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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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TPES: tumor purity estimation from SNVs.

Alessio Locallo1, Davide Prandi1, Tarcisio Fedrizzi1

  • 1Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento, Trento, Italy.

Bioinformatics (Oxford, England)
|May 18, 2019
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Summary

This study introduces a new computational method for estimating tumor purity (TP) using single-nucleotide variants (SNVs). This approach accurately quantifies TP, even in near-euploid tumors, improving genomic analysis for cancer research.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Tumor purity (TP) is crucial for accurate molecular and genomics assessments using next-generation sequencing (NGS).
  • Existing tools for TP quantification often fail on near-euploid tumor genomes due to reliance on somatic copy-number alterations (SCNA).

Purpose of the Study:

  • To develop and validate a novel computational method for estimating tumor purity.
  • To overcome the limitations of SCNA-based methods in near-euploid samples.

Main Methods:

  • Introduced a computational method, tumor purity estimation from single-nucleotide variants (SNVs), leveraging the allelic fraction distribution of SNVs.
  • Validated the method on over 7800 whole-exome sequencing (WXS) datasets from The Cancer Genome Atlas (TCGA).

Main Results:

  • The SNV-based method demonstrated high concordance with existing TP tools (Spearman's correlation 0.68–0.82).
  • Successfully corrected TP estimates for 1,194 (15%) pan-cancer samples, particularly those with near-euploid genomes.
  • The tool, TPES, is available as an R package.

Conclusions:

  • The SNV-based tumor purity estimation method offers a robust alternative for quantifying TP, especially in challenging near-euploid cases.
  • This advancement improves the reliability of genomic analyses in a significant proportion of cancer samples.
  • The TPES R package facilitates wider adoption and application of this improved TP estimation technique.