TY - JOUR T1 - MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions JF - bioRxiv DO - 10.1101/437665 SP - 437665 AU - Yael Baran AU - Arnau Sebe-Pedros AU - Yaniv Lubling AU - Amir Giladi AU - Elad Chomsky AU - Zohar Meir AU - Michael Hoichman AU - Aviezer Lifshitz AU - Amos Tanay Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/08/437665.abstract N2 - Single cell RNA-seq (scRNA-seq) has become the method of choice for analyzing mRNA distributions in heterogeneous cell populations. scRNA-seq only partially samples the cells in a tissue and the RNA in each cell, resulting in sparse data that challenge analysis. We develop a methodology that addresses scRNA-seq’s sparsity through partitioning the data into metacells: disjoint, homogenous and highly compact groups of cells, each exhibiting only sampling variance. Metacells constitute local building blocks for clustering and quantitative analysis of gene expression, while not enforcing any global structure on the data, thereby maintaining statistical control and minimizing biases. We illustrate the MetaCell framework by re-analyzing cell type and transcriptional gradients in peripheral blood and whole organism scRNA-seq maps. Our algorithms are implemented in the new MetaCell R/C++ software package. ER -