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cypress:一个用于细胞类型特异性差异表达分析能力评估的 R/Bioconductor 包。

cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.

机构信息

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, United States.

Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, United States.

出版信息

Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae511.

Abstract

SUMMARY

Recent methodology advances in computational signal deconvolution have enabled bulk transcriptome data analysis at a finer cell-type level. Through deconvolution, identifying cell-type-specific differentially expressed (csDE) genes is drawing increasing attention in clinical applications. However, researchers still face a number of difficulties in adopting csDE genes detection methods in practice, especially in their experimental design. Here we present cypress, the first experimental design and statistical power analysis tool in csDE genes identification. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of multiple influencing factors, by various statistical metrics, to help researchers optimize experimental design and conduct power analysis.

AVAILABILITY AND IMPLEMENTATION

cypress is an open-source R/Bioconductor package at https://bioconductor.org/packages/cypress/.

摘要

摘要

最近在计算信号解卷积方面的方法学进展,使得更精细的细胞类型水平的批量转录组数据分析成为可能。通过解卷积,鉴定细胞类型特异性差异表达(csDE)基因在临床应用中越来越受到关注。然而,研究人员在实际中采用 csDE 基因检测方法仍然面临许多困难,特别是在实验设计方面。在这里,我们提出了 cypress,这是第一个用于鉴定 csDE 基因的实验设计和统计功效分析工具。该工具可以可靠地模拟纯化的细胞类型特异性(CTS)分布、细胞类型组成、生物学和技术变化,为批量 RNA-seq 卷积和解卷积提供高保真度的模拟器。cypress 通过各种统计指标进行模拟并评估多个影响因素的影响,以帮助研究人员优化实验设计和进行功效分析。

可用性和实现

cypress 是一个位于 https://bioconductor.org/packages/cypress/ 的开源 R/Bioconductor 包。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7836/11357793/a3855fae1998/btae511f1.jpg

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