Supplementary Figure 7: Surface marker and transcription factor expression across cell state in thymic hematopoietic cells observed with single-cell RNA-seq.
From: Inferring population dynamics from single-cell RNA-sequencing time series data
(a-c) Surface marker expression across cell state. (a) Natural cubic spline fits to log expression data with cell state covariate (df=10). Shown are markers that are traditionally used to distinguish αβ-T-cell stages. Vertical lines indicate the right boundaries of cell stages in cell state. (b,c) Heatmap of all surface marker genes that are differentially expressed in cell state (log counts with limma (Nucleic Acid Res. 43, Ritchie, M. E. et al., 2015), natural cubic spline model with df = 4, q-value threshold of 1e-5) either in αβ-T-cell (b, n = 10079) or the non-conventional lymphocyte lineage (c, n = 1070). Genes are ordered separately for both lineages by the cell state at which their expression is maximal. Lists of all genes in these two heatmaps are supplied in Supplementary data 2.2.1 (αβ-T-cell lineage) and 2.2.2 (non-conventional lymphocyte lineage). The gene annotation used is supplied in Supplementary data 2.3. The underlying differential expression analysis results are supplied Supplementary data 2.4. (d,e) Transcription factor expression across cell state. (d) Natural cubic spline fits to log expression data with cell state covariate (df = 10). (e) Heatmap of all transcription factor genes that are differentially expressed in cell state (log counts with limma (Nucleic Acid Res. 43, Ritchie, M. E. et al., 2015), natural cubic spline model with df = 4, q-value threshold of 1e-5) in the αβ-T-cell lineage. Genes are ordered by peak time. A list of all genes in this heatmap is supplied in Supplementary data 2.1. The gene annotation used is supplied in Supplementary data 2.3. The underlying differential expression analysis results are supplied Supplementary data 2.4.
