cluster.cpp

   1 /*
   2  *  cluster.cpp
   3  *
   4  *
   5  *  Created by Pat Schloss on 8/14/08.
   6  *  Copyright 2008 Patrick D. Schloss. All rights reserved.
   7  *
   8  */
   9
  10 #include "cluster.hpp"
  11 #include "rabundvector.hpp"
  12 #include "listvector.hpp"
  13 #include "sparsematrix.hpp"
  14
  15 /***********************************************************************/
  16
  17 Cluster::Cluster(RAbundVector* rav, ListVector* lv, SparseMatrix* dm, float c) :
  18 rabund(rav), list(lv), dMatrix(dm), cutoff(c)
  19 {
  20 /*
  21         cout << "sizeof(MatData): " << sizeof(MatData) << endl;
  22         cout << "sizeof(PCell*): " << sizeof(PCell*) << endl;
  23
  24         int nCells = dMatrix->getNNodes();
  25         time_t start = time(NULL);
  26
  27         MatVec matvec = MatVec(nCells);
  28         int i = 0;
  29         for (MatData currentCell = dMatrix->begin(); currentCell != dMatrix->end(); currentCell++) {
  30                 matvec[i++] = currentCell;
  31         }
  32         for (i= matvec.size();i>0;i--) {
  33                 dMatrix->rmCell(matvec[i-1]);
  34         }
  35         MatData it = dMatrix->begin();
  36         while (it != dMatrix->end()) {
  37                 it = dMatrix->rmCell(it);
  38         }
  39         cout << "Time to remove " << nCells << " cells: " << time(NULL) - start << " seconds" << endl;
  40     exit(0);
  41         MatData it = dMatrix->begin();
  42         cout << it->row << "/" << it->column << "/" << it->dist << endl;
  43         dMatrix->rmCell(dMatrix->begin());
  44         cout << it->row << "/" << it->column << "/" << it->dist << endl;
  45         exit(0);
  46 */
  47
  48         // Create a data structure to quickly access the PCell information
  49         // for a certain sequence. It consists of a vector of lists, where
  50         // a list contains pointers (iterators) to the all distances related
  51         // to a certain sequence. The Vector is accessed via the index of a
  52         // sequence in the distance matrix.
  53         seqVec = vector<MatVec>(lv->size());
  54         for (MatData currentCell = dMatrix->begin(); currentCell != dMatrix->end(); currentCell++) {
  55                 seqVec[currentCell->row].push_back(currentCell);
  56                 seqVec[currentCell->column].push_back(currentCell);
  57         }
  58         mapWanted = false;  //set to true by mgcluster to speed up overlap merge
  59 }
  60
  61 /***********************************************************************/
  62
  63 void Cluster::getRowColCells() {
  64         try {
  65                 PCell* smallCell = dMatrix->getSmallestCell();  //find the smallest cell - this routine should probably not be in the SpMat class
  66
  67                 smallRow = smallCell->row;              // get its row
  68                 smallCol = smallCell->column;   // get its column
  69                 smallDist = smallCell->dist;    // get the smallest distance
  70
  71                 rowCells = seqVec[smallRow];    // all distances related to the row index
  72                 colCells = seqVec[smallCol];    // all distances related to the column index
  73                 nRowCells = rowCells.size();
  74                 nColCells = colCells.size();
  75         }
  76         catch(exception& e) {
  77                 errorOut(e, "Cluster", "getRowColCells");
  78                 exit(1);
  79         }
  80
  81 }
  82 /***********************************************************************/
  83 // Remove the specified cell from the seqVec and from the sparse
  84 // matrix
  85 void Cluster::removeCell(const MatData& cell, int vrow, int vcol, bool rmMatrix)
  86 {
  87         ull drow = cell->row;
  88         ull dcol = cell->column;
  89         if (((vrow >=0) && (drow != smallRow)) ||
  90                 ((vcol >=0) && (dcol != smallCol))) {
  91                 ull dtemp = drow;
  92                 drow = dcol;
  93                 dcol = dtemp;
  94         }
  95
  96         ull crow;
  97         ull ccol;
  98         int nCells;
  99         if (vrow < 0) {
 100                 nCells = seqVec[drow].size();
 101                 for (vrow=0; vrow<nCells;vrow++) {
 102                         crow = seqVec[drow][vrow]->row;
 103                         ccol = seqVec[drow][vrow]->column;
 104                         if (((crow == drow) && (ccol == dcol)) ||
 105                                 ((ccol == drow) && (crow == dcol))) {
 106                                 break;
 107                         }
 108                 }
 109         }
 110         seqVec[drow].erase(seqVec[drow].begin()+vrow);
 111         if (vcol < 0) {
 112                 nCells = seqVec[dcol].size();
 113                 for (vcol=0; vcol<nCells;vcol++) {
 114                         crow = seqVec[dcol][vcol]->row;
 115                         ccol = seqVec[dcol][vcol]->column;
 116                         if (((crow == drow) && (ccol == dcol)) ||
 117                                 ((ccol == drow) && (crow == dcol))) {
 118                                 break;
 119                         }
 120                 }
 121         }
 122         seqVec[dcol].erase(seqVec[dcol].begin()+vcol);
 123         if (rmMatrix) {
 124                 dMatrix->rmCell(cell);
 125         }
 126 }
 127
 128
 129 /***********************************************************************/
 130
 131 void Cluster::clusterBins(){
 132         try {
 133         //      cout << smallCol << '\t' << smallRow << '\t' << smallDist << '\t' << rabund->get(smallRow) << '\t' << rabund->get(smallCol);
 134
 135                 rabund->set(smallCol, rabund->get(smallRow)+rabund->get(smallCol));
 136                 rabund->set(smallRow, 0);
 137                 rabund->setLabel(toString(smallDist));
 138
 139         //      cout << '\t' << rabund->get(smallRow) << '\t' << rabund->get(smallCol) << endl;
 140         }
 141         catch(exception& e) {
 142                 errorOut(e, "Cluster", "clusterBins");
 143                 exit(1);
 144         }
 145
 146
 147 }
 148
 149 /***********************************************************************/
 150
