /* Process command line. */ { optionInit(&argc, argv, options); if (argc != 7) usage(); bad = optionExists("bad"); good = optionExists("good"); if (good && bad) errAbort("The good and bad options are mutually exclusive."); txCdsSvmInput(argv[1], argv[2], argv[3], argv[4], argv[5], argv[6]); return 0; }