Fig. 5: Gene Expression Analysis of CNS2-Dependent Feature Cells by Cross-Analysis of Grad-CAM Output from TockyConvNet and RNA-seq Data.
From: Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer
a Timer expression profile (Upper) and Timer Angle and Intensity profile (Lower) of pre-sort CD4+ T cells and fractionated Foxp3 Timer+ cells from WT Foxp3 Timer mice. b Grad-CAM heatmap using the TockyConvNet, trained as shown in Fig. 4, applied to RNA-seq flow cytometric data. (Upper) Visualisation of WT feature analysed via Attention-Conv2 Grad-CAM, highlighted by red on heatmap (Upper); (Lower) KO feature cells visualised through Conv2 Grad-CAM, highlighted with a blue on heatmap. Colour range is normalised per panel. c Bar charts showing the percentage of WT feature cells (i.e. CNS2-dependent cells, left) and KO feature cells (i.e. CNS2-independent cells, right) in pre-sort CD4+ T cells and fractionated Foxp3 Timer+ cells. Error bars indicate standard deviations. n  = 3 biological replicates. Expression dynamics of key genes in fractionated Foxp3 Timer+ cells: d transcription factors downstream of TCR signalling; e genes associated with IL-2 and TGF-β signalling, along with prototypic upstream and downstream Foxp3 genes. p-values were obtained by two-sided Wald tests of the R package DESeq2 and adjusted by the Benjamini & Hochberg method. Asterisks indicate statistical significance (adjusted p-value < 0.05) and shown for comparisons involving B2 only. Error bars indicate standard deviations. n  = 3 biological replicates. Exact p-values are provided in Supplementary Data 1.
