Fig. 6: Single-cell sequencing analysis for assessing immune variation in cellular composition.
From: Genetic and environmental interactions contribute to immune variation in rewilded mice
a, UMAP visualization of scRNA-seq data identifying 23 major immune cell subsets, Block 1 and Block 2. b, Bar plots showing the pseudo R2 measure of effect size of predictor variables and interactions as calculated by MDMR analysis based on cellular composition of cells identified in a in each mouse (Supplementary Data 7) (Block 1, n = 51, 17 129S1, 21 C57BL/6, 13 PWK/PhJ; Block 2, n = 71, 19 129S1, 28 C57BL/6 and 24 PWK/PhJ mice). c, PCA of MLN cellular compositional data as determined by scRNA-seq analysis. d,e, Bar plots showing percentages of B follicular cells (d) and CD4 T cells (e) based on the scRNA-seq identified in a. For d and e, n = 122; 129S1 Lab Uninfected = 8, 129S1 Lab T. muris = 9, 129S1 RW Uninfected = 7, 129S1 RW T. muris = 12, C57BL/6 Lab Uninfected = 10, C57BL/6 Lab T. muris = 10, C57BL/6 RW Uninfected = 13, C57BL/6 RW T. muris = 16, PWK/PhJ Lab Uninfected = 8, PWK/PhJ Lab T. muris = 7, PWK/PhJ RW Uninfected = 10, PWK/PhJ RW T. muris = 10 over two experimental blocks. Statistical significance was determined based on MDMR analysis with R package for b; for d and e, one-way ANOVA test with comparison by Tukey’s multiple analysis was used to test statistical significance between the different groups of interest. Data are displayed as mean ± s.e.m. and for d and e bar plots dots represent individual mice. NS P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
