• 我要登录|
  • 免费注册
    |
  • 我的丁香通
    • 企业机构:
    • 成为企业机构
    • 个人用户:
    • 个人中心
  • 移动端
    移动端
丁香通 logo丁香实验_LOGO
搜实验

    大家都在搜

      大家都在搜

        0 人通过求购买到了急需的产品
        免费发布求购
        发布求购
        点赞
        收藏
        wx-share
        分享

        Microarray Data Analysis: Comparing Two Population Means

        互联网

        386
        Scientists employing microarray profiling technology to compare sample sets generate data for a large number of endpoints. Assuming the experimental design minimized sources of bias, and the analytical technology was reliable, precise, and accurate, how does the experimentalist determine which endpoints are meaningfully different between the groups? Comparison of two population means for individual analysis measurements is the most common statistical problem associated with microarray data analysis. This chapter focuses on the hands-on procedures using SAS software to describe how to choose statistical methods to find the statistically significantly different endpoints between two groups of data generated from reverse phase protein microarrays. The four methods outlined are: (a) two-sample t -test, (b) Wilcoxon rank sum test, (c) one-sample t -test, and (d) Wilcoxon signed rank test. Two sample t -test is used for two independently normally distributed groups. One-sample t -test is used for a normally distributed difference of paired observations. Wilcoxon rank sum test is considered a nonparametric version of the two-sample t -test, and Wilcoxon signed rank test is considered a nonparametric version of the one-sample t -test.
        ad image
        提问
        扫一扫
        丁香实验小程序二维码
        实验小助手
        丁香实验公众号二维码
        扫码领资料
        反馈
        TOP
        打开小程序