Browse code

Correction of the vignette. Made sure breaks are taken into accounts in heatmap.plus function.

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/MineICA@83955 bc3139a8-67e5-0310-9ffc-ced21a209358

Anne Biton authored on 05/12/2013 20:29:39
Showing 9 changed files

... ...
@@ -1,7 +1,7 @@
1 1
 Package: MineICA
2 2
 Type: Package
3 3
 Title: Analysis of an ICA decomposition obtained on genomics data
4
-Version: 1.3.1
4
+Version: 1.3.2
5 5
 Date: 2012-03-16
6 6
 Author: Anne Biton
7 7
 Maintainer: Anne Biton <anne.biton@gmail.com>
... ...
@@ -648,9 +648,9 @@ plotDens2classInComp_plotOnly <- function (annot,
648 648
                     else if (typePlot == "boxplot") {
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                         g <- g + geom_boxplot(aes(x=interest, y = comp, fill = interest),
650 650
                                               position = "identity", data = annot,
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-                                              colour = "black", outlier.shape = if (addPoints) NA else 16) + scale_x_discrete(colAnnot) + scale_y_continuous(if (is.null(ylab)) "Sample contributions" else ylab)                        
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+                                              colour = "black", outlier.shape = if (addPoints) NA else 16) + scale_x_discrete(colAnnot) + scale_y_continuous(if (is.null(ylab)) "Sample contributions" else ylab) + theme_bw()                       
652 652
                         if (addPoints)
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-                            g <- g + geom_jitter(aes(x=interest, y = comp, fill = interest), data = annot, color="#1A1A1A99", size=1.9, position=position_jitter(width=.2)) #+ theme_bw()#  + coord_flip()+position="jitter", 
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+                            g <- g + geom_jitter(aes(x=interest, y = comp, fill = interest), data = annot, color="#1A1A1A99", size=1.9, position=position_jitter(width=.2))+ theme_bw()#  + coord_flip()+position="jitter", 
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                     }
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                 }
... ...
@@ -1038,7 +1038,7 @@ m0.5="#A6CEE3", m1="#1F78B4", m1.5="#B2DF8A", m2="#33A02C", m2.5="#FB9A99", m3="
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    patientsDoublePrel = c("260GIL"= "#8DD3C7","6FON" = "mistyrose4","318AKL" = "#FB8072","39VAS" = "#80B1D3", "74COU" =  "#FDB462",
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   HM = "#8DD3C7", IGR = "#FB8072", HF = "#FDB462" )  
1040 1040
 
1041
-	sousTypes = c(papillaire_pediculee = "paleturquoise4", papillaire_sessile = "turquoise4", papillaire = "turquoise4",solide_vegetante = "chartreuse4", ulceree_necrosee = "chocolate", ulceree_vegetante = "89", ulceree = "89",  plane = "gray30", autre = "gray30",autres = "gray30", "ER-"="bisque","ER+"="palevioletred1",
1041
+	sousTypes = c(papillaire_pediculee = "paleturquoise4", papillaire_sessile = "turquoise4", papillaire = "turquoise4",solide_vegetante = "chartreuse4", ulceree_necrosee = "chocolate", ulceree_vegetante = "89", ulceree = "89",  plane = "gray30", autre = "gray30",autres = "gray30", "ER+"="bisque","ER-"="palevioletred1",
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 	 classique = "paleturquoise4", epidermoede = "turquoise4", epidermoide = "turquoise4",micropapillaire = "chartreuse4", neuroendocrine = "violetred1", sarcomatoide = "violetred4",
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         "endophytique" = "violetred1", "largesMassifs" = "chartreuse4", "plages" = "yellow", "travees_massifsPeuCohesifs" = "chocolate1", 
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 "travees_petitsMassifs" = "chocolate4")
... ...
@@ -184,7 +184,7 @@ qualVarAnalysis <- function(params, icaSet, keepVar, keepComp=indComp(icaSet), k
184 184
             whichAnnotSign <- 1:nrow(resTests)
185 185
         
