Entering edit mode
6.5 years ago
drshahzadbhatti
•
0
Dear
I need some your valuable comments on my cluster analysis as i have very little knowledge of cluster analysis. What will be the best interpretation of it.
To get help, please explain what your data is, what the question you're trying to address is and what analysis steps you've taken. This is in addition to providing whichever image we're supposed to look at.
May kindly send me your email id. I will send the deprogram on it.
regards
Shahzad
The dendrogram alone is not sufficient, we need the context, i.e. the answers to the questions I asked above. If the image doesn't show up here, upload it somewhere and give the link here. Others may be better able to answer your question than me.
Shahzad, you can share images/figures by uploading them her: https://imgbb.com/ Then, obtain the HTML URL and paste it into a reply here.
Also, as my colleague says, you should provide more information on the programs you're using, what the experiment is about (RNA-seq? identity-by-state?; microarray?), etc.
Dear Kevin thanks for your help. I have pasted the link below. As for my experiments is concern, I want to find out the relationship of CAG and GCC repeats length with testosterone and oxytocin other parameters listed below in diabetic premature ejaculatory dysfunction patients. the parameters that i have studied is
I have draw hierarchical cluster for clustering of theses parameters and need your help to elaborate it.
https://ibb.co/cYbYSb
Hi Shahzad,
You appear to have posted the same comment 4 times, so, I have removed 3 of them (and also tidied up your comment, above). Before leaving the page after making a comment, you should check how your comment appears. If it is not formatted correctly, you can edit it.
Your study sounds very interesting. Thank you for providing the dendrogram. It is very small and I cannot read the variable names. However, I can provide the following interpretation:
1) The person who developed the dendrogram appears to have decided that there are 3 main groups in the data, as indicated by the positioning of the dotted horizontal line. These 3 groups consist of:
2) The 2 variables on the left of the dendrogram are almost identical to each other
3) The red- and blue-shaded groups of variables are more similar to each other than they are to the 4 variable on the left
A key point about dendrograms, Shahzad, is the height of the vertical bars and where the bars of 2 variables (or variable groups) meet. For example, I can immediately see that the 2 variables on the left are almost identical to each other because their vertical bars meet/merge at a very low height. On the other hand, the vertical bar for all 4 variables on the left merges with the other 2 main groups in the dendrogram at the maximum possible height at the top of the dendrogram, indicating that these 4 variables are very 'dissimilar', i.e., different, from these other 2 groups.
A dendrogram is usually a graphical representation of what is called a 'distance matrix', i.e., Euclidean distance from one sample to another.
You should also tell how you processed the data for clustering: did you preprocess (e.g. standardize) the variables ? what distance measure did you use ? I can see a potential issue if you went ahead and computed the Euclidean distance on the "raw" values because you have a mix of different variable types: category 1 are interval scale (i.e. numeric) data, category 2 are ordinal data (I don't know about 3-5).
dear i have create a more clear image may kindly see it.
https://ibb.co/gp38Sb