Welch Joshua D, Hartemink Alexander J, Prins Jan F
Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Curriculum in Bioinformatics and Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Genome Biol. 2017 Jul 24;18(1):138. doi: 10.1186/s13059-017-1269-0.
Single cell experimental techniques reveal transcriptomic and epigenetic heterogeneity among cells, but how these are related is unclear. We present MATCHER, an approach for integrating multiple types of single cell measurements. MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type. Using scM&T-seq and sc-GEM data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences. MATCHER also reveals new insights into the dynamic interplay between the transcriptome and epigenome in single embryonic stem cells and induced pluripotent stem cells.
单细胞实验技术揭示了细胞间的转录组和表观遗传异质性，但它们之间的关系尚不清楚。我们提出了MATCHER，一种整合多种类型单细胞测量的方法。MATCHER使用流形对齐从对同一类型的不同细胞进行的转录组和表观遗传测量中推断单细胞多组学图谱。使用scM&T-seq和sc-GEM数据，我们证实MATCHER无需使用已知的细胞对应关系就能准确预测DNA甲基化与基因表达之间真正的单细胞相关性。MATCHER还揭示了单细胞胚胎干细胞和诱导多能干细胞中转录组和表观基因组之间动态相互作用的新见解。