186 186
         
187
-        if (missing(colours) || length(colours)==0)
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+        if (missing(colours) | length(colours)==0)
188 188
               colours <- annot2Color(annot)
189 189
 
190 190
         
... ...
@@ -162,33 +162,34 @@ heatmap.plus = function (x, Rowv = NULL, Colv = if (symm) "Rowv" else NULL,
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     }
163 163
     else iy <- 1:nr
164 164
 
165
-    ## add color breaks, extend the number of colorsd between the 25th and 75th quantiles
166
-    rm <- range(x)
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-    qq1 <- quantile(unlist(x),0.25)
168
-    if (qq1 == rm[1])
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-        qq1 <- quantile(unlist(x),0.35)
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+    if (is.null(breaks)) {
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+        ## add color breaks, extend the number of colorsd between the 25th and 75th quantiles
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+        rm <- range(x)
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+        qq1 <- quantile(unlist(x),0.25)
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+        if (qq1 == rm[1])
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+            qq1 <- quantile(unlist(x),0.35)
170 171
         
171
-    qq2 <- quantile(unlist(x),0.75)
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-    nbCol <- length(heatmapCol)
173
-    extrNbCol <- floor(nbCol/4)-1
174
-    midNbCol <- floor(nbCol/2) 
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-    breaks <- c(seq(from = rm[1], to = qq1, by = abs(qq1-rm[1])/extrNbCol),
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-                seq(from = qq1, to = qq2, by = (qq2-qq1)/midNbCol),
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-                seq(from = qq2, to = rm[2], by = (rm[2]-qq2)/extrNbCol))
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+        qq2 <- quantile(unlist(x),0.75)
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+        nbCol <- length(heatmapCol)
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+        extrNbCol <- floor(nbCol/4)-1
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+        midNbCol <- floor(nbCol/2) 
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+        breaks <- c(seq(from = rm[1], to = qq1, by = abs(qq1-rm[1])/extrNbCol),
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+                    seq(from = qq1, to = qq2, by = (qq2-qq1)/midNbCol),
178
+                    seq(from = qq2, to = rm[2], by = (rm[2]-qq2)/extrNbCol))
178 179
 
179
-    if (!is.null(breaks)) {
180 180
         image(x = 1:nc, y = 1:nr, z = x, xlim = 0.5 + c(0, nc), ylim = 0.5 + 
181 181
               c(0, nr), axes = FALSE, xlab = "", ylab = "",
182 182
               col = if (!is.null(heatmapCol)) heatmapCol else heat.colors(12),
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-              breaks = breaks, ...) ## add colors
183
+              breaks = breaks, ...)
184 184
     }
185
-    
186 185
     else {
187
-        image(x = 1:nc, y = 1:nr, z = x, xlim = 0.5 + c(0, nc), ylim = 0.5 + 
186
+            image(x = 1:nc, y = 1:nr, z = x, xlim = 0.5 + c(0, nc), ylim = 0.5 + 
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               c(0, nr), axes = FALSE, xlab = "", ylab = "",
189
-              col = if (!is.null(heatmapCol)) heatmapCol else heat.colors(12),...)
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+              col = if (!is.null(heatmapCol)) heatmapCol else heat.colors(12),
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+              breaks = breaks, ...) ## add colors
190 190
     }
191 191
     
192
+    
192 193
     axis(1, 1:nc, labels = labCol, las = 2, line = -0.5, tick = 0, 
193 194
         cex.axis = cexCol)
194 195
 
... ...
@@ -349,7 +349,7 @@ These witnesses can be automaticall selected with the function \Robject{selectWi
349 349
 <<witG, echo=TRUE>>=
350 350
 witGenes(icaSetMainz)[1:5]
351 351
 ## We can for example modify the second contributing gene 
352
-witGenes(icaSetMainz)[3] <- "KRT16"
352
+witGenes(icaSetMainz)[2] <- "KRT16"
353 353
 @ 
354 354
 
