Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • chariko
    Member
    • Jun 2010
    • 56

    RnaSeq vs Microarray correlation

    Hi all,

    I have an experiment with 80 samples both of them run with microarray and RnaSeq. I want to correlate the results between the two technologies.

    I received the results from the RnaSeq experiment in two ways:

    a) Raw data (fastq files)
    b)Table of ensembl id´s counts (no idea how this analysis was done).

    I did the analysis in two ways:

    1)
    For the RnaSeq experiment I took the ensembl id´s counts, translated them into Gene Symbol identifiers (various ensembl Id`s derived in the same Gene Symbol so I just used one of them randomly selected and the other ensembl id´s were discarded), and normalized them with voom (log2 with some modifications).

    For the Microarray experiment I normalized them (RMA) using a curated database (hgu133plus2hsentrezgcdf). I translated the entrez id´s probes into Gene Symbol identifiers.

    I did the correlation between microarray and RnaSeq (cor.test, two sides, spearman method)) and I obtained good results for all the samples (07-0.9). Attached figure 1 with the scatterplot of sample 1.

    2)
    I took the fastq files and analyzed them taking into account the HG19 GRC 37 RefSeq as reference. I translated the refseq id´s into gene symbol. I randomly selected one gene symbol per refseq id.

    Same microarray data was used for the correlation.

    I did the same correlatin as before but the results were worse (0.3-48). Figure 2 shows scatterplot of sample 1.

    My question is, does anybody have a clue about why starting with refseq id´s is not giving the same good results? Any clues?

    Thanks in advance.
    Attached Files

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...