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• W2895236603 abstract "ABSTRACT 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." @default.
• W2895236603 created "2018-10-12" @default.
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• W2895236603 date "2018-10-08" @default.
• W2895236603 modified "2023-09-26" @default.
• W2895236603 title "MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions" @default.
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• W2895236603 doi "https://doi.org/10.1101/437665" @default.
• W2895236603 hasPublicationYear "2018" @default.
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