355 355
 
... ...
@@ -531,16 +531,16 @@ keepVar <- c("er","grade")
531 531
 ## heatmap with dendrogram
532 532
 resH <- plot_heatmapsOnSel(icaSet = icaSetMainz, selCutoff = 3, level = "genes", 
533 533
                            keepVar = keepVar,
534
-                           doSamplesDendro = TRUE, doGenesDendro = TRUE, keepComp = 3,
535
-                           heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44),
534
+                           doSamplesDendro = TRUE, doGenesDendro = TRUE, keepComp = 2,
535
+                           heatmapCol = maPalette(low = "blue", high = "red", mid = "yellow", k=44),
536 536
                            file = "heatmapWithDendro", annot2col=annot2col(params))
537 537
 
538 538
 
539 539
 ## heatmap where genes and samples are ordered by contribution values
540 540
 resH <- plot_heatmapsOnSel(icaSet = icaSetMainz, selCutoff = 3, level = "genes", 
541 541
                            keepVar = keepVar,
542
-                           doSamplesDendro = FALSE, doGenesDendro = FALSE, keepComp = 3,
543
-                           heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44),
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+                           doSamplesDendro = FALSE, doGenesDendro = FALSE, keepComp = 2,
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+                           heatmapCol = maPalette(low = "blue", high = "red", mid = "yellow", k=44),
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                            file = "heatmapWithoutDendro", annot2col=annot2col(params))
545 545
 
546 546
 @ 
... ...
@@ -569,79 +569,81 @@ resEnrich <- runEnrich(params=params,icaSet=icaSetMainz[,,1:3],
569 569
 
570 570
 The output \Robject{resEnrich} is a list whose each element contains results obtained on each database for every component tested. For each component, three enrichment results are available, depending on how contributing genes are selected: on the absolute projection values (``both''), on the positive projection (``pos''), and on the negative projection (``neg'').
571 571
 
572
-We can see that the first component is associated with the cell cycle, the second with immune reaction, and the third component with epiderm development: 
573
-
574
-<<runEnrich2, echo=TRUE, print=FALSE, eval=FALSE>>=
575
-## Access results obtained for GO/BP for the first three components 
576
-# First component, when gene selection was based  on the absolute projection values
577
-head(resEnrich$GO$BP[[1]]$both)
578
-@ 
579
-<<runEnrich2bis, echo=FALSE, print=FALSE, eval=TRUE>>=
580
-structure(list(GOBPID = c("GO:0000236", "GO:0031581", "GO:0045786", 
581
-"GO:0045104", "GO:0010389", "GO:0031577"), Pvalue = c(1.07618237962117e-05, 
582
-1.31817485465954e-05, 0.00010249730291343, 0.00199032614648593, 
583
-0.0022683621486939, 0.0022683621486939), OddsRatio = c(37.2312925170068, 
584
-93.6306818181818, 9.90278637770898, 36.7090301003344, 34.0807453416149, 
585
-34.0807453416149), ExpCount = c(0.144770177343467, 0.0497647484618169, 
586
-0.79623597538907, 0.0678610206297503, 0.0723850886717336, 0.0723850886717336
587
-), Count = c(4L, 3L, 6L, 2L, 2L, 2L), Size = c(32L, 11L, 176L, 
588
-15L, 16L, 16L), Term = c("mitotic prometaphase", "hemidesmosome assembly", 
589
-"negative regulation of cell cycle", "intermediate filament cytoskeleton organization", 
590
-"regulation of G2/M transition of mitotic cell cycle", "spindle checkpoint"
591
-), In_geneSymbols = c("BIRC5,CDC20,CDCA8,CENPN", "DST,COL17A1,KRT14", 
592
-"BIRC5,DST,CDC20,TOP2A,CDC45,MCM10", "DST,KRT14", "TOP2A,CDC45", 
593
-"BIRC5,CDC20")), .Names = c("GOBPID", "Pvalue", "OddsRatio", 
594
-"ExpCount", "Count", "Size", "Term", "In_geneSymbols"), row.names = c(NA, 
595
-6L), class = "data.frame")
596
-@ 
572
+We can see that the first component is associated with immune reaction, the second component with epiderm development, and the third component with cell cycle: 
597 573
 
