1. maigesPack
  2. R
  3. plot-methods.R
Browse code

Correcting a warning and some notes on R CMD check.

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/maigesPack@94775 bc3139a8-67e5-0310-9ffc-ced21a209358

Gustavo Esteves authored on 01/10/2014 14:34:31
Showing1 changed files
R/plot-methods.R
History View file @caf9db5
... ... 
@@ -457,7 +457,7 @@ plot.maigesActMod <- function(x, type=c("S", "C")[2], keepEmpty=FALSE, ...) {
457 457 
         idx1 <- order(rownames(table))
458 458 
         idx2 <- order.dendrogram(as.dendrogram(hclust(dist(t(table)))))
459 459
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-        heatmap(table[idx1, idx2], scale="none", col=maigesPack:::greenRed(),
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+        heatmap(table[idx1, idx2], scale="none", col=greenRed(),
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         zlim=limite, Rowv=NA, Colv=NA, ...)
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     }
... ... 
@@ -475,7 +475,7 @@ plot.maigesActMod <- function(x, type=c("S", "C")[2], keepEmpty=FALSE, ...) {
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         idx1 <- order(rownames(table))
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         idx2 <- order.dendrogram(as.dendrogram(hclust(dist(t(table)))))
477 477
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-        heatmap(table[idx1, idx2], scale="none", col=maigesPack:::greenRed(),
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+        heatmap(table[idx1, idx2], scale="none", col=greenRed(),
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         zlim=limite, Rowv=NA, Colv=NA, ...)
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     }
... ... 
@@ -496,7 +496,7 @@ plot.maigesActNet <- function(x, type=c("score","p-value")[1], ...) {
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         limite <- c(0, limite)
497 497 
         idx <- order.dendrogram(as.dendrogram(hclust(dist(t(x@scores)))))
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-        heatmap(x@scores[, idx], scale="none", col=maigesPack:::blackBlue(),
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+        heatmap(x@scores[, idx], scale="none", col=blackBlue(),
500 500 
         zlim=limite, Rowv=NA, Colv=NA, ...)
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     }
... ... 
@@ -508,7 +508,7 @@ plot.maigesActNet <- function(x, type=c("score","p-value")[1], ...) {
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         idx <- order.dendrogram(as.dendrogram(hclust(dist(t(x@Pvalues)))))
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         heatmap(-log10(x@Pvalues)[, idx], scale="none",
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-        col=maigesPack:::blackBlue(), zlim=limite, Rowv=NA, Colv=NA, ...)
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+        col=blackBlue(), zlim=limite, Rowv=NA, Colv=NA, ...)
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     }
514 514 
 }
Browse code

Correction of some bugs and inconvenient warnings

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/maigesPack@27800 bc3139a8-67e5-0310-9ffc-ced21a209358

Gustavo Esteves authored on 05/10/2007 23:59:01
Showing1 changed files
R/plot-methods.R
History View file @fba7a4a
... ... 
@@ -3,7 +3,6 @@
3 3 
 ## Gustavo H. Esteves
4 4 
 ## 27/05/07
5 5 
 ##
6 
-## Version: 1.1
7 6 
 ##
8 7
9 8
Browse code

Initial check-in

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/maigesPack@27611 bc3139a8-67e5-0310-9ffc-ced21a209358

Martin Morgan authored on 28/09/2007 19:23:59
Showing1 changed files
R/plot-methods.R
History View file @a9f5d02
1 1 
new file mode 100644
... ... 