 151 void Cluster::clusterNames(){
 152         try {
 153         //      cout << smallCol << '\t' << smallRow << '\t' << smallDist << '\t' << list->get(smallRow) << '\t' << list->get(smallCol);
 154                 if (mapWanted) {  updateMap();  }
 155
 156                 list->set(smallCol, list->get(smallRow)+','+list->get(smallCol));
 157                 list->set(smallRow, "");
 158                 list->setLabel(toString(smallDist));
 159
 160         //      cout << '\t' << list->get(smallRow) << '\t' << list->get(smallCol) << endl;
 161     }
 162         catch(exception& e) {
 163                 errorOut(e, "Cluster", "clusterNames");
 164                 exit(1);
 165         }
 166
 167 }
 168
 169 /***********************************************************************/
 170 //This function clusters based on the method of the derived class
 171 //At the moment only average and complete linkage are covered, because
 172 //single linkage uses a different approach.
 173 void Cluster::update(){
 174         try {
 175                 getRowColCells();
 176
 177                 vector<int> found(nColCells, 0);
 178                 int search;
 179                 bool changed;
 180
 181                 // The vector has to be traversed in reverse order to preserve the index
 182                 // for faster removal in removeCell()
 183                 for (int i=nRowCells-1;i>=0;i--) {
 184                         if (!((rowCells[i]->row == smallRow) && (rowCells[i]->column == smallCol))) {
 185                                 if (rowCells[i]->row == smallRow) {
 186                                         search = rowCells[i]->column;
 187                                 } else {
 188                                         search = rowCells[i]->row;
 189                                 }
 190
 191                                 for (int j=0;j<nColCells;j++) {
 192                                         if (!((colCells[j]->row == smallRow) && (colCells[j]->column == smallCol))) {
 193                                                 if (colCells[j]->row == search || colCells[j]->column == search) {
 194                                                         found[j] = 1;
 195                                                         changed = updateDistance(colCells[j], rowCells[i]);
 196                                                         // If the cell's distance changed and it had the same distance as
 197                                                         // the smallest distance, invalidate the mins vector in SparseMatrix
 198                                                         if (changed) {
 199                                                                 if (colCells[j]->vectorMap != NULL) {
 200                                                                         *(colCells[j]->vectorMap) = NULL;
 201                                                                         colCells[j]->vectorMap = NULL;
 202                                                                 }
 203                                                         }
 204                                                         break;
 205                                                 }
 206                                         }
 207                                 }
 208                                 removeCell(rowCells[i], i , -1);
 209                         }
 210                 }
 211                 clusterBins();
 212                 clusterNames();
 213
 214                 // Special handling for singlelinkage case, not sure whether this
 215                 // could be avoided
 216                 for (int i=nColCells-1;i>=0;i--) {
 217                         if (found[i] == 0) {
 218                                 removeCell(colCells[i], -1, i);
 219                         }
 220                 }
 221         }
 222         catch(exception& e) {
 223                 errorOut(e, "Cluster", "update");
 224                 exit(1);
 225         }
 226 }
 227 /***********************************************************************/
 228 void Cluster::setMapWanted(bool m)  {
 229         try {
 230                 mapWanted = m;
 231
 232                 //initialize map
 233                 for (int i = 0; i < list->getNumBins(); i++) {
 234
 235                         //parse bin
 236                         string names = list->get(i);
 237                         while (names.find_first_of(',') != -1) {
 238                                 //get name from bin
 239                                 string name = names.substr(0,names.find_first_of(','));
 240                                 //save name and bin number
 241                                 seq2Bin[name] = i;
 242                                 names = names.substr(names.find_first_of(',')+1, names.length());
 243                         }
 244
 245                         //get last name
 246                         seq2Bin[names] = i;
 247                 }
 248
 249         }
 250         catch(exception& e) {
 251                 errorOut(e, "Cluster", "setMapWanted");
 252                 exit(1);
 253         }
 254 }
 255 /***********************************************************************/
 256 void Cluster::updateMap() {
 257 try {
 258                 //update location of seqs in smallRow since they move to smallCol now
 259                 string names = list->get(smallRow);
 260                 while (names.find_first_of(',') != -1) {
 261                         //get name from bin
 262                         string name = names.substr(0,names.find_first_of(','));
 263                         //save name and bin number
 264                         seq2Bin[name] = smallCol;
 265                         names = names.substr(names.find_first_of(',')+1, names.length());
 266                 }
 267
 268                 //get last name
 269                 seq2Bin[names] = smallCol;
 270
 271         }
 272         catch(exception& e) {
 273                 errorOut(e, "Cluster", "updateMap");
 274                 exit(1);
 275         }
 276 }
 277 /***********************************************************************/
 278
 279
 280