598 574
 
599 575
 <<runEnrich3, echo=TRUE, print=FALSE, eval=FALSE>>=
600
-# Second component, when gene selection was based on the negative projection values
601
-head(resEnrich$GO$BP[[2]]$left)
576
+## Access results obtained for GO/BP for the first three components 
577
+# First component, when gene selection was based on the negative projection values
578
+head(resEnrich$GO$BP[[1]]$left)
602 579
 @ 
603 580
 <<runEnrich3bis, echo=FALSE, print=FALSE, eval=TRUE>>=
604
-structure(list(GOBPID = c("GO:0002253", "GO:0006959", "GO:0006955", 
605
-"GO:0002684", "GO:0048584", "GO:0002429"), Pvalue = c(1.52817137763709e-07, 
606
-4.81288352728676e-07, 1.22396115326329e-06, 6.35955291059939e-06, 
607
-9.25622695313701e-06, 1.06767418066984e-05), OddsRatio = c(18.3424657534247, 
608
-27.0398009950249, 28.8538011695906, 11.2582995951417, 7.46730769230769, 
609
-21.6746411483254), ExpCount = c(0.668838219326819, 0.317046688382193, 
610
-0.415086965018566, 1.11467391304348, 2.47991313789359, 0.308360477741585
611
-), Count = c(8L, 6L, 6L, 8L, 11L, 5L), Size = c(154L, 73L, 177L, 
612
-293L, 571L, 71L), Term = c("activation of immune response", "humoral immune response", 
613
-"immune response", "positive regulation of immune system process", 
614
-"positive regulation of response to stimulus", "immune response-activating cell surface receptor signaling pathway"
615
-), In_geneSymbols = c("IGHG1,IGKC,LCK,PTPRC,PTPN22,TRBC1,IGKV4-1,TRAT1", 
616
-"IGHG1,IGKC,SH2D1A,POU2AF1,CXCL13,IGKV4-1", "MS4A1,IGHD,IGHM,IGJ,CXCL9,CXCL11", 
617
-"IGHG1,IGKC,SH2D1A,CCL5,CXCL13,PTPN22,TRBC1,IGKV4-1", "IGHG1,IGKC,LCK,SH2D1A,PTPRC,CCL5,CXCL13,PTPN22,TRBC1,IGKV4-1,TRAT1", 
618
-"LCK,PTPRC,PTPN22,TRBC1,TRAT1")), .Names = c("GOBPID", "Pvalue", 
619
-"OddsRatio", "ExpCount", "Count", "Size", "Term", "In_geneSymbols"
620
-), row.names = c(NA, 6L), class = "data.frame")
621
-
581
+structure(list(GOBPID = c("GO:0006955", "GO:0002694", "GO:0050867", 
582
+"GO:0002429", "GO:0050863", "GO:0051251"), Pvalue = c(4.13398630676443e-16, 
583
+3.53565704068228e-14, 1.0598364083037e-11, 2.10474926262594e-11, 
584
+2.96408182272612e-11, 3.41481775787203e-11), OddsRatio = c(16.1139281129653, 
585
+10.2399932157395, 10.34765625, 13.1975308641975, 14.5422535211268, 
586
+14.3646536754775), ExpCount = c(2.18081918081918, 3.24691805656273, 
587
+2.3821609862219, 1.56635242929659, 1.33711359063472, 1.35043827611395
588
+), Count = c(21L, 23L, 18L, 15L, 14L, 14L), Size = c(185L, 199L, 
589
+146L, 96L, 85L, 85L), Term = c("immune response", "regulation of leukocyte activation", 
590
+"positive regulation of cell activation", "immune response-activating cell surface receptor signaling pathway", 
591
+"regulation of T cell activation", "positive regulation of lymphocyte activation"
592
+), In_geneSymbols = c("CD7,MS4A1,CD27,CTSW,GZMA,HLA-DOB,IGHD,IGHM,IGJ,IL2RG,CXCL10,LTB,LY9,CXCL9,CCL18,CXCL11,CST7,FCGR2C,IL32,ADAMDEC1,ICOS", 
593
+"AIF1,CD2,CD3D,CD3G,CD247,CD27,CD37,CD38,HLA-DQB1,LCK,PTPRC,CCL5,CCL19,XCL1,EBI3,LILRB1,PTPN22,SIT1,TRBC1,TRAC,ICOS,MZB1,SLAMF7", 
594
+"AIF1,CD2,CD3D,CD3G,CD247,CD27,CD38,HLA-DQB1,LCK,PTPRC,CCL5,CCL19,XCL1,EBI3,LILRB1,TRBC1,TRAC,ICOS", 
595
+"CD3D,CD3G,CD247,CD38,HLA-DQB1,IGHG1,IGKC,IGLC1,LCK,PRKCB,PTPRC,PTPN22,TRBC1,TRAC,TRAT1", 
596
+"AIF1,CD3D,CD3G,CD247,HLA-DQB1,PTPRC,CCL5,XCL1,EBI3,PTPN22,SIT1,TRBC1,TRAC,ICOS", 
597
+"AIF1,CD3D,CD3G,CD247,CD38,HLA-DQB1,PTPRC,CCL5,XCL1,EBI3,LILRB1,TRBC1,TRAC,ICOS"
598
+)), .Names = c("GOBPID", "Pvalue", "OddsRatio", "ExpCount", "Count", 
599
+"Size", "Term", "In_geneSymbols"), row.names = c(NA, 6L), class = "data.frame")
622 600
 @ 
623 601
 