@@ -0,0 +1,515 @@
1 
+## Define methods for plot generic function
2 
+##
3 
+## Gustavo H. Esteves
4 
+## 27/05/07
5 
+##
6 
+## Version: 1.1
7 
+##
8 
+
9 
+
10 
+## For maigesRaw class
11 
+plot.maigesRaw <- function(x, bkgSub="subtract", z=NULL, legend.func=NULL,
12 
+ylab="W", ...) {
13 
+
14 
+    ## Correcting background
15 
+    tmp <- backgroundCorrect(as(x, "RGList"), bkgSub)
16 
+    tmp <- as(tmp, "marrayRaw")
17 
+    tmp <- as(tmp, "marrayNorm")
18 
+
19 
+    ## indexing ref labelled with green
20 
+    idx <- tolower(getLabels(x, "Ref")) == "red"
21 
+    if(sum(idx) > 0)
22 
+        maM(tmp)[, idx] <- -maM(tmp)[, idx]
23 
+
24 
+    maLabels(maTargets(tmp)) <- as.character(maInfo(maTargets(tmp))[[1]])
25 
+    maPlot(tmp, z=z, legend.func=legend.func, ylab=ylab, ...)
26 
+}
27 
+
28 
+
29 
+## For maiges class
30 
+plot.maiges <- function(x, z=NULL, legend.func=NULL, ylab="W", ...) {
31 
+
32 
+    ## Converting to marrayNorm class (from marray package)
33 
+    tmp <- as(x, "marrayNorm")
34 
+
35 
+    ## indexing ref labelled with green
36 
+    idx <- tolower(getLabels(x, "Ref")) == "red"
37 
+    if(sum(idx) > 0)
38 
+        maM(tmp)[, idx] <- -maM(tmp)[, idx]
39 
+
40 
+    maPlot(tmp, z=z, legend.func=legend.func, ylab=ylab, ...)
41 
+
42 
+}
43 
+
44 
+
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+## For maigesANOVA class
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+plot.maigesANOVA <- plot.maiges
47 
+
48 
+
49 
+## For maigesDE class (shows volcano plot)
50 
+plot.maigesDE <- function(x, adjP="none", idx=1, ...) {
51 
+
52 
+    ## Adjusting p-values if specified by the user.
53 
+    if(adjP != "none") {
54 
+        tmp1 <- multtest::mt.rawp2adjp(x@p.value[, idx], proc=adjP)
55 
+        x@p.value[, idx] <- tmp1$adjp[order(tmp1$index), 2]
56 
+    }
57 
+
58 
+    if(sum(dim(x@fold)) == 0)
59 
+        stop("I can't plot volcano with fold slot empty.")
60 
+    else
61 
+        tmp1 <- x@fold[, idx]
62 
+
63 
+    tmp2 <- -log10(x@p.value[, idx])
64 
+    if(!is.null(colnames(x@stat)[idx]))
65 
+        plotName <- colnames(x@stat)[idx]
66 
+    else
67 
+        plotName <- x@test
68 
+
69 
+    plot(tmp1, tmp2, main=paste("Volcano plot -", plotName), xlab="log2(fold)",
70 
+    ylab=paste("-log10(P.value)"), ...)
71 
+
72 
+}
73 
+
74 
+
75 
+## For maigesDEcluster class
76 
+plot.maigesDEcluster <- function(x, adjP="none", idx=1, ...) {
77 
+
78 
+    ## Adjusting p-values if specified by the user.
79 
+    if(adjP != "none") {
80 
+        tmp1 <- multtest::mt.rawp2adjp(x@p.value[, idx], proc=adjP)
81 
+        x@p.value[, idx] <- tmp1$adjp[order(tmp1$index), 2]
82 
+    }
83 
+
84 
+    if(sum(dim(x@fold)) == 0)
85 
+        tmp1 <- apply(x@W, 1, mean, na.rm=TRUE)
86 
+    else
87 
+        tmp1 <- x@fold[, idx]
88 
+
89 
+    tmp2 <- -log10(x@p.value[, idx])
90 
+    if(!is.null(colnames(x@stat)[idx]))
91 
+        plotName <- colnames(x@stat)[idx]
92 
+    else
93 
+        plotName <- x@test
94 
+
95 
+    plot(tmp1, tmp2, main=paste("Volcano plot -", plotName), xlab="log2(fold)",
96 
+    ylab=paste("-log10(P.value)"), ...)
