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Fig. 3 | Genome Biology

Fig. 3

From: Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

Fig. 3

Quartz-Seq2 with high UMI conversion efficiency identified more differentially expressed genes and biological pathways. a UMI counts, gene counts, and UMI conversion efficiency for Quartz-Seq2 and Drop-seq experiments. These values depended on the initial fastq reads on average per cell. Error bars represent standard deviations. b We calculated overlapping differentially expressed genes between bulk RNA-seq data and single-cell RNA-seq data. We randomly picked up the indicated number of single cells and calculated differentially expressed genes 20 times. c, d We randomly selected 100 ES cells and 100 PrE cells for each method. c Venn diagram of genes that were differentially expressed between the ES cluster and the Dex-treated ES (PrE) cluster, as identified by Quartz-Seq2, Drop-seq, and bulk RNA-seq (left). The number of genes that differed in expression level between ES and PrE cells by at least twofold was determined (FDR < 0.05). We also present a Venn diagram of the Reactome pathway (right). d Venn diagram of Gene Ontology (GO) terms for genes with highly variable expression, as identified by Quartz-Seq2 and Drop-seq

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