624 602
 <<runEnrich4, echo=TRUE, print=FALSE, eval=FALSE>>=
625
-# Third component
626
-head(resEnrich$GO$BP[[3]]$both, n=5)
603
+# Second component
604
+head(resEnrich$GO$BP[[2]]$both, n=5)
627 605
 @ 
628 606
 <<runEnrich4bis, echo=FALSE, print=FALSE, eval=TRUE>>=
629
-structure(list(GOBPID = c("GO:0008544", "GO:0034330", "GO:0045104", 
630
-"GO:0060687", "GO:0060512"), Pvalue = c(8.06278555738841e-06, 
631
-0.000234608964462521, 0.00034947787505441, 0.000886129575409258, 
632
-0.000980819533506728), OddsRatio = c(7.96682149966821, 7.90315480557594, 
633
-27.305, 71.5032679738562, 18.1833333333333), ExpCount = c(1.40028954035469, 
634
-0.891965255157438, 0.143865363735071, 0.0479551212450235, 0.201411509229099
635
-), Count = c(9L, 6L, 3L, 2L, 3L), Size = c(146L, 93L, 15L, 5L, 
636
-21L), Term = c("epidermis development", "cell junction organization", 
637
-"intermediate filament cytoskeleton organization", "regulation of branching involved in prostate gland morphogenesis", 
638
-"prostate gland morphogenesis"), In_geneSymbols = c("EGFR,KRT5,KRT14,KRT15,KRT16,KRT17,KLK7,S100A7,CALML5", 
639
-"CDH3,GPM6B,KRT5,KRT14,SFRP1,UGT8", "KRT14,KRT16,SYNM", "ESR1,SFRP1", 
640
-"ESR1,SERPINB5,SFRP1")), .Names = c("GOBPID", "Pvalue", "OddsRatio", 
607
+structure(list(GOBPID = c("GO:0045104", "GO:0031581", "GO:0030318", 
608
+"GO:0070488", "GO:0072602", "GO:0034329"), Pvalue = c(2.16044773513962e-05, 
609
+6.04616151867683e-05, 0.000394387232705895, 0.000461592979000511, 
610
+0.000461592979000511, 0.00107959102524193), OddsRatio = c(19.6820175438597, 
611
+26.7826086956522, 14.4053511705686, Inf, Inf, 4.29841077032088
612
+), ExpCount = c(0.366751269035533, 0.237309644670051, 0.366751269035533, 
613
+0.0431472081218274, 0.0431472081218274, 2.09263959390863), Count = c(5L, 
614
+4L, 4L, 2L, 2L, 8L), Size = c(17L, 11L, 17L, 2L, 2L, 97L), Term = c("intermediate filament cytoskeleton organization", 
615
+"hemidesmosome assembly", "melanocyte differentiation", "neutrophil aggregation", 
616
+"interleukin-4 secretion", "cell junction assembly"), In_geneSymbols = c("DST,KRT14,KRT16,PKP1,SYNM", 
617