97 
+
98 
+}
99 
+
100 
+
101 
+## For maigesClass class (bi or tri-dimensional graphs of classifiers)
102 
+plot.maigesClass <- function(x, idx=1, ...) {
103 
+
104 
+    ## Getting sample types
105 
+    types <- colnames(x@W)
106 
+    uTypes <- unique(types)
107 
+
108 
+    if(dim(x@cliques)[2] == 2) {
109 
+        tmp1 <- x@W[x@cliques.idx[idx, 1], ]
110 
+        tmp2 <- x@W[x@cliques.idx[idx, 2], ]
111 
+        xlimite <- c(2*min(tmp1)-quantile(tmp1, 0.35), max(tmp1))
112 
+        ylimite <- c(min(tmp2), 2*max(tmp2)-quantile(tmp2, 0.65))
113 
+
114 
+        toLegend <- c(2*min(tmp1)-quantile(tmp1, 0.3),
115 
+        2*max(tmp2)-quantile(tmp2, 0.7))
116 
+
117 
+        plot(tmp1[types == uTypes[1]], tmp2[types == uTypes[1]], ylim=ylimite,
118 
+        xlim=xlimite, main="Classification graphic", xlab=x@cliques[idx,1],
119 
+        ylab=x@cliques[idx,2], pch=22, bg="red", col="red", ...)
120 
+
121 
+        points(tmp1[types == uTypes[2]], tmp2[types == uTypes[2]], pch=19,
122 
+        col="green")
123 
+
124 
+        legend(toLegend[1], toLegend[2], uTypes, pch=c(22,19),
125 
+        pt.bg=c("red","green"), col=c("red", "green"))
126 
+
127 
+    }
128 
+    else if(dim(x@cliques)[2] == 3) {
129 
+
130 
+        ## Loading required library
131 
+        require("rgl")
132 
+
133 
+        tmp1 <- x@W[x@cliques.idx[idx, 1], ]
134 
+        tmp2 <- x@W[x@cliques.idx[idx, 2], ]
135 
+        tmp3 <- x@W[x@cliques.idx[idx, 3], ]
136 
+
137 
+        labels <- c(x@cliques[idx, 1], x@cliques[idx, 2], x@cliques[idx, 3])
138 
+
139 
+        rgl::open3d()
140 
+
141 
+        rgl::text3d(tmp1[types == uTypes[1]], tmp2[types == uTypes[1]],
142 
+        tmp3[types == uTypes[1]], text=types[types == uTypes[1]], col="red")
143 
+
144 
+        rgl::text3d(tmp1[types != uTypes[1]], tmp2[types != uTypes[1]],
145 
+        tmp3[types != uTypes[1]], text=types[types != uTypes[1]], col="green")
146 
+
147 
+        rgl::decorate3d(xlab=labels[1], ylab=labels[2], zlab=labels[3],
148 
+        box=FALSE)
149 
+
150 
+        rgl::bbox3d(color=c("#767676","black"), emission="#767676",
151 
+        specular="#767676", shininess=10, alpha=0.9, marklen=50)
152 
+    }
153 
+    else
154 
+        stop("I can't plot graphics with more than 3 genes in the classifiers!")
