首页> 美国卫生研究院文献>Genome Biology >scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles
【2h】

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles

机译:scAI:并行分析单细胞转录组和表观基因组图谱的无监督方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Overview of scAI. scAI learns aggregated epigenomic profiles and low-dimensional representations from both transcriptomic and epigenomic data in an iterative manner. scAI uses parallel scRNA-seq and scATAC-seq/single cell DNA methylation data as inputs. Each row represents one gene or one locus, and each column represents one cell. In the first step, the epigenomic profile is aggregated based on a cell-cell similarity matrix that is randomly initiated. In the second step, transcriptomic and aggregated epigenomic data are simultaneously decomposed into a set of low-rank matrices. Entries in each factor (column) of the gene loading matrix (gene space), locus loading matrix (epigenomic space), and cell loading matrix (cell space) represent the contributions of genes, loci, and cells for the factor, respectively. In the third step, a cell-cell similarity matrix is computed based on the cell loading matrix. These three steps are repeated iteratively until the stop criterion is satisfied. scAI ranks genes and loci in each factor based on their loadings. For example, four genes and loci are labeled with the highest loadings in factor 3. Simultaneous visualization of cells, marker genes, marker loci, and factors in a 2D space by an integrative visualization method VscAI, which is constructed based on the four low-rank matrices learned by scAI. Small filled dots represent the individual cells, colored by true labels. Large red circles, black filled dots, and diamonds represent projected factors, marker genes, and marker loci, respectively. The regulatory relationships are inferred via correlation analysis and nonnegative least square regression modeling of the identified marker genes and loci. An arch represents a regulatory link between one locus and the transcription start site (TSS) of each marker gene. The arch colors indicate the Pearson correlation coefficients for gene expression and loci accessibility. The red stem represents the TSS region of the gene, and the black stem represents each locus
机译:scAI概述。 scAI可以从转录组和表观基因组数据中以迭代方式学习汇总的表观基因组概况和低维表示。 scAI使用并行的scRNA-seq和scATAC-seq /单细胞DNA甲基化数据作为输入。每行代表一个基因或一个基因座,每列代表一个细胞。第一步,根据随机发起的细胞-细胞相似度矩阵汇总表观基因组概况。第二步,将转录组数据和汇总的表观基因组数据同时分解为一组低秩矩阵。基因加载矩阵(基因空间),基因座加载矩阵(表基因组空间)和细胞加载矩阵(细胞空间)的每个因子(列)中的条目分别代表了基因,基因座和细胞对该因子的贡献。在第三步骤中,基于小区负载矩阵来计算小区-小区相似度矩阵。反复重复这三个步骤,直到满足停止条件为止。 scAI根据负载量对每个因子中的基因和基因座进行排名。例如,四个基因和基因座被标记为因子3的最高载量。通过基于四个可视化方法构建的整合可视化方法VscAI同时可视化二维空间中的细胞,标记基因,标记基因座和因子。排序由scAI学习的矩阵。小填充点代表单个单元格,并用真标签标记。大红色圆圈,黑色实心圆点和菱形分别代表投影因子,标记基因和标记基因座。通过相关分析和识别出的标记基因和基因座的非负最小二乘回归模型可以推断出调控关系。弧形代表一个基因座与每个标记基因的转录起始位点(TSS)之间的调控连接。拱形颜色表示基因表达和基因座可及性的Pearson相关系数。红色茎代表该基因的TSS区域,黑色茎代表每个基因座

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号