+"DST,COL17A1,KRT5,KRT14", "EDN3,KIT,SOX10,MLPH", "S100A8,S100A9", 
618
+"GATA3,VTCN1", "DST,CDH3,COL17A1,GPM6B,KRT5,KRT14,SFRP1,UGT8"
619
+)), .Names = c("GOBPID", "Pvalue", "OddsRatio", "ExpCount", "Count", 
620
+"Size", "Term", "In_geneSymbols"), row.names = c(NA, 6L), class = "data.frame")
621
+@ 
622
+<<runEnrich2, echo=TRUE, print=FALSE, eval=FALSE>>=
623
+# Third component, when gene selection was based  on the absolute projection values
624
+head(resEnrich$GO$BP[[3]]$both)
625
+@ 
626
+<<runEnrich2bis, echo=FALSE, print=FALSE, eval=TRUE>>=
627
+structure(list(GOBPID = c("GO:0048285", "GO:0051301", "GO:0007067", 
628
+"GO:0007076", "GO:0000086", "GO:0006271"), Pvalue = c(2.1806594780167e-24, 
629
+7.64765765268202e-16, 4.85530963990747e-12, 9.30475956146815e-06, 
630
+1.95701394548966e-05, 5.65649365383205e-05), OddsRatio = c(16.8486540378863, 
631
+14.5290697674419, 17.0874279123414, Inf, 8.18195718654434, 27.2667509481669
632
+), ExpCount = c(3.1816533720087, 2.14748665070889, 1.18268700606506, 
633
+0.063633067440174, 1.18781725888325, 0.233321247280638), Count = c(32L, 
634
+21L, 14L, 3L, 8L, 4L), Size = c(150L, 107L, 65L, 3L, 56L, 11L
635
+), Term = c("organelle fission", "cell division", "mitosis", 
636
+"mitotic chromosome condensation", "G2/M transition of mitotic cell cycle", 
637
+"DNA strand elongation involved in DNA replication"), In_geneSymbols = c("BIRC5,BUB1,CCNA2,CDK1,CDC20,CDC25A,CENPE,IGF1,KIFC1,MYBL2,NEK2,AURKA,CCNB2,KIF23,DLGAP5,NDC80,UBE2C,TPX2,NCAPH,UBE2S,NUSAP1,ERCC6L,CDCA8,CEP55,CENPN,PBK,NCAPG,DSCC1,CDCA3,KIF18B,SKA1,ASPM", 
638
+"BUB1,CCNA2,CDK1,CDC20,CDC25A,CENPE,KIFC1,NEK2,AURKA,TOP2A,CCNB2,NDC80,UBE2C,TPX2,NCAPH,UBE2S,ERCC6L,CDCA8,NCAPG,CDCA3,SKA1", 
639
+"CCNA2,CDK1,CDC25A,CCNB2,TPX2,ERCC6L,CDCA8,CEP55,CENPN,PBK,CDCA3,KIF18B,SKA1,ASPM", 
640
+"NCAPH,NUSAP1,NCAPG", "BIRC5,CCNA2,CDK1,CDC25A,FOXM1,NEK2,CCNB2,MELK", 
641
+"MCM5,CDC45,GINS1,GINS2")), .Names = c("GOBPID", "Pvalue", "OddsRatio", 
641 642
 "ExpCount", "Count", "Size", "Term", "In_geneSymbols"), row.names = c(NA, 
642
-5L), class = "data.frame")
643
+6L), class = "data.frame")
643 644
 @ 
644 645
 
