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Chi-square Analysis

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Related Experiment Video

Updated: Jun 26, 2025

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

SCIPAC: quantitative estimation of cell-phenotype associations.

Dailin Gan¹, Yini Zhu², Xin Lu^2,3

¹Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA.

|May 14, 2024

Summary

This summary is machine-generated.

SCIPAC is a new algorithm that quantifies the link between cells and phenotypes like cancer in single-cell RNA sequencing data. This fast, accurate tool aids in data interpretation and hypothesis generation for biological research.

Keywords:

Cancer research Phenotype association RNA sequencing Single cell

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Last Updated: Jun 26, 2025

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Published on: March 24, 2023

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables high-resolution cellular analysis.
Identifying cell-phenotype associations in scRNA-seq data remains a challenge.

Purpose of the Study:

To develop SCIPAC, the first algorithm for quantitatively estimating cell-phenotype associations in scRNA-seq data.
To provide a statistically robust method (p-value) for these associations across diverse phenotypes.

Main Methods:

SCIPAC algorithm development for quantitative association estimation.
Validation using simulated datasets.
Application to four real-world cancerous and noncancerous scRNA-seq datasets.

Main Results:

SCIPAC accurately estimates cell-phenotype associations.
The algorithm provides p-values for statistical significance.
SCIPAC successfully interpreted real datasets and generated novel hypotheses.

Conclusions:

SCIPAC offers a novel, fast, and computationally efficient solution for cell-phenotype association analysis.
The algorithm requires minimal tuning and is broadly applicable.
SCIPAC facilitates deeper insights and new research directions in scRNA-seq data analysis.