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Fig. 3 | Genome Biology

Fig. 3

From: VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells

Fig. 3

Joint analysis of chromatin accessibility, gene expression, and gene expression noise reveals gene modules with distinct modes of regulation. a Two sets of genes were analyzed based on the correlations in Additional file 1: Figure S3a. Class A genes (left side) have positive expression – gene activity and noise – gene activity correlations. Class B genes have positive expression – gene activity correlation but negative noise – gene activity correlation. b Patterns of expression (top), gene activity (middle) and noise (down) of genes belonging to class A. For convenience, a subset of ~ 300 genes is shown. c As b, but showing genes of class B. All genes in this category were included. d Diagram summarizing the observed patterns in chromatin accessibility, expression and noise for the set of genes in class A and class B. See main text for further details. e Genomic region of CD28 (class A gene). Upper panel: normalized accessibility signal, aggregated across cells from selected clusters. Violin plots (top right) show expression and noise levels across each cluster. Differential accessibility test of T cells against the remaining dataset was performed. Peaks (middle panel) were annotated based on increased accessibility (“Open”), no change (“NA”), or decreased accessibility (“Closed”). Threshold values: log2 fold change (log2FC) > 1.25, adjusted P value (padj) < 0.001. Gene linkages [26] between expression and accessibility within individual peaks (links Ex-Pk) or noise and peak accessibility (links N-Pk) are shown in the lower panel, with scores corresponding to Pearson correlation coefficients. These links bind the TSS of the corresponding gene and peaks where a significant correlation was detected, and they do not represent spatial chromatin organization. f As e, but showing data of AKAP13 (class B gene). Differential accessibility test was performed by comparing monocytes against the remaining dataset

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