Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • slois
    Junior Member
    • Nov 2013
    • 2

    #1

    VarScan2. Strange behaviour of somatic-p-value

    Hi community of SEQanswers,

    I'm running varscan2 for somatic mutation calling following the commands of its documentation: mpileup + varscan somatic

    In order to test the effect of --somatic-p-value parameter in the number of variants called by varscan somatic, I have tried different p-values and logically I obtain different number of variants in the final output. However, checking the differents outputs I see that the value of the field somatic p-value (col. 15 in the tabulated output or value SPV of col. INFO in the VCF format) is greater than the specified threshold. I expected that all the somatic variants of the output has a somatic-p-value below my threshold.

    Is there any reason to explain this behaviour? Am I doing something wrong? I would like to know whether --somatic-p-value parameter is filtering variants according its value or not.

    Thank you in advance for you answers,
    Last edited by slois; 04-03-2014, 08:01 AM.
  • slois
    Junior Member
    • Nov 2013
    • 2

    #2
    Hi,

    According the documentation:
    --p-value - P-value threshold to call a heterozygote [0.99]
    --somatic-p-value - P-value threshold to call a somatic site [0.05]

    Are these parameters filtering the variants identified according its corresponding values?

    Different tests applied using different somatic p-values: (outputs are modified to fit in this post). Last two columns are the variant-p-value and somatic-p-value:

    Results obtained for --somatic-p-value = 1
    result1.snp:chr1 1019753 A G 10 0 0% A 8 2 20% R Somatic 1.0 0.2368
    result1.snp:chr1 1111270 C T 36 2 5,26% C 52 17 24,64% Y Somatic 1.0 0.0090
    result1.snp:chr1 1111272 C T 35 2 5,41% C 55 16 22,54% Y Somatic 1.0 0.0182

    Results obtained for --somatic-p-value = 0.05 (Default)
    result2.snp:chr1 1019753 A G 10 0 0% A 8 2 20% R Somatic 1.0 0.2368
    result2.snp:chr1 1111270 C T 36 2 5,26% C 52 17 24,64% Y Somatic 1.0 0.0090
    result2.snp:chr1 1111272 C T 35 2 5,41% C 55 16 22,54% Y Somatic 1.0 0.0182

    Results obtained for --somatic-p-value = 0.01
    result3.snp:chr1 1019753 A G 10 0 0% A 8 2 20% R Somatic 1.0 0.2368
    result3.snp:chr1 1111270 C T 36 2 5,26% C 52 17 24,64% Y Somatic 1.0 0.0090

    Results obtained for --somatic-p-value = 0.001
    result4.snp:chr1 1019753 A G 10 0 0% A 8 2 20% R Somatic 1.0 0.2368

    Thank you

    Comment

    • vd4mindia
      Member
      • May 2013
      • 40

      #3
      I also have a same question,

      I have also ran the VarScan2 on my normal/tumor pair with p-value as 0.1 somatic p-value 0.001 and then ran the somatic filter with parameters -min-strands2 2 -min-avg-qual 30. I get very less variants that are significant. I get around 250 variants. Can anyone tell me what is the standard threshold for the p-value and somatic pvalue? Which is the paramter you have been supplying?

      Comment

      Latest Articles

      Collapse

      • SEQadmin2
        Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
        by SEQadmin2



        CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

        Despite this, “CRISPR helped turn genome editing from a specialized technique into
        ...
        07-31-2026, 11:01 AM
      • SEQadmin2
        Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
        by SEQadmin2


        Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

        The systematic characterization of the human proteome has
        ...
        07-20-2026, 11:48 AM
      • 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

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by SEQadmin2, Today, 07:41 AM
      0 responses
      9 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 08-03-2026, 10:13 AM
      0 responses
      25 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 07-31-2026, 02:55 AM
      0 responses
      38 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 07-24-2026, 12:17 PM
      0 responses
      25 views
      0 reactions
      Last Post SEQadmin2  
      Working...