machalen/QualityGraphsOligo
Make quality graphs for microarray data based on objects obtained with oligo normalization.

Package index
Search the machalen/QualityGraphsOligo package
Vignettes
Functions
13
Source code
11
Man pages
12
Home
/
GitHub
/
machalen/QualityGraphsOligo
/
many.clusters_V: Function to make many clusters with different methods and...

many.clusters_V: Function to make many clusters with different methods and...
In machalen/QualityGraphsOligo: Make quality graphs for microarray data based on objects obtained with oligo normalization.

Usage Arguments Examples

Usage

1
many.clusters_V(x, resultsDir, Filename, Title, parameters, toPDF = TRUE, conditions = NULL, colors = NULL)

Arguments

x
resultsDir
Filename
Title
parameters
toPDF
conditions
colors

Examples

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, resultsDir, Filename, Title, parameters, toPDF = TRUE, 
    conditions = NULL, colors = NULL) 
{
    labels <- colnames(x)
    use.cor = "pairwise.complete.obs"
    if (toPDF) {
        pdf(file = file.path(resultsDir, paste(Filename, "pdf", 
            sep = ".")))
    }
    if (is.null(conditions)) {
        opt <- par(cex.main = 2, cex = parameters$ce, cex.lab = 1.5, 
            cex.axis = 1.5)
        clust.cor.ward <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "ward")
        plot(clust.cor.ward, main = Title, hang = -1)
        clust.cor.average <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "average")
        plot(clust.cor.average, main = Title, hang = -1)
        clust.cor.complete <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "complete")
        plot(clust.cor.complete, main = Title, hang = -1)
        clust.euclid.ward <- hclust(dist(t(x)), method = "ward")
        plot(clust.euclid.ward, main = Title, hang = -1)
        clust.euclid.average <- hclust(dist(t(x)), method = "average")
        plot(clust.euclid.average, main = Title, hang = -1)
        clust.euclid.complete <- hclust(dist(t(x)), method = "complete")
        plot(clust.euclid.complete, main = Title, hang = -1)
        par(opt)
    }
    else if (is.null(colors)) {
        list1 <- unique(as.character(sort(conditions)))
        ColVect <- c(brewer.pal(8, "Dark2"), brewer.pal(12, "Paired"))
        list2 <- ColVect[1:length(unique(conditions))]
        map = setNames(list2, list1)
        colors <- map[conditions]
        color_cluster <- function(hclus, condition, ce) {
            sampleDendrogram <- as.dendrogram(hclus)
            names(condition) <- hclus$labels
            sampleDendrogram <- dendrapply(sampleDendrogram, 
                function(x, batch) {
                  if (is.leaf(x)) {
                    label <- attr(x, "label")
                    attr(x, "nodePar") <- list(lab.col = as.vector(batch[label]), 
                      pch = "", cex = 0.8, lab.cex = ce)
                  }
                  x
                }, condition)
        }
        opt <- par(cex.main = 1, cex.axis = 0.8, cex = 0.8)
        clust.cor.ward <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "ward")
        clust.cor.ward <- color_cluster(clust.cor.ward, colors, 
            parameters$ce)
        plot(clust.cor.ward, main = Title, xlab = "Ward")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.cor.average <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "average")
        clust.cor.average <- color_cluster(clust.cor.average, 
            colors, parameters$ce)
        plot(clust.cor.average, main = Title, xlab = "Average")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.cor.complete <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "complete")
        clust.cor.complete <- color_cluster(clust.cor.complete, 
            colors, parameters$ce)
        plot(clust.cor.complete, main = Title, xlab = "Complete")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.ward <- hclust(dist(t(x)), method = "ward")
        clust.euclid.ward <- color_cluster(clust.euclid.ward, 
            colors, parameters$ce)
        plot(clust.euclid.ward, main = Title, xlab = "Euclidean Ward")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.average <- hclust(dist(t(x)), method = "average")
        clust.euclid.average <- color_cluster(clust.euclid.average, 
            colors, parameters$ce)
        plot(clust.euclid.average, main = Title, xlab = "Euclidean Average")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.complete <- hclust(dist(t(x)), method = "complete")
        clust.euclid.complete <- color_cluster(clust.euclid.complete, 
            colors, parameters$ce)
        plot(clust.euclid.complete, main = Title, xlab = "Euclidean Complete")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        par(opt)
    }
    else {
        list1 <- unique(as.character(sort(conditions)))
        list2 <- colors
        map = setNames(list2, list1)
        colors <- map[conditions]
        color_cluster <- function(hclus, condition, ce) {
            sampleDendrogram <- as.dendrogram(hclus)
            names(condition) <- hclus$labels
            sampleDendrogram <- dendrapply(sampleDendrogram, 
                function(x, batch) {
                  if (is.leaf(x)) {
                    label <- attr(x, "label")
                    attr(x, "nodePar") <- list(lab.col = as.vector(batch[label]), 
                      pch = "", cex = 0.8, lab.cex = ce)
                  }
                  x
                }, condition)
        }
        opt <- par(cex.main = 1, cex.axis = 0.8, cex = 0.8)
        clust.cor.ward <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "ward")
        clust.cor.ward <- color_cluster(clust.cor.ward, colors, 
            parameters$ce)
        plot(clust.cor.ward, main = Title, xlab = "Ward")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.cor.average <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "average")
        clust.cor.average <- color_cluster(clust.cor.average, 
            colors, parameters$ce)
        plot(clust.cor.average, main = Title, xlab = "Average")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.cor.complete <- hclust(as.dist(1 - cor(x, use = use.cor)), 
            method = "complete")
        clust.cor.complete <- color_cluster(clust.cor.complete, 
            colors, parameters$ce)
        plot(clust.cor.complete, main = Title, xlab = "Complete")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.ward <- hclust(dist(t(x)), method = "ward")
        clust.euclid.ward <- color_cluster(clust.euclid.ward, 
            colors, parameters$ce)
        plot(clust.euclid.ward, main = Title, xlab = "Euclidean Ward")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.average <- hclust(dist(t(x)), method = "average")
        clust.euclid.average <- color_cluster(clust.euclid.average, 
            colors, parameters$ce)
        plot(clust.euclid.average, main = Title, xlab = "Euclidean Average")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        clust.euclid.complete <- hclust(dist(t(x)), method = "complete")
        clust.euclid.complete <- color_cluster(clust.euclid.complete, 
            colors, parameters$ce)
        plot(clust.euclid.complete, main = Title, xlab = "Euclidean Complete")
        legend("topright", legend = list1, cex = parameters$ce + 
            0.2, fill = list2)
        par(opt)
    }
    if (toPDF) {
        dev.off()
    }
    return(list(Corr.ward = clust.cor.ward, Corr.avg = clust.cor.average, 
        Corr.compl = clust.cor.complete, Euclid.ward = clust.euclid.ward, 
        Euclid.avg = clust.euclid.average, Euclid.compl = clust.euclid.complete))
  }

machalen/QualityGraphsOligo documentation built on May 28, 2019, 2:32 a.m.

Related to many.clusters_V in machalen/QualityGraphsOligo...

machalen/QualityGraphsOligo index
README.md

R Package Documentation

rdrr.io home R language documentation Run R code online

Browse R Packages

CRAN packages Bioconductor packages R-Forge packages GitHub packages

We want your feedback!

Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GitHub issue tracker
ian@mutexlabs.com
Personal blog

 
Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.

Close