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Two sample comparisons including zero-inflated continuous data: A parametric approach with applications to microarray

H V Kulkarni1, K P Patil2

  • 1Department of Statistics, Shivaji University, Kolhapur, MS, India.

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|February 8, 2018
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Summary
This summary is machine-generated.

New exact and parametric tests improve statistical inference for molecular biology micro-array experiments with zero-inflated data. These methods outperform traditional tests, especially with small sample sizes, enhancing biological discovery.

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Area of Science:

  • Molecular Biology
  • Biostatistics

Background:

  • Micro-array experiments frequently yield mixed distributions with a zero clump.
  • Challenges include skewed continuous data and small sample sizes, limiting traditional statistical methods.

Purpose of the Study:

  • To develop novel statistical tests for analyzing micro-array data with zero-inflated distributions.
  • To address limitations of existing methods, particularly under small sample conditions.

Main Methods:

  • Proposed an exact test for location-scale family distributions.
  • Developed Generalized Pivot Quantity (GPQ) based parametric tests for non-location-scale distributions.
  • Conducted simulation studies to assess performance.

Main Results:

  • The new exact and GPQ tests demonstrated superior size and power compared to popular two-part tests.
  • Performance advantages were most pronounced in small sample size scenarios.
  • Outperformed Wilcoxon rank sum, t-test, Kolmogorov-Smirnov, Ansari-Bradley, and Siegel-Tukey tests.

Conclusions:

  • The proposed exact and GPQ tests offer a robust solution for analyzing zero-inflated data in micro-array studies.
  • These methods enhance statistical inference quality, especially when dealing with limited sample sizes.
  • Provides more reliable comparisons between biological groups.