155 
+
156 
+}
157 
+
158 
+
159 
+## For maigesRelNetM class (circular graph with significative iterations)
160 
+plot.maigesRelNetM <- function(x=NULL, cutPval=0.05, names=NULL, ...) {
161 
+
162 
+    ## Giving default names if it is NULL
163 
+    if(is.null(names))
164 
+        names <- c(x@types, "Significance of differences")
165 
+
166 
+
167 
+    ## Defining additional functions
168 
+    graphPath <- function(data=NULL, cuttoffPvalue) {
169 
+        N <- length(rownames(data@Corr1))
170 
+        corObj1 <- data@Corr1
171 
+        corObj2 <- data@Corr2
172 
+        Diff <- data@DifP
173 
+        ## Construct graphs centered on the 1st table
174 
+        vertices <- rownames(corObj1)
175 
+        arestas1 <- vector("list", length=length(vertices))
176 
+        names(arestas1) <- vertices
177 
+        arestas3 <- arestas2 <- arestas1
178 
+        for (i in 1:length(vertices)) {
179 
+            idx <- Diff[i, ] <= cuttoffPvalue
180 
+            arestas1[[i]] <- list(edges=vertices[idx],
181 
+            weights=unname(corObj1[i, idx]))
182 
+
183 
+            arestas2[[i]] <- list(edges=vertices[idx],
184 
+            weights=unname(corObj2[i, idx]))
185 
+
186 
+            arestas3[[i]] <- list(edges=vertices[idx],
187 
+            weights=unname(Diff[i, idx]))
188 
+        }
189 
+
190 
+        ## Defining the object with the graphs
191 
+        Path <- list(Type1=new("graphNEL", vertices, edgeL=arestas1),
192 
+        Type2=new("graphNEL", vertices, edgeL=arestas2),
193 
+        Dif=new("graphNEL", vertices, edgeL=arestas3))
194 
+
195 
+        return(Path)
196 
+    }
197 
+
198 
+    plot.dots <- function(xy, v, n) {
199 
+        text(xy[, 1], xy[, 2], v, cex=1.2, col="blue")
200 
+    }
201 
+
202 
+    draw.edges <- function(coor, vertices, edges, alpha, sorting) {
203 
+
204 
+        main <- edges[[1]]
205 
+        a <- coor[which(vertices == main), ]
206 
+        if(sorting == "A")
207 
+            strength <- order(abs(edges[[3]]))
208 
+        else
209 
+            strength <- order(abs(edges[[3]]), decreasing=TRUE)
210 
+
211 
+        for (k in 1:length(edges[[2]])) {
212 
+            b <- coor[edges[[2]][k], ]
213 
+            ba <- b-a
214 
+            ba <- ba/sqrt(sum(ba*ba))
215 
+            x <- a+ba*alpha
216 
+            y <- b-ba*alpha
217 
+            if (edges[[3]][k] > 0 )
218 
+                color <- "red"
219 
+            else
220 
+                color <- "green"
221 
+            a1 <- c(x[1], y[1])
222 
+            a2 <- c(x[2], y[2])
223 
+
224 
+            lines(a1, a2, lwd=strength[k], lty=1, col=color)
225 
+        }
226 
+    }
227 
+
228 
+    def.coor <- function(ce, k, h, w) {
229 
+        if (k == 1)
230 
+            return(ce)
231 
+        else if (k == 2) {
232 
+            r1 <- c(ce[1], ce[1])
233 
+            r2 <- c(ce[2]+h*0.3, ce[2]-h*0.3)
234 
+        }
235 
+        else if (k == 3) {
236 
+            r1 <- c(ce[1], ce[1], ce[1])
237 
+            r2 <- c(ce[2]+h*0.25, ce[2], ce[2]-h*0.25)
238 
+        }
239 
+        else if (k == 4) {
240 
+            r1 <- c(ce[1]-w*0.3, ce[1]+w*0.3, ce[1]+w*0.3, ce[1]-w*0.3)
241 
+            r2 <- c(ce[2]-h*0.3, ce[2]-h*0.3, ce[2]+h*0.3, ce[2]+h*0.3)
242 
+        }
243 
+        else {
244 
+            a <- 1
245 
