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PRANA:一个用于存在附加协变量的差异共表达网络分析的 R 包。

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates.

机构信息

Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA.

Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA.

出版信息

BMC Genomics. 2023 Nov 16;24(1):687. doi: 10.1186/s12864-023-09787-3.

DOI:10.1186/s12864-023-09787-3
PMID:37974076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10652545/
Abstract

BACKGROUND

Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.

RESULTS

We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.

CONCLUSION

Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .

摘要

背景

测序技术的进步和成本降低使得各种用于 RNA 测序数据的统计方法得以出现,包括差异共表达网络分析(或差异网络分析)。该方法的一个主要优点是它考虑了基因之间或基因内部的相互作用,并且不需要对生物途径有既定的了解。到目前为止,还没有任何现有软件可以合并协变量,如果它们是进行差异网络分析时的混杂因素,则需要对其进行调整。

结果

我们开发了一个 R 包 PRANA,用户可以轻松地包含多个协变量。该软件包中的主要 R 函数利用了一种新的伪值回归方法,用于 RNA 测序数据的差异网络分析。该软件还包含了用于提取所有或特定基因的调整后 p 值和系数估计值的补充 R 函数,以及用于从输出中识别在生物学上不同条件下的不同连接(DC,以下简称)的基因名称。

结论

在此,我们通过 COPD 真实数据展示了该软件包的应用。PRANA 可通过 GPL-3 许可证在 CRAN 存储库中获得:https://cran.r-project.org/web/packages/PRANA/index.html 。

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Differential Network Analysis: A Statistical Perspective.差异网络分析:统计学视角
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BMC Bioinformatics. 2023 Jan 9;24(1):8. doi: 10.1186/s12859-022-05123-w.
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Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.通过亚型感知 RNA-seq 深度学习模型提高吸烟状况预测能力。
PLoS Comput Biol. 2021 Oct 11;17(10):e1009433. doi: 10.1371/journal.pcbi.1009433. eCollection 2021 Oct.
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