Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • narges
    Member
    • Aug 2012
    • 29

    RNA-seq sample clustering using NMF package

    I want to cluster RNA-seq samples (417 samples) using based on the expression levels of a group of selected genes using NMF method. Having a matrix (mat) of expression levels with sample names as columns and gene names as rows I tried following command from Bioconductor NMF package:

    Code:
    library(NMF)
    Code:
    res <- nmf(mat, 2:10, nrun = 200, seed = 123456)
    I figure out that rank=4 is the optimum.
    First do you think it is a reasonable way of sample clustering?
    If so, should I normalize/transform the expression levels before clustering (like TMM normalization or log or asinh transformations)?
    And finally, I need to know the names of samples in each calculated clusters. Using command "basisnames" I got NULL. What command should I try to get the samples orders in clusters?

    Thanks for the help.

Latest Articles

Collapse

  • SEQadmin2
    Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
    by SEQadmin2



    Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
    ...
    07-09-2026, 11:10 AM
  • SEQadmin2
    Cancer Drug Resistance: The Lingering Barrier to Rising Survival
    by SEQadmin2



    Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

    There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
    07-08-2026, 05:17 AM
  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
    07-01-2026, 11:43 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by SEQadmin2, 07-13-2026, 10:26 AM
0 responses
15 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-09-2026, 10:04 AM
0 responses
29 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-08-2026, 10:08 AM
0 responses
16 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-07-2026, 11:05 AM
0 responses
33 views
0 reactions
Last Post SEQadmin2  
Working...