Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.
Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.
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DOI：
10.1186/s13059-023-03077-7
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发表时间：
2023-10-20
期刊：
影响因子：
12.3
通讯作者：
Greene, Casey S.
中科院分区：
文献类型：
--
作者：
Hippen, Ariel A.;Omran, Dalia K.;Weber, Lukas M.;Jung, Euihye;Drapkin, Ronny;Doherty, Jennifer A.;Hicks, Stephanie C.;Greene, Casey S.
单细胞基因表达谱为了解肿瘤异质性和肿瘤微环境提供了独特的机会。由于成本和可行性，分析大块肿瘤仍然是主要的人群规模分析策略。许多算法可以使用单细胞剖面对这些肿瘤进行反卷积来推断它们的组成。虽然实验选择不会改变肿瘤的真实潜在成分，但它们会影响检测产生的测量结果。我们通过使用多种策略生成了具有配对表达谱的高级别浆液性卵巢肿瘤数据集，以检查实验因素对下游肿瘤反卷积方法结果的影响程度。我们发现，将样本池用于单细胞测序和随后的解复用具有最小的影响。我们确定解离诱导的差异会影响细胞组成，从而导致可能损害某些反卷积算法基础假设的变化。我们还观察到mRNA富集方法的差异，这在两种数据类型之间引入了额外的差异。我们还发现实验因素改变了细胞组成的估计，并且影响因方法而异。以往反褶积方法的基准在很大程度上忽略了实验因素。我们发现这些方法对实验因素的稳健性各不相同。我们为寻求产生下一代反褶积方法的方法开发者和设计使用反褶积研究肿瘤异质性的实验的科学家提供建议。在线版本包含补充材料，下载地址：10.1186/s13059-023-03077-7。
Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity. The online version contains supplementary material available at 10.1186/s13059-023-03077-7.
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影响因子：
64.8
作者：
通讯作者：
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影响因子：
--
作者：
Rodriguez de la Fuente L;Law AMK;Gallego-Ortega D;Valdes-Mora F
通讯作者：
Valdes-Mora F
DOI：
10.1126/science.abl5197
发表时间：
2022-05-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Domínguez Conde C;Xu C;Jarvis LB;Rainbow DB;Wells SB;Gomes T;Howlett SK;Suchanek O;Polanski K;King HW;Mamanova L;Huang N;Szabo PA;Richardson L;Bolt L;Fasouli ES;Mahbubani KT;Prete M;Tuck L;Richoz N;Tuong ZK;Campos L;Mousa HS;Needham EJ;Pritchard S;Li T;Elmentaite R;Park J;Rahmani E;Chen D;Menon DK;Bayraktar OA;James LK;Meyer KB;Yosef N;Clatworthy MR;Sims PA;Farber DL;Saeb-Parsy K;Jones JL;Teichmann SA
通讯作者：
Teichmann SA
影响因子：
4
作者：
Dai L;Song K;Di W
通讯作者：
Di W
DOI：
10.1007/978-1-4939-9240-9_2
发表时间：
2019-01-01
期刊：
SINGLE CELL METHODS: SEQUENCING AND PROTEOMICS
影响因子：
--
作者：
Braga, Felipe A. Vieira;Miragaia, Ricardo J.
通讯作者：
Miragaia, Ricardo J.