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Genome Biology (2024) 25:119 Genome Biology  \n[https://doi.org/10.1](https://doi.org/10.1) 186/s13059‑024‑03263‑1  \nMETHOD Open Access  \nSCIPAC: quantitative estimation of cell‑phenotype associations  \nDailin Gan1, Yini Zhu2, Xin Lu2,3 and Jun Li 1*  \n*Correspondence: [jun.li@nd.edu](jun.li@nd.edu)  \n1 Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame 46556, IN, USA  \n2 Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame 46556, IN, USA  \n3 Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis 46202, IN, USA  \nAbstract  \nNumerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC’s accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.  \nKeywords: Phenotype association, Single cell, RNA sequencing, Cancer research  \nBackground  \nSingle-cell RNA sequencing (scRNA-seq) technologies are revolutionizing biomedical research by providing comprehensive characterizations of diverse cell populations in heterogeneous tissues [1, 2]. Unlike bulk RNA sequencing (RNA-seq), which measures the average expression profile of the whole tissue, scRNA-seq gives the expression profiles of thousands of individual cells in the tissue [3–7]. Based on this rich data, cell types may be discovered/determined in an unsupervised (e.g., [8, 9]), semi-supervised (e.g., [10–13]), or supervised manner (e.g., [14–16]). Despite the fast development, there are still limitations with scRNA-seq technologies. Notably, the cost for each scRNA-seq experiment is still high; as a result, most scRNA-seq data are from a single or a few biological samples/tissues. Very few datasets consist of large numbers of samples with different phenotypes, e.g., cancer vs. normal. This places great difficulties in determining how a cell type contributes to a phenotype based on single-cell studies (especially if the cell type is discovered in a completely unsupervised manner or if people have limited knowledge of this cell type). For example, without having single-cell data from multiple cancer patients and multiple normal controls, it could be hard to computationally infer whether a cell type may promote or inhibit cancer development. However, such  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. ","cbCaiiW752BRhlLO","https://ap.wps.com/l/cbCaiiW752BRhlLO","pdf",4531301,23,"English","# Abstract\n# Background","[{\"question\":\"What problem does SCIPAC solve in single-cell RNA-seq studies?\",\"answer\":\"SCIPAC targets the fundamental difficulty of determining associations between individual cells and phenotypes, such as cancer, when using single-cell RNA-seq data.\"},{\"question\":\"How does SCIPAC quantify the association between a cell and a phenotype?\",\"answer\":\"SCIPAC quantitatively estimates an association strength for each cell and also outputs a p-value for every association.\"},{\"question\":\"How was SCIPAC evaluated and what practical advantages are claimed?\",\"answer\":\"Results are demonstrated on simulated data for accuracy and on four real cancerous or noncancerous datasets for interpretation and hypothesis generation. The method is described as requiring minimal tuning and running computationally very fast.\"}]","SCIPAC: quantitative estimation of cell-phenotype associations | PDF",1789989558,58]