New features in Ver. 9.0

Upgrade to version.9.0, you can be used the following new features.

VCF file import

Principal Component Analysis

  • Scatter plot is using Eigenvectors. The horizontal axis is first principal component and the vertical axis is second principal component.

    You can confirm samples with outliers.
  • Principal Component Analysis

     

    sa_squareManhattan plot

    • p-value is calculated from case-control study by using NGS Data, and the p-value is showed on Manhattan plot.
      In the lower part of the display, It is a statistical value of SNP. This value was selected on the Manhattan plot.
    • These values (p-value, chi-square, degrees of freedom and effect size) are calculated from Genotype, Allele, Recessive and Dominant models.

    Manhattan plot

    Effect size

    • Effect size is refers to the magnitude of effect of statistical test, there is such as “Standardized difference between two groups” and “Correlation measures of effect size”.

      The larger the absolute value, it indicates that effect is large. For example, correlation measures of effect size is phi(Φ) and Cramer’s V(V).
    • Correlation measures of effect size between two variables (2 x 2) is using the chi-square test.
      SNPAlyze calculates the effect size from each contingency table of 4 genetic models: Genotype, Allele, Recessive and Dominant.
    • Effect size is calculated as follows:

                                                          effect_size_equation3

                where χ2 =chi-square value, N =total number of subjects, k = smaller number of rows or columns.

     


    Related information & Links

    Please look at SNPAlyze top or product overview about the outline of the whole product.

 

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