Entering edit mode
11.3 years ago
Ngsnewbie
▴
380
I have 8 samples ( of which 4 are pre treatment and 4 are post treatment ) where the sample names are unknown , means i dont know what samples belongs to pre treatment and what belongs to post treatment. The gene expression values (count value as well as FPKM) are available. So the objective is be divide them into two groups (4 sample each). What are the best approches to classify on the basis of gene expression values. I have tried hierarchical clustering based on euclidean distance but i am not comfortable with the results. I want to know other algorithms, tools or methodology that can serve best for such classification. Thanks.
This is really an open ended question as there are probably a dozen methods of clustering your reads: What is your research question and what is your criteria for comparing your transcripts? I'm a little confused also: you have 4 control and 4 treatment samples; you don't know which ones are which ("sample names are unknown")? What does "these have to be divided into two groups" mean?
Yes i dont know which one are which !! , and these 8 have to segregate into two groups pre-treatment and post-treatment. So the query is: I have 8 samples (of which 4 belongs to pre-treatment and 4 belongs to post treatment, but i dont know which sample belongs to which one). So i am finding out the best way to cluster/classify these 8 samples into these two group.
Note: I have edited the language of my question in order to make it comprehensive.
I don't think it's a good idea to label unknown samples based on gene expression clustering. My best suggestion is to start the sequencing over and make sure you know which samples are your control and which samples are your treatment.