SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning.
SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning.
复制标题
空间转录组学中半监督学习的细胞型反褶积。
DOI：
10.1038/s42003-023-04761-x
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发表时间：
2023-04-07
影响因子：
5.9
通讯作者：
Li, Mingyao
中科院分区：
文献类型：
--
作者：
Coleman, Kyle;Hu, Jian;Schroeder, Amelia;Lee, Edward B.;Li, Mingyao
空间分辨转录组学(SRT)促进了我们对基因表达空间模式的理解，但基于空间条形码的SRT缺乏单细胞分辨率，阻碍了对单个细胞特定位置的推断。为了确定SRT中细胞类型的空间分布，我们提出了SpaDecon，一种结合了基因表达、空间位置和组织学信息的半监督学习方法，用于细胞类型的去卷积。SpaDecon通过对四个实际SRT数据集的分析，利用细胞类型的预期分布知识进行了评估。对根据基准比例构建的四个伪SRT数据集进行了定量评估。以均方误差和Jensen-Shannon散度为评价标准，我们证明了SpaDecon方法的性能优于已发表的胞格型反褶积方法。鉴于SpaDecon的准确性和计算速度，我们预计它将对SRT数据分析有价值，并将促进基因组学和数字病理学的集成。SpaDecon是一种基于半监督学习的方法，用于空间分辨转录组学(SRT)数据的细胞类型去卷积，对于大规模SRT研究来说，该方法计算速度快，内存效率高。
Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology. SpaDecon is a semi-supervised learning-based method for cell-type deconvolution of spatially resolved transcriptomics (SRT) data that is also computationally fast and memory efficient for large-scale SRT studies.
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DOI：
10.1126/science.aad0501
发表时间：
2016-04-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Tirosh I;Izar B;Prakadan SM;Wadsworth MH 2nd;Treacy D;Trombetta JJ;Rotem A;Rodman C;Lian C;Murphy G;Fallahi-Sichani M;Dutton-Regester K;Lin JR;Cohen O;Shah P;Lu D;Genshaft AS;Hughes TK;Ziegler CG;Kazer SW;Gaillard A;Kolb KE;Villani AC;Johannessen CM;Andreev AY;Van Allen EM;Bertagnolli M;Sorger PK;Sullivan RJ;Flaherty KT;Frederick DT;Jané-Valbuena J;Yoon CH;Rozenblatt-Rosen O;Shalek AK;Regev A;Garraway LA
通讯作者：
Garraway LA
影响因子：
6
作者：
Hu J;Schroeder A;Coleman K;Chen C;Auerbach BJ;Li M
通讯作者：
Li M
影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J
影响因子：
64.5
作者：
Yao Z;van Velthoven CTJ;Nguyen TN;Goldy J;Sedeno-Cortes AE;Baftizadeh F;Bertagnolli D;Casper T;Chiang M;Crichton K;Ding SL;Fong O;Garren E;Glandon A;Gouwens NW;Gray J;Graybuck LT;Hawrylycz MJ;Hirschstein D;Kroll M;Lathia K;Lee C;Levi B;McMillen D;Mok S;Pham T;Ren Q;Rimorin C;Shapovalova N;Sulc J;Sunkin SM;Tieu M;Torkelson A;Tung H;Ward K;Dee N;Smith KA;Tasic B;Zeng H
通讯作者：
Zeng H
影响因子：
16.6
作者：
Wang, Xuran;Park, Jihwan;Li, Mingyao
通讯作者：
Li, Mingyao