646
+
645 647
 The function \Robject{runEnrich} also writes these enrichment results in HTML files located in the sub-directory "GOstatsEnrichAnalysis" of the result path.
646 648
 
647 649
 \subsubsection{Association with sample variables}
... ...
@@ -670,16 +672,16 @@ resQual <- qualVarAnalysis(params=params, icaSet=icaSetMainz,
670 672
 The function creates an HTML file "qualVarAnalysis/qualVar.htm", containing p-values and links toward  boxplots. 
671 673
 If you would  like to plot densities rather than boxplots, please use \verb$'typePlot=density'$.
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-An example of  boxplot is represented below for the third component and the ER status. As suggested by the heatmap, the distribution of the samples on this component is strongly associated with their ER status, the latter coming up at the positive end of the component.      
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+An example of  boxplot is represented below for the second component and the ER status. As suggested by the heatmap, the distribution of the samples on this component is strongly associated with their ER status, the latter coming up at the positive end of the component.      
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 %As in \ref{fig:exhisto} we can see that the muscle-invasive tumors (T2+) seem to over-express the contributing genes of this component.
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 \begin{figure}[htbp]
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   \centering 
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-\includegraphics[width=0.8\linewidth]{mainz/qualVarAnalysis/plots/3_er.png}
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+\includegraphics[width=0.8\linewidth]{mainz/qualVarAnalysis/plots/2_er.png}
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   \caption[Example of boxplot representing the distribution of breast tumors on the second component according to their ER status.]{ 
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 Example of boxplot representing the distribution of ER status on the third component.
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 The Wilcoxon test $p$-value is available in the title of the plot. 
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-The legend indicates that the ER- tumors are represented in beige while ER+ are represented in light pink. The number of tumors in each group is given between brackets. 
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-The gene witness is \textit{KRT16}. Each tumor sample is represented as a square point in the vertical line at the left end of the boxplots whose color denotes its  amount of expression of the \textit{KRT16} gene. The scale of these colors is denoted by a legend at the upper right of the graph.}
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+The legend indicates that the ER+ tumors are represented in beige while ER- are represented in light pink. The number of tumors in each group is given between brackets. 
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+The witness gene is \textit{KRT16}. Each tumor sample is represented as a square point in the vertical line at the left end of the boxplots whose color denotes its  amount of expression of the \textit{KRT16} gene. The scale of these colors is denoted by a legend at the upper right of the graph.}
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   \label{fig:exdens}
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 \end{figure}
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... ...
@@ -710,7 +712,7 @@ The function creates a HTML file "quantVar.htm" containing correlations values,
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 \begin{figure}[htbp]
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   \centering 
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 \includegraphics[width=0.8\linewidth]{mainz/quantVarAnalysis/plots/2_age.png}
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-  \caption[Scatter plot of age vs sample contributions.]{Scatter plot of AGE vs sample contributions. The gene witness is \textit{KRT16}. At the bottom of the plot, each sample is represented by a square point whose colour denotes the expression value of the \textit{KRT16} gene. The scale of these colors is denoted by a legend at the upper right of the graph. Note that the gene expression profiles were centered to have mean zero. }
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+  \caption[Scatter plot of age vs sample contributions.]{Scatter plot of AGE vs sample contributions. The witness gene is \textit{KRT16}. At the bottom of the plot, each sample is represented by a square point whose colour denotes the expression value of the \textit{KRT16} gene. The scale of these colors is denoted by a legend at the upper right of the graph. Note that the gene expression profiles were centered to have mean zero. }
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   \label{fig:exscatter}
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 \end{figure}
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... ...
@@ -752,7 +754,7 @@ Here is the example of the distribution of the tumors according to their ER stat
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 <<stageOnHist, echo=TRUE, results=hide, print=FALSE, eval=FALSE>>=
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 ## plot the positions of the samples on the second component according to their ER status
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 ## (in a file "er.pdf") 
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-plotPosAnnotInComp(icaSet=icaSetMainz, keepVar=c("er"), keepComp=2,  
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+plotPosAnnotInComp(icaSet=icaSetMainz, params=params, keepVar=c("er"), keepComp=2,  
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                    funClus="Mclust")
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 @ 
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 \begin{figure}[htbp]
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