+            z <- seq(a, a+2*pi, len=k+1)
246 
+            z <- z[-1]
247 
+            r1 <- ce[1]+w/2.5*cos(z)
248 
+            r2 <- ce[2]+h/2.5*sin(z)
249 
+        }
250 
+        cbind(r1, r2)
251 
+    }
252 
+
253 
+    plotGraph <- function (graph, coor=NULL, alpha=2.5, main="Some Graph",
254 
+    sorting) {
255 
+
256 
+        ## Define the nodes and the length of the graph (number of nodes)
257 
+        v <- nodes(graph)
258 
+        n <- length(v)
259 
+
260 
+        ## Create an empty draw device
261 
+        plot(c(0, 100), c(0, 100), type="n", axes=FALSE, xlab="", ylab="",
262 
+        main=main)
263 
+
264 
+        ## Define an initial center to the graph
265 
+        center <- matrix(c(50, 50), ncol=2)
266 
+
267 
+        ## Create an object of the locations for each node
268 
+        if (is.null(coor))
269 
+            coor <- def.coor(center, n, 100, 100)
270 
+
271 
+        ## Plot each node on the device
272 
+        plot.dots(coor, v, n)
273 
+
274 
+        ## Locate the nodes to plot on the figure
275 
+        for (i in 1:n) {
276 
+            if(length(edgeL(graph)[[i]]$edges) > 0) {
277 
+                elo <- list(v[i], edgeL(graph)[[i]]$edges,
278 
+                as.numeric(edgeWeights(graph, i)[[1]]))
279 
+                draw.edges(coor, v, elo, alpha, sorting)
280 
+            }
281 
+        }
282 
+        colnames(coor) <- c("x", "y")
283 
+        return(invisible(coor))
284 
+    }
285 
+
286 
+
287 
+    ## Ploting the graphs
288 
+    graph <- graphPath(x, cutPval)
289 
+
290 
+    par(mfrow=c(1,3))
291 
+    tmp <- plotGraph(graph[[1]], main=names[1], sorting="A")
292 
+    plotGraph(graph[[2]], coor=tmp, main=names[2], sorting="A")
293 
+    plotGraph(graph[[3]], coor=tmp, main=names[3], sorting="D")
294 
+
295 
+}
296 
+
297 
+
298 
+## For maigesRelNetB class (circular graph with significative iterations)
299 
+plot.maigesRelNetB <- function(x=NULL, cutPval=0.05, cutCor=NULL, name=NULL,
300 
+...) {
301 
+
302 
+    ## Some tests
303 
+    if(!is.null(cutPval) & !is.null(cutCor))
304 
+        stop("You must specify only, cutPval or cutCor.")
305 
+
306 
+    if(is.null(name))
307 
+        name <- x@type
308 
+
309 
+    ## Defining additional functions
310 
+    graphPath <- function(data=NULL, cuttoffCor=NULL, cuttoffP=NULL) {
311 
+
312 
+        N <- length(rownames(data@Corr))
313 
+        corObj <- data@Corr
314 
+        ## Construct graphs centered on the 1st table
315 
+        vertices <- rownames(corObj)
316 
+        arestas <- vector("list", length=length(vertices))
317 
+        names(arestas) <- vertices
318 
+        for (i in 1:length(vertices)) {
319 
+            if(!is.null(cuttoffCor))
320 
+                idx <- abs(corObj[i, ]) >= cuttoffCor
321 
+            if(!is.null(cuttoffP))
322 
+                idx <- abs(data@Pval[i, ]) <= cuttoffP
323 
+            arestas[[i]] <- list(edges=vertices[idx], weights=unname(corObj[i, idx]))
324 
+        }
325 
+
326 
+        ## Defining the object with the graphs
327 
+        Path <- new("graphNEL", vertices, edgeL=arestas)
328 
+        return(Path)
329 
+    }
330 
+
331 
+    plot.dots <- function(xy, v, n) {
332 
+        text(xy[, 1], xy[, 2], v, cex=1.2, col="blue")
333 
+    }
334 
+
335 
+    draw.edges <- function(coor, vertices, edges, alpha, sorting) {
336 
