Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • sam007
    Junior Member
    • Jan 2019
    • 1

    #1

    Normalisation of RNAseq data from UCSC Xena Browser

    Dear all

    I am very new to this so apologies in advance..

    I am trying to teach myself the basics of R and bioinformatics and would like to attempt some analysis of TCGA data

    I have downloaded the RNA-seq data from the LUAD TCGA data set using the 'HTSeq-Counts' link from this page:



    I would like just do a simple correlation of expression of gene y with gene z

    I then want to perform Kaplan-Meier analysis of overall survival after dividing patients into high and low expression of gene y (using the median to split the cohorts)

    My questions are about normalising the data, before I perform the analyses

    1) The UCSC page explains that the data has been log(x+1) transformed. I would like to know if the raw data was normalised for library size prior to log transformation? If not, is this necessary?

    I obtained the count matrix and back-transformed the counts ((2^x) - 1) and I then summed the total counts per sample and obtained different values per sample, which makes me assume that the counts were not corrected for library size

    2) Finally, assuming that the data have not been normalised for library size (or distribution etc) what method would you suggest to normalise for my analysis. I understand I could just do TPM or could do TMM or another method such as that used by DESeq2

    Thanks in advance for any help, I did try to find the answer to q1 elsewhere but couldn't

Latest Articles

Collapse

  • SEQadmin2
    Multiomic Liquid Biopsy: What DNA/RNA Panels Reveal That DNA Alone Can’t
    by SEQadmin2



    Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?

    Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating
    ...
    10-14-2026, 09:55 AM
  • SEQadmin2
    New Genomics Technologies Take Aim at Long-Standing Limits
    by SEQadmin2


    Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.

    We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing
    ...
    09-28-2026, 10:25 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by SEQadmin2, 10-15-2026, 10:52 AM
0 responses
12 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 10-14-2026, 05:39 AM
0 responses
20 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 10-13-2026, 09:56 AM
0 responses
19 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 10-09-2026, 11:14 AM
0 responses
27 views
0 reactions
Last Post SEQadmin2  
Working...