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Fig. 2 | Genome Biology

Fig. 2

From: Single-cell transcriptomics unveils gene regulatory network plasticity

Fig. 2

A metric tailored to single-cell data allows detection of hidden correlations. a Distribution of Pearson correlations ρp in normalized expression data (7697 microglia cells) or in the Z-score space. We detect only 24 correlations |ρp| > 0.8 in the first scenario, but almost one million |ρp| > 0.8 in the Z-score space. b Examples of correlations using either expression values or Z-score-transformed data (ρp Pearson, ρc Cosine, ρs Spearman). Due to drop-out events and other artifacts, the positive correlation between Mmp25 and Ankrd22 is only exposed using Z-scores. Similarly for the negative correlation between Samd9l and Cx3cr1. c Comparison of detected correlations |ρp| > 0.8 using either original expression values or Z-score-transformed data across different scRNA-seq technologies, sequencing depths (from 625 [12] to 6480 [13] average detected genes per cell), and source material. d An adaptive correlation cutoff and GO annotations are used to infer the regulatory networks from correlation data (see the “Methods” section)

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