+
337 
+        main <- edges[[1]]
338 
+        a <- coor[which(vertices == main), ]
339 
+        if(sorting == "A")
340 
+            strength <- order(abs(edges[[3]]))
341 
+        else
342 
+            strength <- order(abs(edges[[3]]), decreasing=TRUE)
343 
+
344 
+        for (k in 1:length(edges[[2]])) {
345 
+            b <- coor[edges[[2]][k], ]
346 
+            ba <- b-a
347 
+            ba <- ba/sqrt(sum(ba*ba))
348 
+            x <- a+ba*alpha
349 
+            y <- b-ba*alpha
350 
+            if (edges[[3]][k] > 0 )
351 
+                color <- "red"
352 
+            else
353 
+                color <- "green"
354 
+            a1 <- c(x[1], y[1])
355 
+            a2 <- c(x[2], y[2])
356 
+            lines(a1, a2, lwd=strength[k], lty=1, col=color)
357 
+        }
358 
+    }
359 
+
360 
+    def.coor <- function(ce, k, h, w) {
361 
+        if (k == 1)
362 
+            return(ce)
363 
+        else if (k == 2) {
364 
+            r1 <- c(ce[1], ce[1])
365 
+            r2 <- c(ce[2]+h*0.3, ce[2]-h*0.3)
366 
+        }
367 
+        else if (k == 3) {
368 
+            r1 <- c(ce[1], ce[1], ce[1])
369 
+            r2 <- c(ce[2]+h*0.25, ce[2], ce[2]-h*0.25)
370 
+        }
371 
+        else if (k == 4) {
372 
+            r1 <- c(ce[1]-w*0.3, ce[1]+w*0.3, ce[1]+w*0.3, ce[1]-w*0.3)
373 
+            r2 <- c(ce[2]-h*0.3, ce[2]-h*0.3, ce[2]+h*0.3, ce[2]+h*0.3)
374 
+        }
375 
+        else {
376 
+            a <- 1
377 
+            z <- seq(a, a+2*pi, len=k+1)
378 
+            z <- z[-1]
379 
+            r1 <- ce[1]+w/2.5*cos(z)
380 
+            r2 <- ce[2]+h/2.5*sin(z)
381 
+        }
382 
+        cbind(r1, r2)
383 
+    }
384 
+
385 
+    plotGraph <- function (graph, coor=NULL, alpha=2.5, main="Some Graph",
386 
+    sorting) {
387 
+
388 
+        ## Define the nodes and the length of the graph (number of nodes)
389 
+        v <- nodes(graph)
390 
+        n <- length(v)
391 
+
392 
+        ## Create an empty draw device
393 
+        plot(c(0, 100), c(0, 100), type="n", axes=FALSE, xlab="", ylab="",
394 
+        main=main)
395 
+
396 
+        ## Define an initial center to the graph
397 
+        center <- matrix(c(50, 50), ncol=2)
398 
+
399 
+        ## Create an object of the locations for each node
400 
+        if (is.null(coor)) {
401 
+            coor <- def.coor(center, n, 100, 100)
402 
+        }
403 
+        ## Plot each node on the device
404 
+        plot.dots(coor, v, n)
405 
+        ## Locate the nodes to plot on the figure
406 
+        for (i in 1:n) {
407 
+            if(length(edgeL(graph)[[i]]$edges) > 0) {
408 
+                elo <- list(v[i], edgeL(graph)[[i]]$edges,
409 
+                as.numeric(edgeWeights(graph, i)[[1]]))
410 
+                ##if(length(elo[[2]]) > 0)
411 
+                draw.edges(coor, v, elo, alpha, sorting)
412 
+            }
413 
+        }
414 
+        colnames(coor) <- c("x", "y")
415 
+        return(invisible(coor))
416 
+    }
417 
+
418 
+
419 
+    if(!is.null(cutCor)) {
420 
+        ## geting maximum bootstrap value to use in cutCor
421 
+        if((is.character(cutCor)) & (cutCor == "max"))
422 
+            cutCor <- max(x@maxB[upper.tri(x@maxB)])
423 
+        else if((cutCor < 0) | (cutCor >= 1))
424 
+            stop("cutCor must a number in [0,1) or 'max'.")
