comparison cluster.tools/select.k.from.consensus.cluster.R @ 9:a3c03541fe6f draft default tip

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author peter-waltman
date Mon, 11 Mar 2013 17:30:48 -0400
parents a58527c632b7
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8:a58527c632b7 9:a3c03541fe6f
195 k.select <- as.numeric( names( k.select ) ) 195 k.select <- as.numeric( names( k.select ) )
196 ## find the search range 196 ## find the search range
197 k.search.range <- (k.select-2):(k.select+2) 197 k.search.range <- (k.select-2):(k.select+2)
198 k.search.range <- k.search.range[ k.search.range %in% as.numeric( names( vector.of.metric.changes ) ) ] 198 k.search.range <- k.search.range[ k.search.range %in% as.numeric( names( vector.of.metric.changes ) ) ]
199 k.search.range <- vector.of.metric.changes[ as.character( k.search.range ) ] 199 k.search.range <- vector.of.metric.changes[ as.character( k.search.range ) ]
200 k.search.range <- k.search.range[ k.search.range < 0.25 ] 200
201 k.search.range <- k.search.range[ k.search.range > 0.025 ] 201 if ( sum( k.search.range < 0.25 ) == 0 ) {
202 ## this should only happen if this is a garbage clustering
203 k.search.range <- k.search.range[ which.min( k.search.range ) ]
204 } else {
205 k.search.range <- k.search.range[ k.search.range < 0.25 ]
206 }
207
208 if ( sum( k.search.range > 0.025 ) == 0 ) {
209 k.search.range <- k.search.range[ which.max( k.search.range ) ]
210 } else {
211 k.search.range <- k.search.range[ k.search.range > 0.025 ]
212 }
202 k.search.range <- names( k.search.range ) 213 k.search.range <- names( k.search.range )
203
204 214
205 if ( exists("data") ) { 215 if ( exists("data") ) {
206 ## what direction is the clustering in? rows or cols? 216 ## what direction is the clustering in? rows or cols?
207 elts <- unique( names( results[[2]]$consensusClass ) ) 217 elts <- unique( names( results[[2]]$consensusClass ) )
208 if ( all( elts %in% colnames( data ) ) ) { 218 if ( all( elts %in% colnames( data ) ) ) {