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Percentile01:18

Percentile

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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Quantile index predictors using R package hyper.gam.

Tingting Zhan1, Misung Yi2, Inna Chervoneva1

  • 1Division of Biostatistics & Bioinformatics, Department of Pharmacology, Physiology & Cancer Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, United States.

Bioinformatics (Oxford, England)
|July 30, 2025
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Summary
This summary is machine-generated.

This study introduces hyper.gam, an R package for discovering functional protein biomarkers from single-cell expression data. It enables the use of entire protein expression distributions for robust biomarker identification.

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Area of Science:

  • Biomedical research
  • Computational biology
  • Biostatistics

Background:

  • Single-cell protein expression analysis is crucial in biomedical research, particularly for phenotyping tumor microenvironment cells.
  • Functional protein biomarkers require quantitative analysis of expression levels, but methods for utilizing full expression distributions are limited.

Purpose of the Study:

  • To develop a supervised learning framework for deriving biomarkers from single-cell expression data quantiles.
  • To provide a user-friendly R package (hyper.gam) for analyzing heterogeneous protein expression levels.

Main Methods:

  • The hyper.gam R package converts single-cell data into sample quantile functions.
  • Scalar-on-function regression models are employed to estimate an integrand surface.
  • The estimated surface generates quantile index predictors for new datasets.

Main Results:

  • The hyper.gam package offers a supervised learning framework for biomarker discovery using single-cell quantile functions.
  • It provides tools for estimating integrand surfaces and defining quantile index predictors.
  • The package includes user-friendly interfaces and visualization tools for exploring results.

Conclusions:

  • Hyper.gam facilitates the development of novel functional protein biomarkers by leveraging complete single-cell expression distributions.
  • The package addresses the need for methods that account for expression heterogeneity in tissues.
  • It is applicable to various single-cell data types beyond protein expression.