425 
+
426 
+        ## Ploting the graphs
427 
+        graph <- graphPath(x, cutCor, NULL)
428 
+        plotGraph(graph, main=name, sorting="A")
429 
+    }
430 
+    else {
431 
+        ## Ploting the graphs
432 
+        graph <- graphPath(x, NULL, cutPval)
433 
+        plotGraph(graph, main=name, sorting="A")
434 
+    }
435 
+}
436 
+
437 
+
438 
+## For maigesActMod class (heatmap of the significative results)
439 
+plot.maigesActMod <- function(x, type=c("S", "C")[2], keepEmpty=FALSE, ...) {
440 
+
441 
+    ## Making some basic initial tests...
442 
+    if(is.null(x))
443 
+        stop("You MUST specify an object generated by activeMod function.")
444 
+    if(!is.element(type, c("S", "C")))
445 
+        stop("You must be 'C' or 'S'.")
446 
+
447 
+    if(type == "S") {
448 
+        if(keepEmpty)
449 
+            table <- x@modBySamp
450 
+        else {
451 
+            idx <- apply(x@modBySamp != 0, 2, sum, na.rm=TRUE) != 0
452 
+            if(sum(idx) < 2)
453 
+                stop("Less than 2 elements present significant results!")
454 
+            table <- x@modBySamp[, idx]
455 
+        }
456 
+        limite <- max(abs(range(table, na.rm=TRUE)))
457 
+        limite <- c(-limite, limite)
458 
+        idx1 <- order(rownames(table))
459 
+        idx2 <- order.dendrogram(as.dendrogram(hclust(dist(t(table)))))
460 
+
461 
+        heatmap(table[idx1, idx2], scale="none", col=maigesPack:::greenRed(),
462 
+        zlim=limite, Rowv=NA, Colv=NA, ...)
463 
+
464 
+    }
465 
+    else if(type == "C") {
466 
+        if(keepEmpty)
467 
+            table <- x@modByCond
468 
+        else {
469 
+            idx <- apply(x@modByCond != 0, 2, sum, na.rm=TRUE) != 0
470 
+            if(sum(idx) < 2)
471 
+                stop("Less than 2 elements present significant results!")
472 
+            table <- x@modByCond[, idx]
473 
+        }
474 
+        limite <- max(abs(range(table, na.rm=TRUE)))
475 
+        limite <- c(-limite, limite)
476 
+        idx1 <- order(rownames(table))
477 
+        idx2 <- order.dendrogram(as.dendrogram(hclust(dist(t(table)))))
478 
+
479 
+        heatmap(table[idx1, idx2], scale="none", col=maigesPack:::greenRed(),
480 
+        zlim=limite, Rowv=NA, Colv=NA, ...)
481 
+
482 
+    }
483 
+}
484 
+
485 
+
486 
+## For maigesActNet class (heatmap of the significative results)
487 
+plot.maigesActNet <- function(x, type=c("score","p-value")[1], ...) {
488 
+
489 
+    ## Making some basic initial tests...
490 
+    if(is.null(x))
491 
+        stop("You MUST specify an object generated by activeNet function.")
492 
+    if(!is.element(type, c("score", "p-value")))
493 
+        stop("You must be 'score' or 'p-value'.")
494 
+
495 
+    if(type == "score") {
496 
+        limite <- max(x@scores, na.rm=TRUE)
497 
+        limite <- c(0, limite)
498 
+        idx <- order.dendrogram(as.dendrogram(hclust(dist(t(x@scores)))))
499 
+
500 
+        heatmap(x@scores[, idx], scale="none", col=maigesPack:::blackBlue(),
501 
+        zlim=limite, Rowv=NA, Colv=NA, ...)
502 
+
503 
+    }
504 
+    else {
505 
+
506 
+        limite = max(-log10(x@Pvalues), na.rm=TRUE)
507 
+        limite = c(0, limite)
508 
+
509 
+        idx <- order.dendrogram(as.dendrogram(hclust(dist(t(x@Pvalues)))))
510 
+
511 
+        heatmap(-log10(x@Pvalues)[, idx], scale="none",
512 
+        col=maigesPack:::blackBlue(), zlim=limite, Rowv=NA, Colv=NA, ...)
513 
+
514 
+    }
515 
+}