Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.
Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.
复制标题
使用DEDREM对单核RNA测序数据进行偏置校正的散装脑组织细胞型反卷积。
DOI：
10.1186/s12859-023-05476-w
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发表时间：
2023-09-19
影响因子：
3
通讯作者：
Farrer, Lindsay A.
中科院分区：
文献类型：
--
作者：
O'Neill, Nicholas K.;Stein, Thor D.;Hu, Junming;Rehman, Habbiburr;Campbell, Joshua D.;Yajima, Masanao;Zhang, Xiaoling;Farrer, Lindsay A.
在批量组织RNA测序中量化细胞类型丰度使研究人员能够更好地了解复杂系统。较新的去卷积方法，如MuSiC，使用来自单细胞RNA测序（scRNA-seq）数据的细胞类型特征来进行这些计算。单核RNA测序（snRNA-seq）参考数据可以代替scRNA-seq数据用于组织，如人脑，其中难以获得单细胞数据，但由于技术之间的测序差异，准确性受到影响。我们提出了一种名为“DeTREM”的MuSiC修改方案，该方案补偿了细胞类型签名和批量RNA-seq数据集之间的测序差异，以便更好地预测细胞类型分数。我们显示DeTREM在模拟和真实的人脑批量RNA测序数据集中比MuSiC更准确，具有各种细胞类型丰度估计。我们还将DeTREM与SCDC和CIBERSORTx进行了比较，这是两种最近使用scRNA-seq细胞类型签名的去卷积方法。我们发现，它们在模拟数据中表现良好，但在用于解卷积人脑数据时，产生的结果不如DeTREM准确。DeTREM提高了MuSiC的反卷积精度，并且在应用于snRNA-seq数据时优于其他反卷积方法。在scRNA-seq数据不可用的情况下，DeTREM能够实现准确的细胞类型去卷积。这种修饰改善了脑组织中细胞类型特异性效应的表征和各种条件下细胞类型丰度差异的鉴定。在线版本包含补充材料，可通过10.1186/s12859-023-05476-w获得。
Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. We propose a modification to MuSiC entitled ‘DeTREM’ which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions. The online version contains supplementary material available at 10.1186/s12859-023-05476-w.
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影响因子：
7.1
作者：
Bachstetter AD;Van Eldik LJ;Schmitt FA;Neltner JH;Ighodaro ET;Webster SJ;Patel E;Abner EL;Kryscio RJ;Nelson PT
通讯作者：
Nelson PT
单细胞RNA测序，用于研究发育，生理和疾病。
DOI：
10.1038/s41581-018-0021-7
发表时间：
2018-08
期刊：
Nature reviews. Nephrology
影响因子：
--
作者：
Potter SS
通讯作者：
Potter SS
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
DOI：
10.1126/science.aba5257
发表时间：
2021-02-19
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Kuchina A;Brettner LM;Paleologu L;Roco CM;Rosenberg AB;Carignano A;Kibler R;Hirano M;DePaolo RW;Seelig G
通讯作者：
Seelig G
影响因子：
4.3
作者：
Kang, Kai;Meng, Qian;Li, Leping
通讯作者：
Li, Leping