dendrogram


Generating a heatmap that depicts the clusters in a dataset using hierarchical clustering in R

ε祈祈猫儿з 提交于 2020-01-28 05:01:11
问题 I am trying to take my dataset which is made up of protein dna interaction, cluster the data and generate a heatmap that displays the resulting data such that the data looks clustered with the clusters lining up on the diagonal. I am able to cluster the data and generate a dendrogram of that data however when I generate the heatmap of the data using the heatmap function in R, the clusters are not visible. If you look at the first 2 images one is of the dendrogram I am able to generate, the

Generating a heatmap that depicts the clusters in a dataset using hierarchical clustering in R

别等时光非礼了梦想. 提交于 2020-01-28 05:01:05
问题 I am trying to take my dataset which is made up of protein dna interaction, cluster the data and generate a heatmap that displays the resulting data such that the data looks clustered with the clusters lining up on the diagonal. I am able to cluster the data and generate a dendrogram of that data however when I generate the heatmap of the data using the heatmap function in R, the clusters are not visible. If you look at the first 2 images one is of the dendrogram I am able to generate, the

R cluster analysis and dendrogram with correlation matrix

℡╲_俬逩灬. 提交于 2020-01-21 09:21:50
问题 I have to perform a cluster analysis on a big amount of data. Since I have a lot of missing values I made a correlation matrix. corloads = cor(df1[,2:185], use = "pairwise.complete.obs") Now I have problems how to go on. I read a lot of articles and examples, but nothing really works for me. How can I find out how many clusters are good for me? I already tried this: dissimilarity = 1 - corloads distance = as.dist(dissimilarity) plot(hclust(distance), main="Dissimilarity = 1 - Correlation",

Color branches of dendrogram using an existing column

柔情痞子 提交于 2020-01-21 08:55:17
问题 I have a data frame which I am trying to cluster. I am using hclust right now. In my data frame, there is a FLAG column which I would like to color the dendrogram by. By the resulting picture, I am trying to figure out similarities among various FLAG categories. My data frame looks something like this: FLAG ColA ColB ColC ColD I am clustering on colA , colB , colC and colD . I would like to cluster these and color them according to FLAG categories. Ex - color red if 1, blue if 0 (I have only

Color branches of dendrogram using an existing column

筅森魡賤 提交于 2020-01-21 08:52:27
问题 I have a data frame which I am trying to cluster. I am using hclust right now. In my data frame, there is a FLAG column which I would like to color the dendrogram by. By the resulting picture, I am trying to figure out similarities among various FLAG categories. My data frame looks something like this: FLAG ColA ColB ColC ColD I am clustering on colA , colB , colC and colD . I would like to cluster these and color them according to FLAG categories. Ex - color red if 1, blue if 0 (I have only

Calculate ordering of dendrogram leaves

此生再无相见时 提交于 2020-01-13 06:01:08
问题 I have five points and I need to create dendrogram from these. The function 'dendrogram' can be used to find the ordering of these points as shown below. However, I do not want to use dendrogram as it is slow and result in error for large number of points (I asked this question here Python alternate way to find dendrogram). Can someone points me how to convert the 'linkage' output (Z) to the "dendrogram(Z)['ivl']" value. >>> from hcluster import pdist, linkage, dendrogram >>> import numpy >>>

How I can hide the root element in a dendogram d3

*爱你&永不变心* 提交于 2020-01-05 07:43:18
问题 I have the following code var m = [20, this.settings.get("margin_right"), 20, this.settings.get("margin_left")], w = width - m[1] - m[3], h = height - m[0] - m[2], i = 0; var tree = d3.layout.tree() .size([h, w]); var diagonal = d3.svg.diagonal() .projection(function(d) { return [d.y, d.x]; }); var vis = d3.select(this.el).append("svg:svg") .attr("width", w + m[1] + m[3]) .attr("height", h + m[0] + m[2]) .append("svg:g") .attr("transform", "translate(" + m[3] + "," + m[0] + ")"); data.x0 = h

Some questions on dendrogram - python (Scipy)

孤人 提交于 2020-01-03 16:45:11
问题 I am new to scipy but I managed to get the expected dendrogram. I am some more questions; In the dendrogram, distance between some points are 0 but its not visible due to image border. How can I remove the border and make the lower limit of y-axis to -1 , so that it is clearly visible. e.g. distance between these points are 0 (13,17), (2,10), (4,8,19) How can I prune/truncate on a particular distance. for e.g. prune at 0.4 How to write these clusters(after pruning) to a file My python code:

Interpreting the output of SciPy's hierarchical clustering dendrogram? (maybe found a bug…)

泄露秘密 提交于 2020-01-02 07:11:49
问题 I am trying to figure out how the output of scipy.cluster.hierarchy.dendrogram works... I thought I knew how it worked and I was able to use the output to reconstruct the dendrogram but it seems as if I am not understanding it anymore or there is a bug in Python 3 's version of this module. This answer, how do I get the subtrees of dendrogram made by scipy.cluster.hierarchy, implies that the dendrogram output dictionary gives dict_keys(['icoord', 'ivl', 'color_list', 'leaves', 'dcoord']) w/

R cut dendrogram into groups with minimum size

无人久伴 提交于 2020-01-01 03:22:30
问题 Is there an easy way to calculate lowest value of h in cut that produces groupings of a given minimum size? In this example, if I wanted clusters with at least ten members each, I should go with h = 3.80 : # using iris data simply for reproducible example data(iris) d <- data.frame(scale(iris[,1:4])) hc <- hclust(dist(d)) plot(hc) cut(as.dendrogram(hc), h=3.79) # produces 5 groups; group 4 has 7 members cut(as.dendrogram(hc), h=3.80) # produces 4 groups; no group has <10 members Since the

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