Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • sganesh02
    Junior Member
    • Feb 2012
    • 1

    Dealing with low read count in some samples

    Greeting Members

    I have an experiment with 4 time points and 5 biological replicates and each biological replicate has 5 technical replicates (100 samples thus)

    The first two time points are really early is the organism's life cycle and hence we often expect less reads for some genes:

    After summing the reads from the technical replicates, I get this profile (OBVIOUSLY AN ARTEFACT)
    0h GeneA 3 reads
    0h GeneA 2 reads
    0h GeneA 0 reads
    0h GeneA 1 reads
    0h GeneA 3 reads

    24h GeneA 0 reads
    24h GeneA 1 reads
    24h GeneA 2 reads
    24h GeneA 0 reads
    24h GeneA 1 reads

    But DeSeq2 analysis of DE of these genes gives me a profile like this:
    baseMean log2FoldChange lfcSE stat pvalue padj
    31.88739159 -4.026822896 0.943450084 -4.268188603 1.97E-05 0.007173227

    How do I filter for such artefacts ? Though I give an example here, my results are severely afflicted with these kind of low read count but highly significant statistics.
    Any suggestion is highly appreciated...

    I cannot set a minreadcount filter as these genes will have higher read count as the time line progresses (and will also skew the stats if I only remove it for one time point)

Latest Articles

Collapse

  • 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...
    Today, 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
  • SEQadmin2
    Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by SEQadmin2


    I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

    Here are nine questions we think about, in roughly the order they matter, before...
    06-18-2026, 07:11 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by SEQadmin2, Yesterday, 11:05 AM
0 responses
7 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-02-2026, 11:08 AM
0 responses
29 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 06-30-2026, 05:37 AM
0 responses
28 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 06-26-2026, 11:10 AM
0 responses
27 views
0 reactions
Last Post SEQadmin2  
Working...